web content analysis rachel gibson

8/2/2019 Web Content Analysis Rachel Gibson

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Workshop in Advanced Techniques for PoliticalWorkshop in Advanced Techniques for Political

Communication Research: Web Content Analysis SessionCommunication Research: Web Content Analysis Session

Professor Rachel Gibson, University of ManchesterProfessor Rachel Gibson, University of Manchester


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Aims of the SessionAims of the Session

Defining and understanding the Web as on object of study &challenges to analysing web content

Defining the shift from Web 1.0 to Web 2.0 or social media

pproac es o ana ys ng we con en , par cu ar y n re a on oelection campaign research and party homepages.

Beyond websites? Examples of how web 2.0 campaigns are being

studied key research questions and methodologies.


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The Web as an object of studyThe Web as an object of study

Inter-linkage

Non-linearity Interactivity

Multi-media

Global reach Ephemerality

Mitra, A and Cohen, E. Analyzing the Web: Directions and Challenges in Jones, S.(ed) Doing Internet Research1999 Sage


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From Web 1.0 to Web 2.0From Web 1.0 to Web 2.0

Tim Berners-Lee

Web 1.0 was all about connecting people. It was an interactive space, and I think Web 2.0 is ofcourse a piece of jargon, nobody even knows what it means. If Web 2.0 for you is blogs and

wikis, then that is people to people. But that was what the Web was supposed to be all

along. And in fact you know, this Web 2.0, it means using the standards which have been

produced by all these people working on Web 1.0,

(from Anderson, 2007: 5)

Web 1.0

The read only web

Web 2.0 What is Web 2.0 Tim OReilly (2005)

The read/write web


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Web 2.0Web 2.0

Technological definition

Web as platform, supplanting desktop pc

Inter-operability , through browser access a wide range of programmes that fulfil a variety of online

tasks i.e. manage a home page, communicate with friends, share/publish pictures, receive news.

Social understanding

Web 2.0 is based around social networking activities relies on and built through social orparticipatory software.

Users create, distribute, value-add to online content through blogs, facebook profiles, online video,

wikis, tagging tools. Produsers Bruns (2007) or Prosumers distinction between producers and

users/consumers of content disappears.

Hallmark of these applications is the way in which they devolve creative and classificatory power toordinary users.


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Web 1.0 & Web 2.0 applicationsWeb 1.0 & Web 2.0 applications social mediasocial media

Web 1.0

Websites Email

Discussion forums/chat room/IM

Web 2.0 or social media Blogs

Social Network Sites (Facebook)

Online Video sharing/posting (YouTube)

Online Photo sharing/posting (Flickr)

Microblogging sites (Twitter) @, #, RT

Online Link sharing/posting (Del.icio.us, Digg)


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Methodologies used for analysing online campaignsMethodologies used for analysing online campaigns

(supply(supply--side)side)

Adapted from offline - `digitized methods` (Rodgers, 2010) Web 1.0

Online interviews, elite surveys Online discourse analysis

Online content/feature analysis Classic or offline approaches

Online specific - digital methods Web 2.0

Hyperlinking and webspheres Voson, Issue Crawler, SocScibotOnline, NodeXL,

Blog analysis toolkits and search engines Technorati . BAT (Blog Analysis Toolkit)

Twitter and facebook scrapers - Infoscape Lab, 140kit.com, Twapperkeeper, Discovertext

YouTube scrapers -Tubemogul, Tubekit

Wikipedia Wikiscanner


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Methodologies for analysing online campaignsMethodologies for analysing online campaigns(demand side)(demand side)

User Focused (demand) Focus groups: Price and Capella (2001); Stromer-Galley and Foot (2002)

Surveys: Bimber and Davis (2003); Gibson & McAllister (2007)

Experiments: Iyengar (2002); Lupia & Philpott (2005)

User statistics: Hitwise, Sitemeter, Alexa, Google Analytics

Computer tracking devices: Phorm

Online SNA software: NodeXL,


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Discourse analysis to study web contentDiscourse analysis to study web content

Focus of the analysis is on web as socio-cultural texts.

Described and explored from a qualitative perspective to uncover underlyingmeaning and significance.

Examples of studies: ar am owar a en ne

Benoit and Benoit (2000) The Virtual Campaign: Presidential Primary Websites in Campaign 2000American Communication Journal

Warnick (1998) Appearance or Reality? Political Parody on the Web in Campaign 96 Critical Studies inMass Communication


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Web specific content/feature analysisWeb specific content/feature analysis

Focus is on the web page/site as unit of analysis

Structural-Functional & Quantitative - systematic set of criteria developed andapplied to sites to yield largely quantitative measures of content, functionality,usability, and design features.

Quantitative-automated - Bauer and Scharl (2000) -

Web 1.0 Web 1.5? Action Centers

Heuristic Evaluation - Nielsen & Molich (1990) Heuristic evaluation of userinterfaces; Collings and Pearce (2002); Yates (2006).


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Bauer and Scharl (2000)

Internet Research


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Quantitative semiQuantitative semi--automatedautomated

1. Web 1.0 static/fixed homepages

- Parties and campaigns sites Gibson and Ward (2000; 2002) Foot & Schneider (2002)

- E-government field see Baker (2009) Pina et al. (2009) Panopoulou et al (2008) West(2007) Henriksson et a. (2006) Holzer and Kim (2005); Garcia et al (2005)http://www.insidepolitics.org/egovt06us.pdf

2. Web 1.5? - Updated web 1.0 schemes to incorporate web 2.0 elements.- ,

NSM - Stein, 2009

3. Action Centers - MyBO, Membersnet, MyConservatives, LibDemACT (Gibson, 2010;Lilleker and Jackson, 2010)


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Gibson & Ward (2000)

SSCORE


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Gibson & Ward (2002) AJPS


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Gibson & Ward (2002) AJPS


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Stein, L.. 2009 NM&S


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Gibson, R. 2010 APSA


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HyperlinkingHyperlinking andand websphereswebspheres

Focus is on inter-relational/textual element of websites and the wider context. Unit

of analysis is the web network rather than individual sites.

Thematic set of websites are identified, captured/archived over time and the

linkage patterns explored as well as content.

e o p oneere y c ne er an oo e p ere ana ys s .

Define a Web Sphere as a hyperlinked set of dynamically defined digital resources

spanning multiple Web sites and deemed relevant to related to a central theme or

object. The borders of the sphere are defined relationship to central theme/object and

a given time period.

See Library of Congress, Internet Archive http://www.archive.org/index.php &WebArchivist.org http://webarchivist.org/


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Hyperlink analysisHyperlink analysisMore sophisticated/automated methods have developed for academic research since

around 2003.

Adaptations of search engine technology most simple form of online link analysis. Hindman et al. (2003) Googlearchy pioneered the term googlearchy referred to a

power law .operating in regard to the prominence of sites.

Adamic and Glance (2005) examined linkage between left and right-wing blogs in the. . www. u . .

Ackland and Gibson (2007) comparative analysis of political party systems linkageusing VOSON http://anu.voson.edu.au crawler to examine the out and inboundlinkages among political parties in 6 different democracies. Hyperlinks = networkedcommunication inclusivity, identity, opponent dismissal, force multiplication. Test vianos to links to other parties, target of links by tld, inter-linkage within party groups.

For tools see http://www.issuecrawler.net ; http://www.touchgraph.comhttp://socscibot.wlv.ac.uk/


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InterInter--linkage between Parties by ideological familylinkage between Parties by ideological family

Table 4: Inter-linkage between political parties by party type(1)

Far Left

(2)

Left

(3)

Centre

(4)

Right

(5)

Far Right

(6)

Ecol.

(7)

Reg.

(8)

Total no. of parties linking to

other parties

(9)

Total no.

of linksmade to

other

arties

(10)

% of links

within partyfamily

Ecologist 0 0 2 1 0 6 0 6 14 79

Far Left 11 7 0 2 1 3 1 12 71 46

Left 3 4 3 2 0 2 0 2 22 50Centre 0 4 2 2 0 1 0 5 12 25

Right 1 3 1 8 1 2 1 9 20 55

Far Right 0 0 0 1 2 0 0 3 7 71Regionalist 0 0 0 0 0 0 1 1 1 100

39 147Note: The numbers in columns 1-7 are counts of parties (e.g. 11 far left parties linked to other far left parties). Note that the

same party can appear in more than once in a given row in columns 1-7, and this is why column 8 (showing the total numberof parties that have linked to another seed site) is not equal to the sum of the preceding columns.


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Beyond websites?Beyond websites?

Blog analysis key studies

Types, Content, Structure

Sweetster-Trammel, Kaye D. 2007. Candidate campaign Blogs: Directly Reaching Out to the Youth Vote.The American Behavioral Scientist. 50(9): 1255-1264.

Xifra, J. and A. Huerta2008. s Blogging PR: An Exploratory Analysis of Public Relations Public Relations

Review 34:265-275.

Herring, S. et al. Weblogs as a bridging genre Information Technology and People 18(2): 2005: 142-171.

Influence

Karpf, D. 2008. Measuring Influence in the Political Blogosphere Institute Politics and Technology Review,

Institute for Politics, Democracy and the Internet. :33-41

http://www.the4dgroup.com/BAI/articles/PoliTechArticle.pdf

Etling, B. et al. 2010. Mapping the Arabic blogosphere: politics and dissent online 12: 1225-1243.

Elmer, G.; Ryan, P.M.; Devereaux, Z.; Langlois, G.; Redden, J. and McKelvey, F. 2007. Election Bloggers:

Methods for Determining Political Influence. First Monday 12(4).

Drezner and Farrell. 2008. The Power and Politics of Blogs Public Choice (134): 15-30


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Twitter analysis

Boyd, D. et al. 2010 Tweet Tweet Retweet Working paper.http://www.danah.org/papers/TweetTweetRetweet.pdf

Tumasjan et al. 2010. Predicting Elections with Twitter Association

.

Computer Reviewhttp://ssc.sagepub.com/content/early/2010/09/24/089443931038655

7.full.pdf+html

Zuckerman, E. Studying Twitter and the Moldovan Protestswww.Ethanzuckermann.com/blog/


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YouTube analysis Shah, C. 2010. Supporting Research Data Collection from YouTube with Tubekit Journal of

Information Technology & Politics 7(2&3): 226-240

Wallsten, K. 2010. Yes We Can: How Online Viewership, Blog Discussion, Campaign

, .

Journal of Information Technology & Politics 7(2&3): 163-181

Zink, M. Suh, K and J. Kurose. 2009. Characteristic of YouTube network traffic at a campus

network, Computer Networks 53: 501-514

Tian, Y. 2010. Organ Donation on Web 2.0: Content and Audience Analysis of Organ

Donation Videos on YouTube Health Communication 25: 238-246.


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Online resources and tools for collection andOnline resources and tools for collection and

analysing web 2.0 content.analysing web 2.0 content.

Digital Tool Kits

Digital Methods Initiative - https://wiki.digitalmethods.net/Dmi/ToolDatabase Richard Rodgers,

Natively Digital: The Link | The URL | The Tag | The Domain | The PageRank | The Robots.txt Device Centric: Google | Google Images |Google News | Google Blog Search | Yahoo | YouTube | Del.icio.us | Technorati | Wikipedia | Alexa | IssueCrawler | Twitter | Facebook

Infoscape Research Lab Tools www.infoscapelab.ca Greg Elmer

- Blog Aggregator measures activity levels over time and hyperlinks cited in blog posts- , , , , ,

- Facebook Group scraper - title, type, number and members of group

Training / Research

Exploring Online Research Methods Website: http://www.geog.le.ac.uk/ORM/site/home.htm

Digital Methods Initiative Training Course https://www.digitalmethods.net/Digitalmethods/WebHome

http://nms.sagepub.com/

http://jcmc.indiana.edu/ http://www.connectedaction.net/

http://www.methodspace.com/group/sageresearchmethodsonline


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Concluding commentsConcluding comments There are a growing number of options open to researchers seeking to capture and analyze

campaigns and their influence on voters.

Approaches have evolved as Web itself has. From adaptation traditional methods suitableto analysis of static point to mass old media to newer web specific tools that allow forcapture and analysis of more dynamic many to many medium.

Network analysis tools increasingly used reflecting a shift in the understanding andpractice of the Web as a dynamic social context - conversational - rather than a fixedinfrastructure.

- -controlled (although MSM still dominant in news).

This has some significant implications for content analysis:

1. Linkage of the context to the content. To interpret meaning of web content twitter feed, facebookprofiles, locate it in the wider and ongoing stream of dialogue.

2. . Locating content inter-operability of the various platforms means that content is shared acrossmultiple sites. So need to follow the content.

3. Volume of content increases - capturing and archiving issues4. Quality of content changes . A new language or terminology emerging content = actions. Posts,

embeds, widgets, tweets, email, views, followers, befriending or liking a politician/party . A newsoftware language has grown up - @, #, RT, http. Automating/manual coding.


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Short demo of a digital methodsShort demo of a digital methods

Getting started with NodeXL.

Manual: Hansen et al.Analyzing Social Media Networks with NodeXL

Not focused on web content per se but structure or influence of that content. So ex.

compare politicians twitter networks, or a trending topic import by #.

Example: Fake FB network but you can import pajek, ucinet datasets or network data

rom tw tter, youtu e, ema sts

Vertices = nodes or agents and edges or ties between nodes.

Edges are relationships between them - undirected or directed

Unweighted edge is simplest whether relationship exists. Weighted adds

information (strength, frequency of the tie) have darker/thicker lines.

NodeXL works through setting up an edge list which represents a network. This

network can then be represented in a graphical form. Through this you can

calculate various measures of centrality of individuals or nodes in the network.

Group function can group types of individuals and calculate a collective weight.

web content analysis rachel gibson

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