an open source approach to social media data gathering
TRANSCRIPT
Air Force Institute of TechnologyAFIT Scholar
Theses and Dissertations Student Graduate Works
3-22-2018
An Open Source Approach to Social Media DataGatheringAnthony J. Kallhoff
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Recommended CitationKallhoff, Anthony J., "An Open Source Approach to Social Media Data Gathering" (2018). Theses and Dissertations. 1935.https://scholar.afit.edu/etd/1935
AN OPEN SOURCE APPROACH TO SOCIAL MEDIA DATA GATHERING
THESIS
Anthony J. Kallhoff, 2nd Lieutenant, USAF
AFIT-ENS-MS-18-M-130
DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY
AIR FORCE INSTITUTE OF TECHNOLOGY
Wright-Patterson Air Force Base, Ohio
DISTRIBUTION STATEMENT A.
APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED.
The views expressed in this thesis are those of the author and do not reflect the official
policy or position of the United States Air Force, Department of Defense, or the United
States Government. This material is declared a work of the U.S. Government and is not
subject to copyright protection in the United States.
AFIT-ENS-MS-18-M-130
AN OPEN SOURCE APPROACH TO SOCIAL MEDIA DATA GATHERING
THESIS
Presented to the Faculty
Department of Operational Sciences
Graduate School of Engineering and Management
Air Force Institute of Technology
Air University
Air Education and Training Command
In Partial Fulfillment of the Requirements for the
Degree of Master of Science in Operations Research
Anthony J. Kallhoff, BS
2nd Lieutenant, USAF
March 2018
DISTRIBUTION STATEMENT A.
APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED.
AFIT-ENS-MS-18-M-130
AN OPEN SOURCE APPROACH TO SOCIAL MEDIA DATA GATHERING
Anthony J. Kallhoff, BS
2nd Lieutenant, USAF
Committee Membership:
Dr. Bradley Boehmke
Chair
Maj. Jason Freels
Reader
iv
AFIT-ENS-MS-18-M-130
Abstract
Modern usage of social media affords the military intelligence and analytic
communities novel approaches to gather information. However, the tools and resources to
develop these methodologies are still maturing. Furthermore, current data acquisition
tools are not available to the DoD for all social media platforms. This thesis addresses a
small subset of this problem by developing an open source methodological approach to
collect and manage data from a popular social media site that has previously been
inaccessible to defense intelligence organizations. This approach was operationalized via
the R package called instaExtract, and an exemplar analysis was performed to
demonstrate its application and efficiency for intelligence gathering.
v
Acknowledgments
I would like to express my sincere appreciation to my faculty advisor, Dr. Bradley
Boehmke, for his guidance and support throughout the course of this thesis effort. The
insight and experience was certainly appreciated. I would also like to thank my sponsor
for both the support and latitude provided to me in this endeavor.
Anthony J. Kallhoff
vi
Table of Contents
Page
Abstract .............................................................................................................................. iv
Acknowledgments................................................................................................................v
Table of Contents ............................................................................................................... vi
List of Figures .................................................................................................................. viii
List of Tables ..................................................................................................................... ix
I. Introduction .....................................................................................................................1
1.1 General Issue ..........................................................................................................1
1.2 Research Goals .......................................................................................................2
1.3 Research Contributions ..........................................................................................2
1.4 Assumptions and Limitations .................................................................................3
1.5 Organization ...........................................................................................................3
II. Literature Review ............................................................................................................4
2.1 Overview ................................................................................................................4
2.2 The Evolving State of Intelligence Gathering ........................................................4
2.3 Instagram ................................................................................................................6
2.4 Data Collection and Management ........................................................................10
2.5 Existing Instagram Data Extraction Software ......................................................14
2.6 Reproducible Analysis..........................................................................................18
III. Methodology ...............................................................................................................19
3.1 Overview ..............................................................................................................19
3.2 A JSON-Based Instagram Scrapper in R– instaExtract ................................19
3.3 Adaptable, Reproducible, Distributable – Hosting on the AFIT Data Science
Lab’s GitHub Page .....................................................................................................29
vii
3.4 Summary...............................................................................................................37
IV. Exemplar Analysis .......................................................................................................38
4.1 Overview ..............................................................................................................38
4.2 Gathering Information on the Region...................................................................38
4.3 Hashtag Investigation ...........................................................................................39
4.4 User Investigation .................................................................................................44
4.5 Geo Mapping ........................................................................................................49
4.6 Summary...............................................................................................................51
V. Conclusions and Recommendations ............................................................................52
5.1 Overview ..............................................................................................................52
5.2 The instaExtract Package ..............................................................................52
5.3 The AFIT Data Science Lab .................................................................................54
5.4 Summary...............................................................................................................55
Appendix A ........................................................................................................................56
Appendix B ........................................................................................................................57
Appendix C ........................................................................................................................58
Appendix D ........................................................................................................................59
Appendix E ........................................................................................................................60
Appendix F.........................................................................................................................61
Bibliography ......................................................................................................................62
viii
List of Figures
Page
Figure 1. An Instagram Post ............................................................................................... 8
Figure 2. An Instagram Account ......................................................................................... 9
Figure 3. An Instagram Location ...................................................................................... 10
Figure 4. The Analytic Cycle (Wickham & Grolemund, 2016) ....................................... 11
Figure 5. Word Cloud for Top 200 Hashtags in the DC Area .......................................... 41
Figure 6. Count of 150 Most Used Hashtags in the DC Area........................................... 42
Figure 7. Most liked Instagram Post by usairforce (usairforce, 2018) ............................. 45
Figure 8. Post Likes Over Time for usairforce Account ................................................... 46
Figure 9. Network of Commenting Users on usairforce Posts Where Edge is Proportional
to Number of Comments ............................................................................................ 48
Figure 10. All Washington DC Locations from Instagram Overlaid with Actual Location
.................................................................................................................................... 49
Figure 11. Clustered Locations for Washington DC ........................................................ 50
Figure 12. Zoomed in View of White House Instagram Locations .................................. 51
ix
List of Tables
Page
Table 1 - Instagram Data Extraction Software ................................................................. 15
Table 2 - instaR Functions ................................................................................................ 16
Table 3 - Instagram-php-scraper Functions ...................................................................... 17
Table 4 - Search Functions Provided by the instaExtract Package ........................... 22
Table 5 - Get Functions for the instaExtract Package ............................................... 23
Table 6 - Format of the Results for the Get Functions in the instaExtract Package .. 24
Table 7 - Functions Enabling Location Mapping Capabilities ......................................... 33
1
AN OPEN SOURCE APPROACH TO SOCIAL MEDIA DATA GATHERING
I. Introduction
1.1 General Issue
The modern world is producing mountains of information that can be of
indispensable benefit to the Department of Defense (DoD) intelligence community.
Traditionally, intelligence has been gathered through arduous means and required
advanced systems or highly trained professionals. However, the emergence of social
media, and the breadth of new information it provides, offer analysts a unique
opportunity to access large amounts of data at little cost. While the techniques to make
the most of this data are still in development, they present an exciting supplement to
traditional intelligence gathering.
In academic and applied research, there is an emphasis on advancing and applying
rigorous analytical techniques to data, but also important are the methods to acquire the
data. Just as higher resolution microscopes provide new insights in biology, tools that can
offer new and refined streams of information can help to better inform analysts. Without
attention being given to the acquisition of data, analysts are forced to apply their
techniques to flawed or constrained data sets. Current data gathering tools have varying
limitation in their scope, functionality, and platform, restricting the benefits they may
offer.
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1.2 Research Goals
The current suite of in-house data acquisition tools does not span all the various
social media platforms. One notable site that is presently excluded is Instagram, a photo
and video sharing platform. While there exist third-party programs that offer some form
of information gathering from Instagram, they are limited in varying ways. This research
aims to establish a reliable and reproducible approach to extract Instagram data for use by
the DoD intelligence community. To this end, this research seeks to create an R-based
Instagram data acquisition package with the following functionality:
• Information retrieval without the need for user authorization or API integration
• Search functions for identifying users and hashtags in Instagram’s databases
• Media post retrieval that includes meta-data and information on community
involvement
• Retrieval of information for users and their activity on the site
• Raw data stream cleaning and transformation resulting in user friendly data
• Example analytic functions to serve as a basis for future development
• Hosting on a platform that facilitates community involvement, continued support,
and version control
1.3 Research Contributions
This research aims to expand the DoD intelligence community’s information
gathering capabilities with a consistent and robust approach to pulling Instagram data.
This approach will be in the form of an R package hosted on the Air Force Institute of
Technology (AFIT) Data Science Lab’s GitHub account, consistent with the approach of
recent analytic tools developed by AFIT. More than a one-off deliverable, the results of
3
this work will establish a groundwork for more Instagram based intelligence gathering
and analysis. By focusing on the particulars of data acquisition, this research supports the
efforts of data analytics by providing the material to investigate. This package will also
have an immediate impact on the DoD, who will have access to the information and
insights Instagram provides.
1.4 Assumptions and Limitations
In development of these resources, some level of continued success by Instagram
and the usefulness of its information is assumed. The tools developed also rely on a
general structure to how Instagram presents its data and allows users to interact with that
data. Significant reworkings to Instagram’s procedures might invalidate some functions
in this research.
1.5 Organization
The paper begins with a literature review in Chapter 2 that explores intelligence
gathering and its evolution with social media. Furthermore, Chapter 2 looks at the current
resources for collection social media data, diving specifically into Instagram, its layout
and tools that interact with it. Finally, Chapter 2 addresses the topic of reproducibility in
analysis. Chapter 3 discusses the methodological framework for extracting Instagram
social media data and the open source R package, instaExtract, that operationalizes
this approach. Next, Chapter 4 presents an exemplar analysis, showing how an analysis
might utilize instaExtract. Finally, Chapter 5 summarizes the work accomplished
with this thesis and discusses areas for future research.
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II. Literature Review
2.1 Overview
The objective of this thesis is to develop and operationalize a methodological
approach to collect and manage data from a popular social media site as well as provide
an R package that allows for reproducible analysis by users. To this end, this paper
begins with a literature review broken into six sections, including the overview. The
second section will be a look at the more traditional fields of intelligence gathering and
how computers are being integrated with older methods. The third section will look
closer at the specific social media platform explored in this thesis, Instagram, and the
structure of its information. The fourth section covers practices for collecting and
managing large amounts of user data pulled from a social media site using its application
program interface (API). Fifth, the review examines existing data extraction software and
their advantages and limitations. The final section explores techniques and considerations
to ensure reproducible analysis.
2.2 The Evolving State of Intelligence Gathering
The gathering of intelligence has developed throughout the history of warfare.
Knowledge of the enemy and of the landscape of the battle can be vital to the success of
the combatants. Accordingly, the U.S. has developed many institutions and practices for
the purpose of acquiring and deciphering intelligence (Richelson, 2015). Many of the
techniques relied on, however, are fallible and inconsistent. Combing through traditional
paperwork can take many man hours, and even experienced eyes can miss important
details. Many human intelligence procedures also depend on investigators trusting their
memories or interpreting ambiguous signs (Loftus, 2011). With the advent of computers,
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the nature of intelligence gathering is shifting rapidly. Digitized files can be searched in
milliseconds, and while computers carry their own sets of faults, they do not tire or relent.
More than just work horses though, computers offer unique opportunities for data
acquisition and analysis (Leese, 2015). With the internet came the introduction and
spread of social media sites like Facebook and Instagram. These sites are populated with
information from users all over the world and represent almost all demographics. There is
such a wealth of information that has sprung up so recently, that the intelligence
community is struggling to adapt fast enough to utilize all the data now at its disposal
(Loftus, 2011). Along with a flood of new information, computers are powerful
processers of this information. As an example, consider the analysis of terrorist
networks. Developing an organizational structure of terrorist’s cells has been a backbone
of human intelligence in the fight against terror. Isolating important targets and
determining the flow of information has allowed us to neutralize cells with minimal
damage (Ronczkowski, 2011). However, these models are currently still heavily reliant
on the same unreliable techniques mentioned early. Computers and the new sources of
information they provide could afford the intelligence communities new manners of
network analysis and formulation. Specifically, social media can provide information
unlike any other source. Social media data provides relational and geospatial data at a
real time pace, closing the turnaround of reports for analysts (Pitic, Volovici, Tara, &
Mite, 2013).
The structure of social media sites and the way users interact with them provide
new types of information to be used in analysis. One of the primary and most useful of
these types comes in the form of social networks, a network of actor nodes and
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relationship or interaction edges that bind them together (Aggarwal, 2011). While social
networks have been studied in conventional scenarios for some time, social media data
presents itself in a manner so conducive to social network analysis that it has created a
renewed fervor in the scientific study of the field (Aggarwal, 2011). These online social
networks tend to reach large scales which manifest with special structures and behaviors
(Milgram, 1967). Online networks in particular are saturated with data, offering unique
circumstances for analysis of two primary kinds of data: structural analysis which
identifies important nodes, links and regions, and content-based analysis which delves
into the specifics of the content being shared (Aggarwal, 2011). Finally, online networks
evolve in much shorter time spans than conventional networks, a complication that is the
subject of recent research (Aggarwal & Yu, 2005).
The previous paragraph mentions content-based analysis briefly, but the scope of
analysis to be done on social media data is extensive. Almost all social media platforms
are rich in text based information which lends itself to interesting fields of analysis like
classification, clustering, and sentiment analysis (Aggarwal, 2011; Ashraf, Verma, &
Tech, 2016). Often, social media data will also contain image and video content, enabling
methods involving image and voice recognition (Li, Zha, Huet, & Tian, 2016). One other
category of information, geotagging, is a descriptor of content, but can provide for
meaningful analysis in its own right. Location information can help to contextualize
online content in the real world (Andriopoulou & Lymberopoulos, 2012).
2.3 Instagram
Instagram launched on 6 October 2010 with 25,000 sign-ups on the first day
(“Our Story – Instagram,” n.d.). Two years later, on 9 April 2012, the company, now at
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30 million users, was purchased by Facebook for nearly $1 billion (Chaykowski, 2016).
Today, it boasts 500 million daily users, and more than 800 million monthly active users
(“Our Story – Instagram,” n.d.). Instagram is, at its core, just a photo-sharing app, but
with more users than Twitter, Snapchat, and Pinterest combined, it has placed itself as
one of the fastest growing social media platforms of all time (Chaykowski, 2016).
The typical Instagram experience revolves around two types of content: a post and
a story. A post, seen in Figure 4, is the term for a user submission encompassing their
submitted media, description, and community response. A post can either be a video or a
photo. It is accompanied with a user created caption and hashtags, markers that promote
and categorize a post. Posts can also have comments and likes left by other users. Once a
post is created, it will exist on a user’s account indefinitely unless it is deleted. A story is
similar to a post, but it is only up for a short time and comments and likes are not
displayed. Instead, stories act as a slideshow that move through videos and photos posted
by the accounts a user follows. Others can comment on a story, but the comment and who
viewed a story is only visible to the creator. Currently, Instagram only supports mobile
users viewing the story of accounts that they do not follow.
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Figure 1. An Instagram Post: 1) Media – can be a video or photo, 2) Owner Thumbnail,
3) Owner Username, 4) Post Caption, 5) Post Tags, 6) Commenter Username, 7)
Comment, 8) Post Likes, 9) Post Date
A user’s history of posts is stored as an account, shown in Figure 2. An account
page shows the user’s thumbnail and username. It also has statistics on how many posts
the account has made, how many other users follow the account, and how many others
the user follows. To follow an account means to have the posts of that account appear in
the stream of posts provided to the user, called their feed. If someone is signed into
Instagram, it also possible to view which accounts are followers and which accounts the
given account is following. Next, accounts have a bio that may describe their content or
themselves. Finally, on the account page are listed the posts of the user from most recent
to oldest.
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Figure 2. An Instagram Account
Similar to an account page, Instagram has pages for locations. Locations are
created by users, and subsequently used by others to classify future posts. An example
location is shown in Figure 3. The page shows the coordinates of location, a story that
users can add content to, the current top nine posts, and the list of all other content in
reverse chronological order. Since locations are user created, there is not an extensive list
of all locations, but it is possible to view the top 1000 locations for a city by navigating
the options of the “explore/locations” subdomain.
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Figure 3. An Instagram Location
2.4 Data Collection and Management
Data analysis can be thought of as a continuing process rather than a one-step
application. This analytic cycle is depicted in Figure 4. Although much of the work in the
process is done in the sub-section labeled “understand”, it is also important to not neglect
the first two steps, “import” and “tidy.” Importing entails pulling information from a
source, whether it is already organized or through more intensive processes like web
scraping. Tidying information is the process of formatting data into a usable and
consistent structure (Wickham & Grolemund, 2016). These two steps are essential to the
analytic process since without them, the analyst has no data to work with or the data
exists in a form that cannot be analyzed. Therefore, in the pursuit of analyzing social
media information, attention should be given to the importing and tidying of the data, as
is the emphasis of this thesis.
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Figure 4. The Analytic Cycle (Wickham & Grolemund, 2016)
The first step to acquiring information from a social media site is determining an
avenue to the information. One source of information is to scrape the desired fields
directly from the displayed page, commonly referred to as web-scraping. Such a program
would traverse the actual webpage that is displayed when a person uses the site. To move
between pages, it would use built in links or travel along links in the page to search for
requested information. This web-scraping approach relies on the underlying structure of
HTML-based sites to process what was made for human interaction (Glez-Peña,
Lourenço, López-Fernández, Reboiro-Jato, & Fdez-Riverola, 2013). This approach offers
complications to a program since it does not adapt well to changes to the site and requires
specific implementation for each portion of a site (Glez-Peña et al., 2013). Most websites
also offer an application programming interface (API) which allows outside organizations
access to the information stored in the sites databases. APIs offer a more consistent
means of retrieving data in consistent formats, but it is not guaranteed that they will offer
all information that the site stores (Glez-Peña et al., 2013). Additionally, as is the case
with Instagram, APIs may be subject to strict regulations that prevent the public from
accessing them. The alternative to this is an open API, which allows third-party
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developers access without needing authorization, but it can still suffer the other problems
of APIs (Jang & Lee, 2009). With both APIs and web scraping, sites can be behind an
authentication wall that will not allow some or all of their information to be displayed
unless the user is logged into an account.
Once a method of access to the website is determined, the information is still in
need of storage and management. The information collected is still in a raw state and as a
stream is difficult to work with (Injadat, Salo, & Nassif, 2016). When web scraping, the
data can be stored in almost any format, depending on how the web scraping software
pulls its information. APIs will return a data structure based on established guidelines,
one of the benefits of interacting through an API (Glez-Peña et al., 2013). In the case of
Instagram’s open API, data is return in JavaScript Object Notation (JSON). JSON files
are fundamentally text files, but they are organized in a way to make them both readable
for humans and easy for machines to parse (“JSON,” n.d.). A complete list of data
formats would be infeasible, but the basic principle is that data will come in a certain
format depending on the chosen importing method.
Whatever the method, data will be required to conform to certain requirements in
order to be compatible with the needs of tools and data storage later in the process. This
conversion of data falls under the category of “tidying” the data, or ensuring that the data
is in format that best facilitates future work(Wickham & Grolemund, 2016). Toward this
goal, the programming language R has resources that facilitate the “tidying” process. R is
an open-source programming language with a focus on statistical computing and
extensibility (“R: What is R?,” n.d.). R is extended via packages, fundamental units of
reproducible code (Hadley Wickham, 2015). One such package, tidyr, specializes in
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tidying data (Hadley & Henry, 2018). This package and more like it make R a great
programming language for manipulating data of many different formats and preparing it
for use in analysis.
A question not yet answered is what information is important to pull? It is
impractical to have access to all the data flowing through social media sites without the
cooperation of the company behind them, especially for sites as large as Instagram. A
decision must be made then to choose what data to target and how to find the data
relating to that target by second order connections. While the ultimate use of information
and methods of analyzing are outside the scope of this thesis, exploring potential use
cases allows for the creation of a program that more accurately reflects the needs of
future users.
One strategy for analysis is a user focused approach. By viewing the post rate and
other statistics like following or resubmissions, analysts can put together a map of
influential users and their reach (Erlandsson, Bródka, Borg, & Johnson, 2016). Further,
they can track the flow of information and events to construct communities and networks,
similar to those created now with more conventional gathering techniques (Lv & Guo,
2016). Networks can also be established to connect users. Algorithms that watch the
behavior of users can flag behaviors and interests that share a high correlation with target
user bases, providing new insight into potential persons and groups of interest (Mezghani,
Péninou, Zayani, Amous, & Sèdes, 2017). This is an example of a unique application of
computers, where it may discover underlying connections that even an experienced
investigator might miss.
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Another angle for analysis is to focus on events rather than networks. Social
media platforms have already shown the potential to be important barometers in public
perception of events and topics. By following trends, hashtags, and locations of posts,
analysists can create models that capture the sentiment and movement of a target
audience (Bian et al., 2016). This leads again to an area where computer driven analytics
can greatly help in information gathering. In a world of social media and
interconnectedness, discussion of events can evolve faster than a system administrator
can have time to adapt to. So it becomes important for the information gathering
software to have ways of dynamically associating common terms and pulling from
sources that a conventional search might overlook (Chan, Vasardani, & Winter, 2014).
2.5 Existing Instagram Data Extraction Software
Tools for extracting information from Instagram currently exist but are limited in
their usefulness. An overview of some available solutions is presented in Table 1. This
review will look more closely at instaR and Instagram PHP Scraper, but looking
at Table 1 shows that the various programs approach the same problem in many ways.
There are free programs like Instagram-scraper by Richard Arcega (Arcega, 2018)
and monetized software like the Instagram scraper provided by Im Rista (“Instagram
Scraper - Im Risto - Internet Marketing Blog,” n.d.). Many of the programs are based on
open source languages like Python and PHP, but only instaR notably uses R. The
monetized programs do not make their source code available, and as such, would not
easily be extendable. The existing software serves as a framework to inform
functionality, but ultimately each program fails to capture every important feature. The
monetized software has restrictive licensing issues and closed code. Programs in PHP and
15
Python do not capitalize on the existing analytic tools employed by the DoD community.
And lastly, instaR relies on interactions with Instagram’s private API, and the
authorization required to use that do not fit with mission needs.
Table 1 - Instagram Data Extraction Software: O – Optional, G – GitHub, ? –
Unavailable Information
insta
gram
-scra
per
Insta
gram
Super Scra
per
Insta
gram
PHP Scraper
Im R
isto
Insta
Scraper
insta
R
R ✓
Python ✓
PHP ✓ ✓
Custom ✓ ✓
API ✓ ? ✓
Scrapping ✓ ?
JSON ✓ ✓ ✓ ?
Authentication O O O ✓ ?
Authorization ✓
Availability G G G $49 $8 G
GUI ✓ ✓ ✓
These programs, however, can still be useful as templates for this research. First,
this thesis explores the instaR package by Pablo Barberá (Barberá, 2017). The program
is an R-based package that allows the user to interact with Instagram’s API to perform a
range of functions that are detailed in Table 2. The functionality of instaR is not as
expansive as other programs, but it highlights a few important considerations for
Instagram scrapers. Besides being outside of DoD control, instaR would have been a
good choice for Instagram data extraction based in R if it didn’t require authorization.
16
Since November of 2015, all API endpoints require a valid access_token, which requires
the application to be reviewed and approved by Instagram themselves (“Platform
Changelog • Instagram Developer Documentation,” n.d.). This is already a difficult
process for civilian users, but is a severe deterrent for the use of the API in a DoD
capacity. However, working with the API has two advantages Instagram-php-scraper, the
program investigated next, does not have. First, working with the API generally ensures a
higher level of stability with how a function will behave. Secondly, the API has some
functions that are hard to replicate through other means, namely, the ability to search for
posts in a given region.
Table 2 - instaR Functions
Function Purpose
getComments Retrieves up to 150 recent comments for a given post
getFollowers Retrieves the list of users that follow a given user
getFollows Retrieves the list of users a given user follows
getLikes Retrieves the list of users who liked a post
getLocation Retrieves location information
getPopular Retrieves up to 24 popular posts
getTagCount Retrieves a count of times a hashtag has been used
getUser Retrieves public information about a user
getUserMedia Retrieves public media from a given user and can
download media
instaOAuth Creates an OAuth access token enabling R to
communicate with Instagram’s API
searchInstagram Search media for mention of hashtag or for posts in a
given location
The other program investigated in detail is Instagram-php-scraper by
Postaddict.me (Postaddict.me, 2018), whose functions are detailed in Table 3. Unlike
17
Table 3 - Instagram-php-scraper Functions
Function Purpose
withCredentials Allows the program to run with account information
searchTagsByTagName Returns the hashtag results in a search for a given tag
getErrorBody An error handling function
searchAccountsByUsername Returns the accounts results in a search for a given
username
generateHeaders Adds credentials to a query
getMedias Retrieves the n most recent posts for a given username
getMediaById Retrieves information about a post with a given ID
getMediaByUrl Retrieves information about a post with a given URL
getMediaByCode Retrieves information about a post with a given
shortcode
getPaginateMedias Returns the first page of recent posts for a given
username
getMediaCommentsById Retrieves comments on a post with a given ID
getMediaCommentsByCode Retrieves comments on a post with a given shortcode
getMediaLikesByCode Retrieves likes on a post with a given shortcode
getAccountById Retrieves information on an account with a given ID
getAccount Retrieves information on an account with a given
username
getMediasByTag Retrieves n most recent posts with a given hashtag
getPaginateMediasByTag Returns the first page of recent posts with a given
hashtag
getCurrentTopMediasByTagName Retrieves the top nine posts with a given hashtag
getCurrentTopMediasByLocationId Retrieves the top nine posts for a given location ID
getMediasByLocationId Retrieves the n most recent posts for a given location ID
getLocationById Retrieves information for a given location ID
getFollowers Retrieves the accounts following a given account ID
getFollowing Retrieves the accounts a given account ID is following
getStories Retrieves the story information for a given account
setProxy Establishes a proxy for queries to be passed through
instaR, Instagram-php-scraper accesses Instagram’s open API. This allows users
to retrieve most public information without needing authorization from Instagram or even
an account. However, it does allow for users to log-in to access information limited to
authenticated accounts. The functionality of this program is also much more extensive
than others. Note, however, that while it has functions that interact with specific
locations, it cannot find posts in a given region. The two main factors that make
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Instagram-php-scraper an imperfect candidate is the language it is coded in, PHP,
and that it is controlled by a source outside the DoD. It does, however, serve as an
excellent blueprint for other programs interacting with Instagram’s open API.
2.6 Reproducible Analysis
The last subject that this thesis aims to address is the reproducibility of analysis.
To the ends of validation and verification, is has been standard for independent bodies to
try and replicate the results of a study (R. D. Peng, 2011). However, with more research
being done with the aid of data and independently collected information, all sciences are
suffering a crisis of reproducibility (R. Peng, 2015). Pulling information from a website
can result in drastically different outputs if the procedures for searches and search criteria
are not specified. Even then, the information might have changed since most websites
allow edits and deletions, as well as new data always being entered. The concept of
ensuring that the data and procedures for a study are made available to others is referred
to as “reproducible research” (R. D. Peng, 2009). For the context of intelligence agencies,
this is valuable as an extra step of validation. One of the largest benefits of using open
source software is that it is highly reliable and reproducible (Ven & Verelst, 2006). When
future users attempt to reproduce the results of previous work, open source programming
languages like R allow them to ensure the same work is being done. In a practical sense,
this means that a user knows what to expect when they run a function. Unlike in closed
source software, a user could even look at the source code to verify that the same
operation is being carried out. In scholarly and scientifically rigorous applications,
developers should design software to facilitate reproducibility.
19
III. Methodology
3.1 Overview
The goals of this research are achieved by creating an R package called
instaExtract hosted on the GitHub page for the Data Science Lab headquartered in the
Air Force Institute of Technology. The package uses publicly available links from
Instagram to retrieve information on users, posts, and locations. Using R as the backbone
for this package allows analysts to easily handle the data returned as well as create
reproducible reports. Being hosted on the Data Science Lab’s GitHub page allows for the
package to be both readily accessible and routinely updated. As a demonstration of the
future of such a package this thesis includes an example function,
createLocationMapping(), to demonstrate what a future user might contribute to the
project.
3.2 A JSON-Based Instagram Scrapper in R– instaExtract
The limitations of other accessible Instagram scraping software have already been
discussed. In addition to being outside the military community’s reach of control, these
other programs suffer from challenges that make them unsuitable for use by intelligence
gatherers. Namely, they are limited by interacting with the restrictive and exclusive
Instagram API or they are difficult to operate and distribute.
This research overcomes these challenges with the creation of an R package that
is based on interactions with publicly available Instagram links that return JSON files.
The primary advantage of acquiring data this way is that most information on the site can
be obtained without the need to log-in, nor interact at all with the closed API. This would
allow a user behind a proxy to access almost all information on Instagram without
20
needing to provide any of their own information. Furthermore, the use of JSON links is
far less demanding on a user’s bandwidth than loading every page in a traditional
browser. A JSON link will not load images or videos which greatly reduces the time
demand of queries. Instead, all the information for a page is reduced to the data defining
it. For instance, an image would be represented by a unique identification number and
Uniform Resource Locator (URL). If, in the future, a user wanted to download an image,
they could use this information to download it.
3.2.1 Helper Functions
There is a collection of generic links that the package uses to create these JSON
links. To access the information a user wants, the package will take a user input and
substitute it into the generic link for each category. For more complex interactions, the
package will use information contained in the JSON file to create a new link, simulating
pagination through all the possible results, or until the desired number of results is
returned. These links and their manipulators are located in the Endpoints.R file. In the
example below, an example user query is used to create a working link to Instagram’s
open API.
User_Media_Json_Link <- "https://www.instagram.com/{username}/?__a=1&max_id={maxID}"
getUserMediaJsonLink <- function(user, maxID){ link <- User_Media_Json_Link link <- gsub("{username}", user, link, fixed = TRUE) link <- gsub("{maxID}", maxID, link, fixed = TRUE) return(link) }
21
To work with Instagram’s identification numbers, the package utilizes transformer
functions in Transformers.R. Instagram uses both IDs and shortcodes to identify their
content. IDs are most commonly returned in the JSON file, but shortcodes are the
identifiers used when creating the links. To go from shortcode to ID, for each character in
the code, the program multiplies a placeholder ID by 64 and adds that number to the
index of the given character in an alphanumeric alphabet. To go from ID to shortcode,
process is reversed. However, since Instagram’s IDs are larger than the integer size that
R works with, the package utilizes the bit64 package to accurately represent the integer
when doing math. The functions used to perform these operations are shown below.
getIDFromCode <- function(code){
alphabet <-
"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_" ID <- 0
for(i in 1:nchar(code)){
c <- substr(code,i,i)
id <- id * 64+ regexpr(c, alphabet)[1]
}
return(ID) }
getCodeFromID <- function(ID){
alphabet <- "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_"
code <- ""
ID <- bit64::as.integer64.character(ID)
while(ID >0){
remainder <- ID %% bit64::as.integer64.double(64)
ID <- (ID - remainder) %/% 64 code <- paste(substr(alphabet,remainder+1,remainder+1),code,sep="")
}
return(code)
}
22
3.2.2 Search Functions
With endpoints and transformers established, the package can utilize a large
assortment of functions to pull specific information or search for a user provided query.
The smaller subset of functions are the search functions, which return tags or account
names similar to the query given by the user. Table 4 shows these two functions and
their purposes.
Table 4 - Search Functions Provided by the instaExtract Package
Function Purpose
searchAccountsByUsername Will return a data frame of accounts and their
information whose username contains the users
query
searchTagsByTag Will return a data frame of tags in use that contain
the users query
3.2.3 Get Functions
The majority of instaExtract’s functionality comes in the form of its Get
functions, which retrieve information based on what the user supplies. Table 5 lists these
functions as well as their purposes. All these functions operate without a user needing to
log in. When using a function, the user will typically provide two arguments, the query
they are interested in and the number of results they wish to return. In cases where the
number is not provided, the package will only return the first page of information. The
package will then return a data frame containing information relevant to the given
element in question. These results are detailed in Table 6.
23
Table 5 - Get Functions for the instaExtract Package
Function Purpose
getCommentsByMediaCode Return the first n comments and comment
information for a media with a given
shortcode
getCommentsByMediaID Calls getCommentsByMediaCode after
transforming a media ID into a shortcode
getCurrentTopMediaByLocationID Returns the top nine media for a location
with the given location ID
getCurrentTopMediaByTag Returns the top nine media for a given
hashtag
getLikesByMediaCode Returns information on the first n likes on a
media post with the given shortcode
getLikesByMediaID Returns information on the first n likes on a
media post with the given ID
getLocationByID Returns information on a location for a
given location ID
getMediaByCode Returns information on a media post for a
given media shortcode
getMediaByID Calls getMediaByCode after transforming
ID to a shortcode
getMediaByLocationID Returns information on the n most recent
media post for a given location ID
getMediaByTag Returns information on the n most recent
media post for a given hashtag
getMediaByURL Returns information on a media post with a
given URL
getMediaByUsername Returns the n most recent media posts from
an account with the given username
24
Table 6 - Format of the Results for the Get Functions in the instaExtract Package
Function Results
getCommentsByMediaCode n x 6 data frame: id, text, created_at, owner.id,
owner.profile_pic_URL, owner.username getCommentsByMediaID n x 6 data frame: id, text, created_at, owner.id,
owner.profile_pic_URL, owner.username getCurrentTopMediaBy LocationID
9 x 14 data frame: id, thumbnail_src, tubmnail_resources,
is_video, code, date, display_src, video_views, caption,
dimensions.height, dimensions.width, owner.id,
comments.count, likes.count
getCurrentTopMediaByTag 9 x 14 data frame: id, shortcode, taken_at_timestamp,
display_URL, tumbnail_src, tumbnail_resources, is_video,
video_view_count, edge_media_to_caption.edges,
edge_media_to_comment.count, dimensions.height,
dimensions.width, edge_liked_by.count, owner.id
getLikesByMediaCode n x 7 data frame: id, username, full_name,
profile_pic_URL, is_verified_followed_by_viewer,
requested_by_viewer
getLikesByMediaID n x 7 data frame: id, username, full_name,
profile_pic_URL, is_verified_followed_by_viewer,
requested_by_viewer getLocationByID 1 x 6 data frame: id, name, has_public_page, lat, lng, slug
getMediaByCode 1 x 42 data frame: meta information about a post not
including its comments and likes getMediaByID 1 x 42 data frame: meta information about a post not
including its comments and likes getMediaByLocationID n x 15 data frame: comments_disabled, id, thumbnail_src,
thumbnail_resources, is_video, code, date, display_src,
video_views, caption, dimensions.height,
dimensions.width, owner.id, comments.count, likes.count
getMediaByTag n x 16 data frame: comments_disabled, id, thumbnail_src,
thumnail_resources, is_vdeio, code, date, display_src,
caption, dimensions.height, dimensions.width, owner.id,
comments.count, likes.count getMediaByURL 1 x 42 data frame: meta information about a post not
including its comments and likes getMediaByUsername n x 17 data frame: _typename, id, comments_disabled,
getting_info, media_preview, thumnail_src,
thumbnail_resources, is_video, code, date, display_src,
caption, dimensions.height, dimensions.width, owner.id,
comments.count, likes.count
In order to provide a better understanding of how the package operates, this paper
provides a walk-through of the getMediaByTag function, which is representative of
25
how most of the other Get functions operate.
getMediaByTag <- function(tag, n = 20, maxID = ""){
The function call has three arguments: 1) tag – the query a user is looking to
gather data on; in this case, a hashtag. 2) n – the number of responses that will be
returned. Each function defaults to the number of results that are returned in the first
JSON file. 3) maxID – an identifier that allows the function to start its search from a
different position. The user will usually only use the default of an empty character, but
the function will use it to paginate through JSON results.
i <- 0
moreAvailable <- TRUE
data <- data.frame()
Next, the function initializes variables used in its operation. The integer i is used
to keep track of the number of results returned. The boolean moreAvailable will track
if more results from Instagram are available. Initializing data as a data frame allows
inserting new rows as results come in.
#will run while more data exists and it has not reached n results
while(moreAvailable && i < n){
The main operations of the function, creating a link and collecting the results, will
happen in this while loop. It will loop until there are no more results available or until
the number of results is greater than n.
26
#create the url from Json Link
url <- getTagMediaJsonLink(tag,maxID)
Tag_Media_Json_Link <- "https://www.instagram.com/explore/tags/{tag}/?__a=1&max_id={maxID}"
getTagMediaJsonLink <- function(tag, maxID){ link <- Tag_Media_Json_Link link <- gsub("{tag}", tag, link, fixed = TRUE) link <- gsub("{maxID}", maxID, link, fixed = TRUE) return(link) }
Calling the helper function getTagMediaJSONLink creates the JSON link URL.
When this function is called, it passes the desired hashtag and the current maxID. The
helper function will then alter a constant string that was defined before compilation by
inserting the given hashtag and maxID. The character string this function returns is the
URL that will be used to retrieve the desired JSON file.
#the unflattened response response <- jsonlite::fromJSON(url)
#will return as list if there is only one result
if(!is.data.frame(response$graphql$hashtag$edge_hashtag_to_media$edges$node)){ return(response$graphql$hashtag$edge_hashtag_to_media$edges$node) }
else{ #flattening the data down to the nodes, into a dataframe media <- jsonlite::flatten(response$graphql$hashtag$edge_hashtag_to_media$edges$node)
27
Next, the URL is used to retrieve the JSON file from Instagram. The imported
function jsonlite::fromJSON translates the JSON file to a R usable data frame. If
there is only one result in this JSON file, it will return this list without doing the rest of
the function which assumes that it has a data frame. In the normal case, when a data
frame is returned, jsonlite::flatten will flatten the data frame to be a regular two
dimensional data frame, meaning that each column of the data frame is atomic, rather
than a list with multiple values. In most cases, the desired information is located inside
containers such as “graphql” and coupled with superfluous information. To target the
useful information, the package must reference the correct container in the data frame
returned from the JSON link. In each case, this path is unique to a function and can
change between Instagram updates. This points towards a need to have this package
quickly modifiable and subsequently distributable, which is addressed in Section 3.3.
#iterating over the rows of the media for(row in 1:nrow(media)){
#will exit loop and return data if reaching the limit if(i == n){ return(data) }
#will add a new row of media to data data <- plyr::rbind.fill(data,media[row,])
#incrementing the counting index i <- i + 1 }
This segment of code will iterate through the rows of the results returned from
Instagram. Each time, it will add that row to the data frame intended to be returned. The
rbind.fill function from the plyr package to ensures that information from video
28
and picture posts can be added to the same data frame. The function keeps track of how
many rows it has entered and will return the results if it reaches the desired number.
#Where to start the next query to the instagram link
maxID <- response$graphql$hashtag$page_info$end_cursor
#makes sure more exists
moreAvailable <-
response$graphql$hashtag$page_info$has_next_page
}
}
The last step in the while loop is storing the identifier that allows the function to
retrieve the next set of information. Again, emphasis is given to the nature of the JSON
files’ structures returned. The location of these identifiers inside the JSON is subject to
change, and without a method for updating, the function might become obsolete.
#convert the json data to R dataframe return(data) }
Now that data is filled with n results, it is returned to the user. Each Get function
returns a variety of data frames with different elements, as outlined above in Table 6. The
remaining Get functions operate very similarly to getMediaByTag, however each has a
unique URL and data structure to its JSON file.
The Get functions in this package represent most of the functionality available in
other Instagram scrapping programs. Not currently possible is retrieving following and
follower information from an account, or getting a username from an owner ID. This gap
in functionality also means that the package operates without needing log-in credentials
or interaction with the Instagram API. Utilizing the JSON-based open API, the package
29
relies on constants that must be defined by the developer. Therefore, the maintainer of the
package is incentivized to house it in a manner that is adaptable to updates to Instagram.
This concern is solved by hosting the package on the AFIT Data Science Lab’s GitHub
page, a process discussed in the next section.
3.3 Adaptable, Reproducible, Distributable – Hosting on the AFIT Data Science
Lab’s GitHub Page
To ensure the instaExtract package is adaptable, reproducible, and
distributable, it is hosted on it on the GitHub page of the AFIT Data Science Lab. From
their website, “GitHub is a code hosting platform for version control and collaboration”
(“Hello World · GitHub Guides,” n.d.). That succinct description captures why GitHub
can be an indispensable tool for this package and for many military analysis operations.
GitHub allows the package to be accessible to all systems that have access to the internet,
to store previous versions of the package to reproduce reports done in the past, and to
facilitate discussion and evolution between users and developers of the package.
3.3.1 Benefits of GitHub for Military Analysts
An immediate consequence of hosting the package in an online repository is that
the most current edition of the package is available to any user with access to the internet.
The tools needed to conduct Instagram data scraping are not locked away on a machine.
Instead, the user can have a consistent and reliable way to locate the tools they are
familiar with and confident in. A further benefit of a centralized hub like the AFIT Data
Science Lab is that consolidated resources will lead to more capable and competent tools.
When the standard practice for analysts becomes working from a common set of
applications, those applications benefit from increased attention and use. Additionally,
30
users who begin working within established systems will have increased code and data
compatibility. The benefits of consolidation and cooperation are multiplicative.
GitHub furthers this research’s goal of enabling reproducible analysis. R as a
language already facilitates reproducibility with workable data types and dynamic reports
in R markdown. GitHub goes a step further and ensures that the exact version of the
package used to construct the report is available to an analyst who is validating it. GitHub
stores the changes made to a package, meaning as long as the version of the package used
is recorded in the report, it can be downloaded on the GitHub page. This prevents future
changes to a function from impacting the validation process. An older version of the
package should operate just as it did when the analysis was first conducted.
Perhaps the strongest benefit of using the GitHub platform is the level of
collaboration and adaptability. It is a common occurrence in large organizations, the
military included, for there to be a large disconnect between the developer of a tool and
the end users of that tool. The end user is often left with needless capabilities or lack
those that are essential. GitHub facilities a connection between users and developers, and
in some cases, blurs the distinction between the two. The development process for a
conventional program hosted on a local machine is filled with inefficiencies and
difficulties. First, the developer is made aware of a need. This alone might have taken
months to perforate a command structure and reach their desk. Next, they need to collect
information on the end user’s needs or operate off of the limited information they might
have been given with the assignment. After working on the project for a time, they will
seek feedback with the end user. This will lead to revision cycles that take time, money
and are bound to miss complications that will arise when the program ships. The resulting
31
program will then need a method of distribution, which can range from an online
download, to an executable passed through a file sharer. Whatever the method, the end
user is left with a program that might or might not meet all their needs. The first few
weeks of operations will be monitored by the developer, who can make necessary
changes and encourage users to download the most recent version. But after a time, the
gap between a program’s users and the developer widen. New issues arise, whether from
updated needs of the user or from unforeseen use cases. Complaints may be raised, but
resolutions suffer from the same slowdowns that the initial development suffered from. If
users result to fixing these issues themselves, different versions of the program can begin
to diverge, causing new issues with incompatibility and distribution. The end result of
the conventional set up is that developers are isolated from the users they are creating for
and the users are stuck with programs that are stagnant and restrictive.
Comparing that process with that of development through GitHub illustrates the
benefits of a shared hosting platform. With centralized hubs of development, like the
AFIT Data Science Lab, requests for new tools can be addressed with expedience.
Depending on the scope and the urgency of the problem, the development might be
conducted by students, civilians (government or non-government), or dedicated military
analysts. In all cases, the development process can be much more organic than the
conventional process. Errors and inadequacies are to be expected to some degree in all
forms of computer programs, but GitHub facilitates communication and issue resolution
to minimize the distribution flaws cause the user. In the conventional example, an issue
that arises after the development process of an application can be hard to remedy. With
GitHub, the end user can instantly notify the developer or the staff assigned as
32
administrators to the application. The error might be as simple as misuse of the
application, in which case, the administrator can reply to the issue and guide the user
through operation. If the issue turns out to be something more complex, the administrator
can use the issue as immediate feedback about the workings of the application. Since the
application is hosted in one predictable location, any updates the administrator makes to
fix the problem will automatically be reflected in the current of the program. Users can
always ensure that their programs are up to date and they are operating with the same
tools that everyone is using.
3.3.2 An Example of GitHub’s Potential Adaptability – Location Mapping in the
instaExtract Package
GitHub also offers users the ability to directly submit their own contributions,
blurring the line between developer and user and greatly increasing the adaptability of a
program. If a user finds that they have need for a new function, its within their power to
create it. GitHub gives user access to the source code, allowing them to add to and
modify their local version of the package. More than that, GitHub provides the means for
that user to add their own custom function to the main package. They may request a
change to the main program, and provided the administrators approve the change, that
custom function can now be used any other user with access to the package. This
integration of users into the development process results in ever expanding functionality
and increased resiliency.
To illustrate the potential of user created functionality, the instaExtract
package includes a set of functions that broaden the capability of the package and enable
users to conduct more thoughtful analysis. This set of functions is detailed in Table 7. A
33
current weakness in the JSON dependence of the package compared with the Instagram
API is that there is no readily available way to search for posts around a given latitude
and longitude. The solution presented by this research to this problem relies on extracting
the latitude and longitude from a location’s page. These values are then used to create a
location mapping, a data frame with locations and their information.
Table 7 - Functions Enabling Location Mapping Capabilities
Function Purpose
createLocationMapping Creates a data frame with all
locations in a search region
haversineDistance Calculates the distance in miles
between two latitude and longitude
points
getCurrentTopMediaByLocationMapping Returns the top n results for each
location in a location mapping
getMediaByLocationMapping Returns the n most recent results for
each location in a location mapping
getLocationsInRange Returns a filtered location mapping
with locations within a certain radius
The core function developed to achieve these functions is called
createLocationMapping.
createLocationMapping <- function(country ="", city = "", lat_long = FALSE){
This function creates the data frame in the format referred to as a “location
mapping” in this paper. It has three arguments: 1) country – the name/s of a country to
search inside. If no name is given, the mapping will be for all countries in Instagram’s
explore page. 2) city – the name/s of a city to search inside. If no name is given, the
mapping will be the top 1000 cities for all countries in the scope of the function. 3)
34
lat_long – if true, the function will gather the latitude and longitude values for each
location. It is defaulted to false since this operation requires a web query for each location
and can take significant amounts of time.
The key component to the operation of the createLocationMapping function
is the explore/locations page on Instagram. The top layer for this subdomain lists the
names of all countries in Instagram’s databases. The function will grab the names of all
countries in its scope, as well as their slug’s and ID. Using this information, it can move
down a layer to the country’s subpage, where it lists the top 1000 cities for that country.
Likewise, the function will grab information from this page to travel to each city within
the argument’s scope. The last level of explore/locations is a list of the top 1000 locations
in all the selected cities. These locations are what are added to the location mapping,
along with their slug and ID and the slug and ID of the city and country they belong to. If
the user selected to gather coordinates from the location, the function will also visit each
location page to grab the latitude and longitude. This can take a very long time depending
on the speed of the user’s internet connection and the number of locations in the location
mapping. However, since the information is retained in a data frame, a user can utilize
location mappings made in prior, avoiding this time constraint. Furthermore, it would be
possible for future contributors to build package that facilitate this process by hosting
current versions of location mappings. This would mean the analyst only has to
download the relatively small file instead of performing thousands of time consuming
queries.
With the location mapping created, it offers the user new manners for conducting
smart analysis on Instagram’s potential data. Two of the functions extend the package’s
35
ability to get media from a location to getting media from a location mapping. The
haversineDistance function enables the user to filter a location mapping to only
locations with a mileage radius of place of interest. To demonstrate what these functions
add to the package, this paper walks through an example scenario. Suppose that a user is
interested in what posts are being made in vicinity of the White House. The first step
would be to create the location mapping of Washington D.C.
washington_dc <- createLocationMapping("United States", "Washington", TRUE)
This function call creates the a mapping for the city “Washington” in the country
“United States” with the coordinates information. The variable washington_dc now
contains information for the top 1000 locations on Instagram in Washington DC.
lat <- 38.8977
long <- -77.0365
range <- .5 #miles
Next, the user sets the region they are interested in. The coordinates for the White
House are 38.8977° N, 77.0365° W. In this case, they are interested in locations within
half a mile from the point.
36
locations_near_white_house <- getLocationsInRange(washington_dc, range, lat, long)
getLocationsInRange <- function(mapping, r, lat, long, ...){
if(!is.numeric(r) || !is.numeric(lat) || !is.numeric(long)){ stop("r, lat, and long, must be numeric") } mapping <- filter(mapping, haversineDistance(latitude, longitude, lat, long) <= r)
return(mapping)
}
Using the simple function, getLocationInRange, the user can narrow down
the location mapping to a location mapping of only those locations within the range of
the white house set before. The haversineDistance function will return the distance
of each point from the White House, and only locations within half a mile from the White
House will be included in the location mapping named
locations_near_white_house.
near_house_media <- getMediaByLocationMapping(locations_near_white_house)
The last step is to use location mapping version of the get media function to
retrieve information on the recent media from the locations within our range. This
procedure results in a data frame with information on the most recent posts made at
locations near the White House. A user would be free to conduct whatever analysis they
wish with the data provided, but possible areas of interest might be mapping commonly
used hashtags for an area, comparing data from multiple days to observe fluctuations in
time, identifying prolific accounts, etc.
37
The location mapping functions created represent the future potential of the
instaExtract package or any package hosted on a collaborative platform like GitHub.
As users discover new ways to conduct analysis or require new data sources, the
functions they spur or create themselves can be added to the package. This in turn,
facilitates and inspires other analysts in conducting sophisticated and modern analysis of
their own.
3.4 Summary
This research culminates in a R package called instaExtract hosted on the
AFIT Data Science Lab’s GitHub page. R is an open source program that is easily
accessible and built around reproducible practices. The instaExtract package
provides users with a way to scrape current Instagram data without authentication or
interaction with the Instagram API. By hosting the package on the AFIT Data Science
Lab’s GitHub page, the package has an increased potential to stay relevant and useful to
real world analysts. Also included is an original set of functions curtained around
mapping locations to illustrate how future collaboration on this project might continue to
extend its usefulness.
38
IV. Exemplar Analysis
4.1 Overview
To better illustrate the capabilities and value of the instaExtract package, this
thesis includes an exemplar analysis that will emulate how an end user might use the
package. The user is assumed to have a working understanding of R and Instagram. The
first scenario focuses of an analyst investigating threats near a location, in this example
the White House. Section 4.2 discusses the gathering of information about the area. Next,
the analysis will conduct basic filtering and visualization techniques as well as other
instaExtract functions to investigate further.
4.2 Gathering Information on the Region
The investigation of the threat begins by collecting information about the
locations surrounding the White House. The user creates a location mapping of the
Washington D.C. region. The first 30 entries of the data frame of 1000 locations
generated by this function are found in Appendix B.
washington_dc <- createLocationMapping("United States", "Washington", TRUE)
Next, the location mapping is narrowed down to find only the locations within a
mile of the White House. As in the demonstration in the methodology section, the user
sets the coordinates of the White House and use the function getLocationsinRange
to filter the locations. The first 30 entries of the data frame of 524 locations are shown in
Appendix C.
39
lat <- 38.8977
long <- -77.0365
range <- 1 #miles
locations_near_white_house <- getLocationsInRange(washington_dc, range, lat, long)
Again, the next step is to collect the media from the area, but to be a little more
thorough, this user will get the last 50 posts from each area. The first 60 of 26,200 results
are partially shown in Appendix D. The complete values of the URLs and captions are
obscured to allow the table to be represented on one page. This operation took about 30
minutes, meaning that instaExtract is able to pull the information about a post around
15 times a second.
media_near_white_house <- getMediaByLocationMapping( locations_near_white_house, 50)
4.3 Hashtag Investigation
Now possessing media data, the user can begin to investigate it. There are many
different avenues to take at this point, where the following courses represent only some
of the possible paths. First, the user chooses to investigate the hashtags being used. The
hashtags contained in the captions are isolated and sorted to find the most commonly
used hashtags. Using the tidytext and stringr packages, the user first breaks down
each caption to separate elements for each word. Then, the words are filtered down to
only those words that start with ‘#’; our hashtags. Sorting these, the user finds the most
common hashtags recently used in the search region. The results indicate that there are
36971 hashtags used in total, with the top 15 displayed below. Filtering that result
further, only 59 hashtags have been used more than 100 times in search radius. Further,
40
as Figure 5 and Figure 6 show, even the top 150 hashtags are greatly skewed to the top,
with the top two, “#washingtondc” and “#dc” being used upwards of 2000 times each,
with next highest, “#washington” only being used 513 times.
library(stringr)
library(tidytext)
hashtags <- media_near_house3 %>% unnest_tokens(word, caption,token = "regex") %>% filter(substr(word,1,1) == '#') %>% count(word,sort = TRUE) # A tibble: 15 x 2 word n <chr> <int> 1 #washingtondc 2612 2 #dc 2171 3 #washington 513 4 #travel 489 5 #usa 430 6 #igdc 423 7 #love 405 8 #instagood 371 9 #acreativedc 369 10 #tbt 350 11 #photography 338 12 #foodie 325 13 #foodporn 313 14 #washingtonmonument 296 15 #valentinesday 295
41
Figure 5. Word Cloud for Top 200 Hashtags in the DC Area
42
Figure 6. Count of 150 Most Used Hashtags in the DC Area
For the next data utilization scenario, the user has a list of key words that they are
monitoring. It is possible to compare the list of keywords against the list of hashtags to
look for suspicious hashtags. The code to perform this comparison and the top 6 results of
such a search are shown below. It reveals 148 potentially threating hashtags. The top
result, “#justgoshoot”, has been used 45 times in the scope of the search.
43
keywords <- c('shoot', 'gun', 'kill', 'bomb')
threats <-hashtags %>% filter(str_detect(word, paste(keywords, collapse = "|"))) # A tibble: 6 x 2 word n <chr> <int> 1 #justgoshoot 45 2 #photoshoot 34 3 #shoot2kill 10 4 #citykillerz 7 5 #shootingwithshooters 7 6 #tonekillers 7
This hashtag turns out to be a hashtag used by photographers, but if it really was a
threat, instaExtractcan continue provide more information. To find more data on the
usage of this hashtag, the user retrieves the last 10,000 posts that used this hashtag.
Further, to know who used this hashtag most in the most recent 10,000 uses, the user
counts the list again, and finds that the account with ID 6961358493 used the hashtag 50
times. Outside tools could provide for even more complex analysis. The large amount of
written word in the captions makes a good source for sentiment analysis. This data
contains numbers of likes and comments, so a user could also sort for popular posts that
fit a given criteria.
44
just_go_shoot <- getMediaByTag("justgoshoot", 10000)
just_go_shoot %>% count(owner.id, sort =TRUE)
owner.id n <chr> <int> 1 6961358493 50 2 2014353892 45 3 495504345 37 4 7129727912 30 5 29197162 29 6 4843411210 29 7 374142894 28 8 298064835 24 9 1524025234 20 10 4976873299 18
4.4 User Investigation
A user can pull information from an account to find more detailed information
about that account’s history. The first step would be to download media from the chosen
account’s page. In this case, the account under investigation has the username
“usairforce.” Retrieving their last 2000 posts returns 1390 values, the first 60 of which
are shown in Appendix E, since this account has only posted 1390 times.
usairforce <- getMediaByUsername("usairforce", 2000)
The data collected can be used to get a better idea user’s is attributes. The package user
begins by finding the post with the most likes, their most popular post. Running any code
is not needed to do this, as R’s data viewer allows sorting a column in descending value.
In this case, the account’s top post, ID 1698060742908836418, has 41,541 likes, and the
photo is shown in Figure 7. The number of likes plotted against the postdate results in
45
Figure 8, showing the trend of like counts over time. This figure which shows a gradual
increase in the average likes over time.
Figure 7. Most liked Instagram Post by usairforce (usairforce, 2018)
46
Figure 8. Post Likes Over Time for usairforce Account
The package also allows a closer look at a post. A media ID is used to retrieve the
comments from the top post on this account in the getCommentsByMediaID function,
the first 60 of 269, which are shown in Appendix F. Using this information, the most
common words used in the captions are found in a process similar to that used above.
47
comments <- getCommentsByMediaID('1698060742908836418', n = 300)
commentWords <- comments %>% unnest_tokens(word, text,token = "regex") %>% filter(!word %in% stop_words$word) %>% count(word,sort = TRUE)%>% mutate(word = reorder(word, n)) # A tibble: 6 x 2 word n <fctr> <int> 1 love 14 2 air 13 3 rdgjklcb 11 4 force 10 5 girl 7 6 pilot 7
Comments can also provide insight to connections between users. Using the
comments from posts, the user can build a network of users who interact with the
usairforce account. To begin, 300 comments from the last 50 posts are collected. From
these, the users who have left the most comments is calculated. Although simple, the
network built from this data, Figure 9, hints at how networks can be developed from the
social media data obtained this way.
48
all_comments <- data.frame()
for(row in 1:50){
comment_holder <- getCommentsByMediaID(usairforce[row,'id'],300)
all_comments <- plyr::rbind.fill(all_comments, comment_holder)
}
commenting_users <- all_comments %>% count(owner.username, sort=TRUE)
# A tibble: 6 x 2
owner.username n
<chr> <int>
1 my_babel_physics_project_ai 30
2 admininnotinuse 28
3 zafarwestern 26
4 gomezbaquerosol 21
5 viktoria__lg.troxell 21
6 lydiavassallo 20
Figure 9. Network of Commenting Users on usairforce Posts Where Edge is
Proportional to Number of Comments
49
4.5 Geo Mapping
Part of the analytic cycle, shown in Figure 4, is visualizing the data. This allows
the analyst to grasp what type of data they are working with and understand the
significance of their analysis. R has many great packages that can help an analyst
accomplish this feat. One of these packages is leaflet, a JavaScript library used to
make interactive maps (Cheng, 2017). This section revisits the data used in section 3.3.2,
the location mapping of Washington DC. Using leaflet, an analyst can overlay the
locations in the location mappings with a real map of the area. The result, seen in Figure
10, is cluttered beyond much helpfulness.
Figure 10. All Washington DC Locations from Instagram Overlaid with Actual
Location
50
Luckily, leaflet comes with a clustering option that drastically increase the
readability of the map. In Figure 11, clustering has been turned on. The analyst can now
see where the locations are focused, without overwhelming the senses. Hovering over a
cluster shows the region it represents. For closer detail, the analyst can click on a cluster
to zoom in and expand its contents. Once a node is clicked, leaflet will display the name
of the location. Figure 12 is a fully zoomed in example. All of the data displayed in these
maps is nothing more than the information in a location mapping, but displaying that
information in a real world setting adds context and meaning to the information that
allows analysts to draw connections and make inferences that they would be hard pressed
to do without that context.
Figure 11. Clustered Locations for Washington DC
51
Figure 12. Zoomed in View of White House Instagram Locations
4.6 Summary
The applications of the data derived from this package are plentiful. With the
many R packages at the disposal of users, visualization and analysis can be accomplished
with style and ease. The data provided allows for media, user, and location driven
analysis. As this package matures, these procedures can be added to the functionality of
the package, but it currently serves an important role of providing useful and unique
social media data to analysts in the intelligence community.
52
V. Conclusions and Recommendations
5.1 Overview
The potential for the instaExtract package and the practices this thesis hopes
to encourage in the military analytical community has been shown to be promising, but
the long-term success of this and future programs will rely on the adoption rate of users
and the continued support of technical organization like the AFIT Data Science Lab. At
its core, the instaExtract package achieve its operational goal of providing resources
for analysts to extract Instagram data. Additionally, this thesis attempts to accomplish this
in a manner with forward reaching implications. R is a heavily supported, open source
language, with new resources created by the community on a routine basis. Choosing to
host the package on GitHub allows the package the benefit of future support and user
involvement.
5.2 The instaExtract Package
The instaExtract package boasts most of the same functionality offered by
similar available programs while exhibiting even more paths for it to grow. First and
foremost, the package achieves its goal of scraping Instagram for data. It offers search
functions and a suite of get functions that allow a user to obtain almost any information
that Instagram tracks. Furthermore, it has a collection of location mapping functions that
mimic the features that were traditionally limited to programs interacting with the
Instagram API. All of these functions can be operated without authorization and the need
to have log-in information for an Instagram account.
The package also has many areas for future development. While most information
can be retrieved without authorization, or otherwise inferred from available data, there
53
are some areas of Instagram that require log-in credentials. Future work should be done
to ensure the robustness of operations, such as compilation and use time error and
compatibility checking. As a final step towards professionalization, the package could
also be published to the Comprehensive R Archive Network (CRAN). CRAN is, as its
name suggests, a comprehensive archive of R packages that amount to the most
commonly used and relied upon functions in R. Publishing a package to CRAN allows
users to be sure of a certain set of requirements and accessibility that are enforced by the
CRAN maintainers.
In addition to work that falls within the purview of good coding practices, the
instaExtract package has many avenues of growth that would benefit users and
increase its applications. While certainly not exhaustive, this thesis suggests a few ideas
considered to be worthy of attention:
• Extending Location Mapping Functionality – Currently, the location mapping
functions can only access those locations that are listed through the
explore/locations page on Instagram. Adding functions that allow users to
quickly add their choice of locations to a mapping or that seek out other locations
on their own could further enhance the usefulness of this collection.
• A Shiny Application – Shiny is an R package that creates applications to allow
users to interact with a user interface rather than command lines in R. A Shiny
app would increase the reach of the package and potentially help inform its users
of all of its functions
• Proxy Settings – The instaExtract package uses the cURL package to
interface with internet. As such, it does not have well behaved proxy settings.
54
Incorporating better ways to control these settings could allow for a user to work
through a proxy, integrating with a local network or masking the location of their
requests.
• Other Scrapping Techniques –How the JSON link approach to data scrapping is
susceptible to changes in Instagram’s structuring has already been discussed.
However, it would be possible for future developers to allow for users to scrape
their selected data through other means. One such option is the Selenium
package, which uses conventional browsers to navigate between webpages.
Although it is exclusive, gaining access to the Instagram API could enable many
new options and considerably accelerate existing ones.
• Analysis Functions – The package was designed around the goal of scrapping
data, but it would be natural to include analytical functions in the future. The
current package lends itself to sentiment analysis, anomaly detection, and more.
With methods of pulling the images that are represented currently only as URLs
would also allow for image recognition.
5.3 The AFIT Data Science Lab
A major academic pursuit of this paper is to validate the viability and incentives
of programs hosted on a centralized and collaborative platform, such as the AFIT Data
Science Lab. The GitHub platform will greatly simplify and facilitate the future
development of this package. Users not only have access to a reliable and up-to-date
version of the software, but can maintain a responsive dialog with the maintainers of the
code as well as share the results of their individual efforts. GitHub’s version control also
55
enables analysis with a focus on reproducibility by storing previous versions of the
package.
The long-term value of these pursuits will be determined by the adoption rate of
similar practices by other analytical software and the amount of support given to software
development organizations. As discussed, packages stored in a centralized location
receive a multiplicative benefit of enjoying a higher user base and more engaged
developers. But of course, this requires the support structure to address development
issues and to create new packages to meet user needs. Ensuring this support staff is
valued and funded will require further commitment to similarly focused programs and
research that can validate the worth of such programs.
5.4 Summary
It is clear that instaExtract package adds value to the analytical and
intelligence communities, but this research acknowledges that there is more work to be
done in solidifying the robustness of this package and validating and realizing a
collaborative and comprehensive hub for analytic resources. Future research should be
done into more data acquisition software and the statistical benefits of platforms like
GitHub and their effects on DoD workflows.
56
Appendix A
Link to the instaExtract package: https://github.com/AFIT-R/instaExtract
57
Appendix B
id name slug city_ID city_Name city_Slug
country
_ID
country_N
ame country_Slug latitude longitude
213480180
Washington, District of
Columbia
washington-district-
of-columbia c2427178 Washington
washington-
united-states US
United
States united-states 38.8951 -77.0367
225931565 The Obama White House
the-obama-white-
house c2427178 Washington
washington-
united-states US
United
States united-states 38.89768 -77.03655
15712 Lincoln Memorial lincoln-memorial c2427178 Washington
washington-
united-states US
United
States united-states 38.889444 -77.050278
3001994 United States Capitol united-states-capitol c2427178 Washington
washington-
united-states US
United
States united-states 38.8897301 -77.0070362
214773851
Washington Monument
National Monument
washington-
monument-national-
monument c2427178 Washington
washington-
united-states US
United
States united-states 38.8890235 -77.0331092
4366681 National Gallery of Art
national-gallery-of-
art c2427178 Washington
washington-
united-states US
United
States united-states 38.8913397 -77.0196344
82474402
Smithsonian’s National Zoo
and Conservation Biology
Institute
smithsonians-
national-zoo-and-
conservation-biology-
institute c2427178 Washington
washington-
united-states US
United
States united-states 38.9299778 -77.0511297
1191441824276880 The White House the-white-house c2427178 Washington
washington-
united-states US
United
States united-states 38.8968447 -77.0366049
235453813 Nationals Park nationals-park c2427178 Washington
washington-
united-states US
United
States united-states 38.8732565 -77.0075808
372247132 Capital One Arena capital-one-arena c2427178 Washington
washington-
united-states US
United
States united-states 38.89795 -77.02096
262515071
Smithsonian National
Museum of African American
History and Culture
smithsonian-
national-museum-of-
african-american-
history-and-culture c2427178 Washington
washington-
united-states US
United
States united-states 38.8911015 -77.0325639
373555 Renwick Gallery renwick-gallery c2427178 Washington
washington-
united-states US
United
States united-states 38.898867 -77.039447
236471522 The Mall (Washington DC)
the-mall-washington-
dc c2427178 Washington
washington-
united-states US
United
States united-states 38.89 -77.0236111
214720506 The Capitol, Washington D.C.
the-capitol-
washington-dc c2427178 Washington
washington-
united-states US
United
States united-states 38.8898395 -77.0154594
279173 Howard University howard-university c2427178 Washington
washington-
united-states US
United
States united-states 38.92314 -77.02158
214638086
Smithsonians National
Museum of Natural History
smithsonians-
national-museum-of-
natural-history c2427178 Washington
washington-
united-states US
United
States united-states 38.8910781 -77.026232
2222215 National Building Museum
national-building-
museum c2427178 Washington
washington-
united-states US
United
States united-states 38.89779 -77.01752
838999
National Museum of American
History
national-museum-of-
american-history c2427178 Washington
washington-
united-states US
United
States united-states 38.8913741 -77.0299286
175770
Hirshhorn Museum and
Sculpture Garden
hirshhorn-museum-
and-sculpture-
garden c2427178 Washington
washington-
united-states US
United
States united-states 38.8883276 -77.0229156
212896512
National Air and Space
Museum, Smithsonian
Institution
national-air-and-
space-museum-
smithsonian-
institution c2427178 Washington
washington-
united-states US
United
States united-states 38.8881412 -77.0198422
42620 9:30 Club 930-club c2427178 Washington
washington-
united-states US
United
States united-states 38.91803 -77.02363
214513963
Union Station, Washington
D.C.
union-station-
washington-dc c2427178 Washington
washington-
united-states US
United
States united-states 38.8975 -77.00621
2150339
The George Washington
University
the-george-
washington-
university c2427178 Washington
washington-
united-states US
United
States united-states 38.8982042 -77.050286
139456 Jefferson Memorial jefferson-memorial c2427178 Washington
washington-
united-states US
United
States united-states 38.881111 -77.036667
576233589
Lijst van bekende mensen uit
Washington D.C.
lijst-van-bekende-
mensen-uit-
washington-dc c2427178 Washington
washington-
united-states US
United
States united-states 38.8951111 -77.0366667
216550363
National Portrait Gallery,
Smithsonian Institution
national-portrait-
gallery-smithsonian-
institution c2427178 Washington
washington-
united-states US
United
States united-states 38.8977641 -77.0229628
849479 The Library of Congress
the-library-of-
congress c2427178 Washington
washington-
united-states US
United
States united-states 38.8887863 -77.0058375
1009997177 Georgetown, DC georgetown-dc c2427178 Washington
washington-
united-states US
United
States united-states 38.9026909 -77.0494537
251483 Capitol Hill capitol-hill c2427178 Washington
washington-
united-states US
United
States united-states 38.889722 -77.011111
58
Appendix C
X id name slug cit y_ID cit y_Name cit y_Slug
count r
y_ID
count ry
_Name count ry_Slug lat it ude longit ude
1 213480180
Washingt on, Dist r ict of
Columbia washingt on-dist r ict -of -columbia c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.8951 -77.0367
2 225931565 The Obama Whit e House t he-obama-whit e-house c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.89768 -77.03655
3 15712 Lincoln Memorial lincoln-memorial c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.889444 -77.050278
5 214773851
Washingt on Monument
Nat ional Monument
washingt on-monument -nat ional-
monument c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.8890235 -77.0331092
8 1191441824276880 The Whit e House t he-whit e-house c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.89684467 -77.03660488
10 372247132 Capit al One Arena capit al-one-arena c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.89795 -77.02096
11 262515071
Smit hsonian Nat ional
Museum of Af r ican
American Hist ory and
Cult ure
smit hsonian-nat ional-museum-
of -af r ican-american-hist ory-
and-cult ure c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.89110149 -77.03256389
12 373555 Renwick Gallery renwick-gallery c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.898867 -77.039447
13 236471522
The Mall (Washingt on
DC) t he-mall-washingt on-dc c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.89 -77.02361111
16 214638086
Smit hsonians Nat ional
Museum of Nat ural
Hist ory
smit hsonians-nat ional-museum-
of -nat ural-hist ory c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.89107814 -77.02623198
18 838999
Nat ional Museum of
American Hist ory
nat ional-museum-of -american-
hist ory c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.89137413 -77.02992863
19 175770
Hirshhorn Museum and
Sculpt ure Garden
hirshhorn-museum-and-
sculpt ure-garden c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.88832764 -77.02291558
23 2150339
The George Washingt on
Universit y
t he-george-washingt on-
universit y c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.8982042 -77.05028604
25 576233589
Lijst van bekende
mensen uit Washingt on
D.C.
lijst -van-bekende-mensen-uit -
washingt on-dc c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.89511111 -77.03666667
26 216550363
Nat ional Port rait
Gallery, Smit hsonian
Inst it ut ion
nat ional-port rait -gallery-
smit hsonian-inst it ut ion c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.8977641 -77.02296276
28 1009997177 Georget own, DC georget own-dc c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.9026909 -77.0494537
30 2091823
Nat ional World War II
Memorial nat ional-world-war- ii-memorial c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.88935278 -77.04055556
31 26131 Newseum newseum c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.89313 -77.01935
33 590718213 Dupont Circle dupont -circle c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.9096 -77.0434
34 21614
Walt er E. Washingt on
Convent ion Cent er
walt er-e-washingt on-
convent ion-cent er c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.9042406 -77.02325315
38 342980053 Tidal Basin t idal-basin c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.8837 -77.0389
39 215073 Ref lect ing Pool ref lect ing-pool c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.88926578 -77.04750981
40 683422 Madame Tussauds DC madame-t ussauds-dc c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.89759 -77.0261699
41 75367954 Le Diplomat e le-diplomat e c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.91136386 -77.03157239
43 21783
Int ernat ional Spy
Museum int ernat ional-spy-museum c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.89699 -77.02329
44 218723464
Nat ional Museum of
Women in t he Art s
nat ional-museum-of -women-in-
t he-art s c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.90006 -77.02916
47 212897059 Washingt on, DC washingt on-dc c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.89511111 -77.03666667
53 1420252
Mart in Lut her King, Jr.
Nat ional Memorial
mart in- lut her-king-jr-nat ional-
memorial c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.88611111 -77.045
55 11195531
Smit hsonians Freer and
Sackler Galler ies
smit hsonians-f reer-and-sackler-
galler ies c2427178 Washingt on
washingt on-
unit ed-st at es US
Unit ed
St at es unit ed-st at es 38.88796 -77.02645
59
Appendix D
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5243
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pg
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70
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pg
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pg
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61
Appendix F
id text created_at owner.id owner.profile_pic_urlowner.username
17902716826081000 Nice!! 1516644761 1257719479 https://scontent-ort2-2.cdninstagram.com/vp/6e9dc1d18060a803c403d0fa1284e47e/5B1531BB/t51.2885-19/s150x150/24126366_139165150008018_2795172155120156672_n.jpgsoph_ontwowheels
17892879493145000 <f0><U+009F><U+0091><U+008C><f0><U+009F><U+008F><U+00BB>1516644775 306180651 https://scontent-ort2-2.cdninstagram.com/vp/2a1d9e70f9c5472848c18ff719f76afd/5B49ABE9/t51.2885-19/s150x150/27890965_2099072973656754_7303907244821708800_n.jpgsallykoonin_123
17920219525007200 Good girl! <U+2764><f0><U+009F><U+0092><U+00AA>1516644783 31383965 https://instagram.fagr1-1.fna.fbcdn.net/vp/3230896e49952035c4a21d078561d30f/5B1DB27A/t51.2885-19/11906329_960233084022564_1448528159_a.jpgcc_the_dj
17846245039238900 ah 1516644792 1581502399 https://scontent-ort2-2.cdninstagram.com/vp/474effd3afd430c302057e586bd8a3f7/5B4AE6C8/t51.2885-19/s150x150/11856685_981735955211282_219583063_a.jpgbig_candycane_lane_madeosteel
17929182385004300 Get it. 1516644802 3016165931 https://scontent-ort2-2.cdninstagram.com/vp/3d7412d5dc34b278dc9edf938dd40c9d/5B1AB5F5/t51.2885-19/s150x150/23498420_146923939275945_5315202627879829504_n.jpgdennisgivens
17859767317219600 <f0><U+009F><U+0091><U+008F><f0><U+009F><U+0091><U+008F><f0><U+009F><U+0091><U+008F><f0><U+009F><U+0091><U+008D>1516644827 3922217374 https://scontent-ort2-2.cdninstagram.com/vp/27fa9aed991bb98fe03072086ae170ff/5B1139A4/t51.2885-19/s150x150/24846014_362064277586536_2437640544617234432_n.jpgsam.deylami4054
17919995560054000 its a timex expedition moment1516644852 1581502399 https://scontent-ort2-2.cdninstagram.com/vp/474effd3afd430c302057e586bd8a3f7/5B4AE6C8/t51.2885-19/s150x150/11856685_981735955211282_219583063_a.jpgbig_candycane_lane_madeosteel
17902404370091000 @anna.ekstromh kan inte du va såhär cool?1516644867 2222529160 https://scontent-ort2-2.cdninstagram.com/vp/9b6c447f0cb7df0c14a7f5df3fc71a8f/5B1D70E5/t51.2885-19/s150x150/27877706_204447570291007_9108200985573982208_n.jpgville360
17846218849241100 Nice 1516644876 5713619573 https://scontent-ort2-2.cdninstagram.com/vp/f40c811ddb5b8e07f28f62efd407e93e/5B28DC9C/t51.2885-19/s150x150/26319515_528530067515824_1373147541847146496_n.jpgairforce.ir
17846242351233800 That is awesome... My daughter wants to be a pilot in the Air Force.. You are such and inspiration. @usairforce1516644878 270062153 https://scontent-ort2-2.cdninstagram.com/vp/7de4cfd05e7b680cbc833706ca1b6d7f/5B1B554C/t51.2885-19/s150x150/18947843_650353381831190_2087312547752443904_a.jpgrozyp77
17892406873161800 Eyebrows on fleek 1516645059 33344140 https://scontent-ort2-2.cdninstagram.com/vp/e2796b68f027501ec5d114aec8dfb00d/5B2BD081/t51.2885-19/s150x150/27879767_295904594267297_3289743914201776128_n.jpgtreybrah_
17907310702129400 <f0><U+009F><U+0091><U+008A>1516645111 39121918 https://scontent-ort2-2.cdninstagram.com/vp/c6a059f1da8a1f0b88436c60514ae6fd/5B1E50D8/t51.2885-19/s150x150/26392794_2001319253490120_9011118206240161792_n.jpgmekala333
17896066945138500 Shaw AFB represent! <f0><U+009F><U+0087><U+00BA><f0><U+009F><U+0087><U+00B8>1516645119 34501017 https://scontent-ort2-2.cdninstagram.com/vp/3f38c8630a1c294bb62a54d39a48e810/5B4C1BDB/t51.2885-19/s150x150/26866190_499624393770957_8844942846361337856_n.jpgchrisastro
17923311298058600 SALUTE!! 1516645150 6481124 https://scontent-ort2-2.cdninstagram.com/vp/b86c08814740bdb1545218bba73af9a5/5B4A2C21/t51.2885-19/11881824_986618044721646_1650377176_a.jpgpaulagoble
17919239926045500 @idcvickyy u can be her.1516645242 415630583 https://scontent-ort2-2.cdninstagram.com/vp/118d8643ead538c26431286035861f40/5B4BFAF0/t51.2885-19/s150x150/27893291_168703120427388_2988161910655942656_n.jpgjoha_balay
17893049878150400 YEA! Viper pilots rule1516645267 3266740512 https://scontent-ort2-2.cdninstagram.com/vp/ffb4cf6739f26fd4447ffd38ae372ec7/5B11AAA9/t51.2885-19/s150x150/26272222_570047826669746_6501235328156696576_n.jpgonyxreaper
17902779208084300 <f0><U+009F><U+00A4><U+0098><f0><U+009F><U+008F><U+00BB>1516645297 210947008 https://scontent-ort2-2.cdninstagram.com/vp/ea86b958e8150aa3ae4e12fbb4aba1cf/5B2A2F79/t51.2885-19/s150x150/16585096_476771682711740_4484955204403331072_a.jpgcrazy_gpigz
17846326054239800 Ville, jag är 14<f0><U+009F><U+0098><U+0082> @ville3601516645301 857171570 https://scontent-ort2-2.cdninstagram.com/vp/72b0c949a73b5f8a74158c20e5dd8fb9/5B1766AC/t51.2885-19/s150x150/25005578_663682734019309_7577762945254293504_n.jpganna.ekstromh
17907677278112200 #goals<U+2764><U+FE0F><f0><U+009F><U+0092><U+00AF>1516645343 22737475 https://scontent-ort2-2.cdninstagram.com/vp/29278116f4d7753f08165c42beabb172/5B1613D8/t51.2885-19/s150x150/26181292_443533316063348_3256149732279451648_n.jpggiaxlana
17895607483134500 <f0><U+009F><U+0091><U+008F><f0><U+009F><U+008F><U+00BC><f0><U+009F><U+0091><U+008F><f0><U+009F><U+008F><U+00BC><f0><U+009F><U+0091><U+008F><f0><U+009F><U+008F><U+00BC><f0><U+009F><U+009A><U+0081><f0><U+009F><U+00A6><U+0085><U+26A1><U+FE0F><f0><U+009F><U+0092><U+0080><f0><U+009F><U+0087><U+00A7><f0><U+009F><U+0087><U+00B7>1516645352 4608014410 https://scontent-ort2-2.cdninstagram.com/vp/a1c7b6e6f49dd5314ac8d423363fe4a4/5B1DC428/t51.2885-19/s150x150/25026154_1862082484104283_7468195177027338240_n.jpgciopaer_to
17919464815010700 @amanduh0429 <f0><U+009F><U+0098><U+008E>1516645382 400073675 https://scontent-ort2-2.cdninstagram.com/vp/81b090a1da1636247301ecc73ee88c1d/5B0EEFD0/t51.2885-19/s150x150/22639157_180220312553059_2932803269723947008_n.jpgcacraig11
17879493088196800 <f0><U+009F><U+0098><U+008D><f0><U+009F><U+0098><U+008D><f0><U+009F><U+0098><U+008D><f0><U+009F><U+0098><U+008D><f0><U+009F><U+0098><U+008D>1516645436 4979572474 https://scontent-ort2-2.cdninstagram.com/vp/4a8b9b71c86d72d3e4b0013e5ce06e8c/5B120E93/t51.2885-19/s150x150/27891601_196679197587689_7702282456711299072_n.jpgjaymon3002
17919810205032800 <f0><U+009F><U+0098><U+008D><f0><U+009F><U+0098><U+008D><f0><U+009F><U+0098><U+008D><f0><U+009F><U+0098><U+008D><f0><U+009F><U+0098><U+008D>1516645436 4979572474 https://scontent-ort2-2.cdninstagram.com/vp/4a8b9b71c86d72d3e4b0013e5ce06e8c/5B120E93/t51.2885-19/s150x150/27891601_196679197587689_7702282456711299072_n.jpgjaymon3002
17894531890188700 Sickkkk 1516645519 1981970767 https://scontent-ort2-2.cdninstagram.com/vp/c0f6d68e43f75b28eeb8bae8465b3bcd/5B1A5341/t51.2885-19/s150x150/26227541_156133235038686_1302106944525303808_n.jpgjust.__tin___can_
17920278703051200 <U+2764><U+FE0F> my heart at Eglin <f0><U+009F><U+0087><U+00BA><f0><U+009F><U+0087><U+00B8> #USAFmom1516645582 285396048 https://scontent-ort2-2.cdninstagram.com/vp/7c705b3964588d105058a11211e14575/5B147527/t51.2885-19/s150x150/13397676_1792024331013142_2095304786_a.jpgj_yvette_l
17894926300130200 Can you get her for career day <f0><U+009F><U+0098><U+0082> @mo_elizabeth3 ?1516645687 295804343 https://scontent-ort2-2.cdninstagram.com/vp/f4e2c8eac6d31bcf283b75023d4b325c/5B10BC8C/t51.2885-19/s150x150/25008072_171693896897547_257306796304105472_n.jpglo_lights_
17902481515084500 NND PBSBHBJBLBZBXBCB Q RDGJKLCB RDGJKLCB RDGJKLCB RDGJKLCB RDGJKLCB RDGJKLCB RDGJKLCB RDGJKLCB RDGJKLCB RDGJKLCB RDGJKLCB1516645713 6330870614 https://scontent-ort2-2.cdninstagram.com/vp/1c5687ff2d0a685bd95181ef071ae865/5B048A09/t51.2885-19/s150x150/23164537_135653563756420_5548774266675134464_n.jpgnoa727
17906989870099700 Veeeeery niiiiiiiiiice<U+270C><U+270C><f0><U+009F><U+0092><U+0095><f0><U+009F><U+0092><U+0095><f0><U+009F><U+0098><U+0099><f0><U+009F><U+0098><U+0099><f0><U+009F><U+0098><U+009A><f0><U+009F><U+0098><U+009A><U+270C><U+270C><f0><U+009F><U+0092><U+009E><f0><U+009F><U+0092><U+009E><U+270C><U+270C><f0><U+009F><U+0092><U+0095><f0><U+009F><U+0092><U+0095><U+270C><U+270C>1516645731 3579379587 https://scontent-ort2-2.cdninstagram.com/vp/e00f983169e55b62e85a03b7edebe8e6/5B05688A/t51.2885-19/s150x150/27575031_171202726997569_2043483463058718720_n.jpgkoroush_imani
17879685745196500 @lo_lights_ I asked for a myriad of officers ranging from 2d lt to Capt1516645755 424550255 https://scontent-ort2-2.cdninstagram.com/vp/887e8a0f66d9425b7e723227b0ef19f2/5B04FF84/t51.2885-19/s150x150/18094555_254650541667515_2726418108192391168_a.jpgmo_elizabeth3
17920758868017800 @frankpaint 1516645776 46654786 https://scontent-ort2-2.cdninstagram.com/vp/9352930c4d18d4c8df7eb21f23538e7c/5B15859A/t51.2885-19/927393_855653307791887_527646871_a.jpglaurenpainter_
17915043367070100 @mo_elizabeth3 I just like that she's a female fighter from Shaw.1516645806 295804343 https://scontent-ort2-2.cdninstagram.com/vp/f4e2c8eac6d31bcf283b75023d4b325c/5B10BC8C/t51.2885-19/s150x150/25008072_171693896897547_257306796304105472_n.jpglo_lights_
17924786815057300 Found <f0><U+009F><U+0092><U+00A7><U+270A>1516645911 2266396868 https://scontent-ort2-2.cdninstagram.com/vp/69f1cae72e6271b6624cde188193c67a/5B1233E4/t51.2885-19/s150x150/27892042_2048175975420147_6924264451189243904_n.jpga.badhisutawan
17921940580013500 <f0><U+009F><U+0099><U+008C><f0><U+009F><U+008F><U+00BB> cool <f0><U+009F><U+0098><U+008E>1516645921 3514876709 https://scontent-ort2-2.cdninstagram.com/vp/e3fbfeb1641cfcbff13d7505b8b4aeb4/5B07B835/t51.2885-19/s150x150/21984976_1739498929416714_3158543980547801088_n.jpgfawzinelfarr
17907142621097000 Bravo Bravo Bravo <f0><U+009F><U+0098><U+0080> <f0><U+009F><U+0098><U+0080><f0><U+009F><U+0091><U+008F><f0><U+009F><U+0091><U+008F><U+270C><U+FE0F><U+270C><U+FE0F><f0><U+009F><U+0098><U+0098><f0><U+009F><U+0098><U+0098><f0><U+009F><U+008C><U+00B9><f0><U+009F><U+008C><U+00B9>1516645957 4755191572 https://scontent-ort2-2.cdninstagram.com/vp/de2d83fba16f557704ad88dccdd9e6c6/5B2AEE98/t51.2885-19/s150x150/26869666_199713793941897_8150894711356260352_n.jpgmiro.jaume
17893056922148200 Great picture , thank for your services... God bless you ..1516646062 4699730528 https://scontent-ort2-2.cdninstagram.com/vp/2de3ea6bd4ecef8b4e32c6038d7449e1/5B169D39/t51.2885-19/s150x150/28155962_2046093055672503_3645957326227111936_n.jpglmma2003
17904741163126700 You shot the @usairforce1516646228 3228699880 https://scontent-ort2-2.cdninstagram.com/vp/2643911ee8e1ddebfb46f2ee22ce264a/5B4A52F6/t51.2885-19/s150x150/27581997_1991437354512823_1111268654072201216_n.jpgian_t_wilson_
17905736824126400 Oh wait wrong picture1516646264 3228699880 https://scontent-ort2-2.cdninstagram.com/vp/2643911ee8e1ddebfb46f2ee22ce264a/5B4A52F6/t51.2885-19/s150x150/27581997_1991437354512823_1111268654072201216_n.jpgian_t_wilson_
17905311511124800 #CooL 1516646416 4285556838 https://scontent-ort2-2.cdninstagram.com/vp/6475f971cc2cdf3667ab612a380118fc/5B1E5BDF/t51.2885-19/s150x150/25021995_712730572270192_1895124379142979584_n.jpgmarlilny
17860723798209200 @jadentkorth Eglin hua1516646421 2064800284 https://scontent-ort2-2.cdninstagram.com/vp/8487ec6298d9a4bdf4433b63767b9438/5B0D9F47/t51.2885-19/s150x150/25018711_239437903262579_3841898226806423552_n.jpganthonyliz_
17920338988032700 @anthonyliz_ HUA 1516646464 246687008 https://scontent-ort2-2.cdninstagram.com/vp/a1217065a3f24d9db484713a73e19b81/5B1D98F5/t51.2885-19/s150x150/26152031_193338297912578_7166435164266954752_n.jpgjadentkorth
17893257619192700 BEAUTY AND STRENGTH! <f0><U+009F><U+0098><U+0087><f0><U+009F><U+008C><U+00B9><f0><U+009F><U+0092><U+008B><f0><U+009F><U+008E><U+00B5>1516646513 4871254374 https://scontent-ort2-2.cdninstagram.com/vp/8d2167e74606ec1265d2e4df8e0a361b/5B297DB2/t51.2885-19/s150x150/17439306_283536282059742_1794652896382418944_a.jpgjjascosmos
17920623730011200 @jamaican_johnny @walker_polsinelli @kylematern352 <f0><U+009F><U+0091><U+0080><f0><U+009F><U+0091><U+0080><f0><U+009F><U+0091><U+0080>1516646584 487458548 https://scontent-ort2-2.cdninstagram.com/vp/a7947eb9b112ae77765fc4de588978cb/5B17D374/t51.2885-19/s150x150/19761734_1414126255332842_7719441564023390208_a.jpgcyrbuiltsedan
17892821923152100 YGBSM 1516646811 31484972 https://scontent-ort2-2.cdninstagram.com/vp/082e02b38907eda2b7003c9a50cd7a73/5B06249A/t51.2885-19/s150x150/27577043_273972819803577_1611486401067483136_n.jpgisthisguyserious
17895336844131900 Beauty with the Beast #f16 my all time fav <f0><U+009F><U+009B><U+00A9><f0><U+009F><U+009B><U+00A9>1516646905 5880550922 https://scontent-ort2-2.cdninstagram.com/vp/e53f77cf21e1d98eaff8bc0b69ce56c2/5B0DAC55/t51.2885-19/s150x150/20987512_347130965723587_8001599182836072448_a.jpgsuman_ayush13
17919505357015800 Wow strong fight woman1516646962 2039822000 https://scontent-ort2-2.cdninstagram.com/vp/95e359675d5af205a6d9fec406545156/5B4E3182/t51.2885-19/s150x150/24838326_101231340667063_5819415055237644288_n.jpgahmedn22n
17918963671061900 Fire 1516646979 2039822000 https://scontent-ort2-2.cdninstagram.com/vp/95e359675d5af205a6d9fec406545156/5B4E3182/t51.2885-19/s150x150/24838326_101231340667063_5819415055237644288_n.jpgahmedn22n
17921023285040300 Too cool! @morgart_kayla1516647093 2017650474 https://scontent-ort2-2.cdninstagram.com/vp/1b1c915dada58ecd2c675f729fc1e19d/5B29F701/t51.2885-19/s150x150/24845327_135140523831942_6806056965404360704_n.jpgadrianna_morgart
17860535284208300 <f0><U+009F><U+0098><U+008D> in love !!!1516647293 191343376 https://scontent-ort2-2.cdninstagram.com/vp/a520c26468b0cb683d1f67e91e340ec1/5B1FD536/t51.2885-19/11910350_297402050430190_2026110713_a.jpgpepegrillo89
17907340705106600 @ihashubeita 1516647348 2345937556 https://scontent-ort2-2.cdninstagram.com/vp/d8ada6a718c57d9a59439dc73a3b2877/5B4BBC1D/t51.2885-19/s150x150/28151230_1729478153781410_7990125066035658752_n.jpgatilio_vurvopolos
17911435531078600 LOVE this!! 1516647427 27753143 https://scontent-ort2-2.cdninstagram.com/vp/64851f367870effc3007913a7f459a8e/5B202F43/t51.2885-19/s150x150/23347487_1707081442677369_7867464622582792192_n.jpgjacqgould
17920140301030800 A dream that i will never accomplish ...1516647437 3245473458 https://scontent-ort2-2.cdninstagram.com/vp/a9c38ec3cc104250afc258b250857b70/5B21E475/t51.2885-19/s150x150/27576504_165985677365135_2541958432584368128_n.jpgcall_me_numpt
17910967678076700 @charlie_collie 1516647440 15755357 https://scontent-ort2-2.cdninstagram.com/vp/8b37724a96371a4b90aeea9cf95bb7a5/5B04F0EB/t51.2885-19/s150x150/26265550_155658871750736_768762089006170112_n.jpgpassion4nyc
17920419412009400 Does she have Instagram? <f0><U+009F><U+0098><U+008D><f0><U+009F><U+0098><U+008D><f0><U+009F><U+0098><U+008D>1516647462 5527825192 https://scontent-ort2-2.cdninstagram.com/vp/77ddab8d0ad9e53ff61736bafb85fa75/5B11E2F4/t51.2885-19/s150x150/18723116_1696264410677425_7877078713601359872_n.jpgaraujo253
17905484125127500 Very good 1516647881 6796434290 https://scontent-ort2-2.cdninstagram.com/vp/bf1c9154fd05177b08c3a973cf4fd1bb/5B2AC2D4/t51.2885-19/s150x150/25018195_345706149227073_2352056333332643840_n.jpgaminallah.a
17889439036173600 #TeamShaw 1516647902 365607661 https://scontent-ort2-2.cdninstagram.com/vp/435c11c5ce19fdb69cf1d79c99b50ed6/5B055F3A/t51.2885-19/s150x150/14583454_679701112186705_6361606284961120256_a.jpgbeningold318
17861953780206500 @anna.ekstromh ålder är bara en siffra<f0><U+009F><U+0099><U+0084>1516648016 2222529160 https://scontent-ort2-2.cdninstagram.com/vp/9b6c447f0cb7df0c14a7f5df3fc71a8f/5B1D70E5/t51.2885-19/s150x150/27877706_204447570291007_9108200985573982208_n.jpgville360
17902468441091800 <f0><U+009F><U+0098><U+00A5><f0><U+009F><U+0092><U+00AA>1516648216 5910717791 https://scontent-ort2-2.cdninstagram.com/vp/aa780dcaeb1acedc96ad291f3a9296de/5B181D3A/t51.2885-19/s150x150/20986994_265826810572415_4917742609096507392_a.jpgshocktherapy.jn
17895479689186600 <f0><U+009F><U+0091><U+008D><f0><U+009F><U+008F><U+00BC><f0><U+009F><U+0087><U+00BA><f0><U+009F><U+0087><U+00B8>1516648412 1370345057 https://scontent-ort2-2.cdninstagram.com/vp/50ea7f8efbe5aaffd3e871d7967a27a5/5B1837AF/t51.2885-19/s150x150/11258372_117880065219772_1152994460_a.jpgjoeypr69
17907772753102700 BIUTIFUL 1516648554 2870647697 https://scontent-ort2-2.cdninstagram.com/vp/dcd3a183478cefc2d2d8dd68d43ce8cd/5B1ACB4B/t51.2885-19/s150x150/23735234_1946590205607281_4776352826878066688_n.jpgshahin_e_aseman
17893612492192200 @jonathan_viv123 I can't wait to see you paired up with someone like this. <f0><U+009F><U+0098><U+0087><f0><U+009F><U+00A4><U+0097><f0><U+009F><U+0098><U+0098><f0><U+009F><U+0087><U+00BA><f0><U+009F><U+0087><U+00B8><f0><U+009F><U+0087><U+00BA><f0><U+009F><U+0087><U+00B8>1516648563 1184245960 https://scontent-ort2-2.cdninstagram.com/vp/14fa7c884c0014626856e74f1e665da7/5B0C1754/t51.2885-19/s150x150/13151013_891647477630324_381289661_a.jpgjmonje14
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TITLE AND SUBTITLE
An Open Source Approach to Social Media Data Gathering
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6. AUTHOR(S)
Kallhoff, Anthony J., Second Lieutenant, USAF
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Air Force Institute of Technology
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14. ABSTRACT
Modern usage of social media affords the military intelligence and analytic communities novel approaches to gather information. However, the tools and resources to develop these methodologies are still maturing. Furthermore, current data acquisition tools are not available to the DoD for all social media platforms. This thesis addresses a small subset of this problem by developing an open source methodological approach to collect and manage data from a popular social media site that has previously been inaccessible to defense intelligence organizations. This approach was operationalized via the R package called instaExtract, and an exemplar analysis was performed to demonstrate its application and efficiency for intelligence gathering.
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Data Mining, Social Media, Instagram, Data Acquisition, Data Tidying, Open-Source, R, GitHub,
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