user behavior in microblogs with a cultural emphasis
TRANSCRIPT
User Behavior in Microblogs with a Cultural Emphasis
Ruth García-Gavilanes
Advisor : Ricardo Baeza-YatesWeb Research Group
Universitat Pompeu Fabra& Yahoo Labs
PhD Thesis Defense February 26, 2015
2
• The study of the trails left behind by users when they use the web: Interactions, choices, searches, purchases, etc.
• User interactions are increasingly mediated and shaped by algorithms and computational methods.
• Massive amount of data
• Great cultural value
User Behavior
• Rise of Computational Social Sciences
Introduction
CSS AGENDA
PRESENT
FUTURE
Develop new instruments to tap into the potential of found data and crowds ‘: building a telescope for the Social Sciences
Online impacts offline! Build new algorithms and tools to shift the current configurations of societies towards better futures.
3Introduction
Claudia Wagner
Social Science Research
Computer Science and Information
Science
Survey Design and Methodology
Objective of CSS
4
Computational Social Science
Introduction
5
User behavior
Microblogs Culture• All users: human
recommendations, behavior evolution
• Cross-country comparisons
Cultural Emphasis
Introduction
6
Friendship links in Twitter do not need to be reciprocal
I follow you
information
Case Study : Twitter
Introduction
#HASHTAGS
@Mentions & Retweets
7
#Disasters Teheran, Iran
Introduction
8Introduction
Background
Kwak et al. What is Twitter, a Social Network or a News Media? WWW’10
Cha et al. Measuring User Influence in Twitter: The Million Follower Fallacy. ICWSM’10
Bakshy et al. Everyone’s an Influencer: Quantifying influence on Twitter. WSDM’11
De Choudhury et al. How Does the Data Sampling Strategy Impact the Discovery of Information Difussion in Social Media? ICWSM’10.
9Introduction
Goals
• Study the effect of recommendations made by users
• Compare the evolution of user behavior through time
• Find differences and similarities across countries
• Study how cultural models can be used with data
• Use cultural models socio economic indicators to study user behavior
In Microblogs :
Contributions• Providing a study of human generated recommendation on Twitter and its effect.
o García-Gavilanes et al. Follow My Friends This Friday! An Analysis of Human-generated Friendship Recommendations. SocInfo’13 [Best paper award]
• Describing the evolution of user behavior over time regarding the content they generate.o García-Gavilanes et al. Who are my Audiences? A Study of the Evolution of Target Audiences in
Microblogs. SocInfo’14
• Describing differences and similarities of users across countries regarding the way people tweet and connect with others. o García-Gavilanes et al. Microblogging without Borders: Differences and Similarities. Websci’11. o w/ Poblete et al. Do All Birds Tweet the Same? Characterizing Twitter Around the World. In CIKM’11
• Proposing how to combine anthropological studies of culture with large scale data. • Correlating how and when people tweet with dimensions of national culture and pace
of life o García-Gavilanes et al. Cultural Dimensions in Twitter: Time, Individualism and Power. ICWSM’13
[Honorable mention]
• Improving the prediction of the communication strength between users from different countries by taking into account several cultural and socio-economic indicators taken from diverse sources. o García-Gavilanes et al. Twitter ain’t Without Frontiers: Economic, Social, and Cultural Boundaries in
International Communication. CSCW’14.
10Introduction
11
Data Mining Cultural
All users
Q1) What is the effect on users from
Human generated recommendations?
Q2) How do user behavior evolve over time?
Cross-countryQ3) Do all users from
different countries tweet the same?
Q5) Does culture influences the way we
tweet online?
Q6) Can culture influence online interactions with
users from other nations?
Thesis Structure
Q4) What cultural models to use?
Introduction
12
Data Mining Cultural
All users
Q1) What is the effect on users from
Human generated recommendations?
Q2) How do user behavior
evolve over time?
Cross-countryQ3) Do all users from
different countries tweet the same?
Q5) Does culture influences the way we
tweet online?
Q6) Can culture influence online interactions with
users from other nations?
Thesis Structure
Q4) What cultural models to use?
Introduction
Q1) Human Recommendations
Recommendations 13
[Garcia-Gavilanes et al. Follow My Friends This Friday!, SocInfo’13]
Friendship Recommendations
• Self organized• Trendy• Measurable
Track recommendations during 24 weeks
Q1) Acceptance
Recommendations 14
TotalRecommendation Instances 59,055,205
Accepted Recommendation Instances
354,687
Social link recommendations made by current friends have a measurable effect on
link formation
0.60% instance acceptance
Receiver Recommender Recommendation Week
[Garcia-Gavilanes et al. Follow My Friends This Friday!, SocInfo’13]
4M users
Recommendations 15
Follow Friday recommendations outperform the two alternativeconditions.
Q1) Acceptance
The accepted recommendations have more longevity than other links.
[Garcia-Gavilanes et al. Follow My Friends This Friday!, SocInfo’13]
Q1) Results
Recommendations 16
Features MAPAll 0.496User-based 0.074Relation-based 0.398Recommendation-based 0.062User + Relation 0.518User + Format 0.079Relation + Format 0.379
USER-BASED (per user)• Attention• Activity
RELATION-BASED (per pair)• Tie Strength• Similarity
RECOMMENDATION-BASED (per recommendation)• Repetitions• Format
The link formation is influenced mostly by the user and relation-based characteristics
Rotation Forest
140features
[Garcia-Gavilanes et al. Follow My Friends This Friday!, SocInfo’13]
17
Data Mining Cultural
All users
Q1) What is the effect on users from
Human generated recommendations?
Q2) How do user behavior evolve over time?
Q4) What cultural models to use?
Cross-countryQ3) Do all users from
different countries tweet the same?
Q4) Does culture influences the way we tweet online?
Q5) Can culture influence online interactions with users from other
nations?
Thesis Structure
Evolution
Active in 2011 & 2013
2011 2013
Users 1,315,313 1,125,968
English Tweets
406,719,999 256,330,241
Min 1 and max 22 tweet per working day.
8M
4.3M
770K
1.1M2011
2013
2011 2013
Q2) DATA
18
Inactive < 1 tweet per dayHyperactive > 22 per day
530K 570K
1.3M
[García-Gavilanes et al. Evolution of Target Audiences. SocInfo’14]
Evolution
Q2)Tweeting Behavior
19
No Mentions
Tweets
With links
Original tweets (OT)
Without links
Mentions
Re-tweets (RT)
No Mentions
With links Without links
Mentions
% % % % % % 2011
% % % % % % 2013
Evolution
[García-Gavilanes et al. Evolution of Target Audiences. SocInfo’14]
Q2) Clusters
20
0%
25%
50%
75%
100%
0%
25%
50%
75%
100%
0%
25%
50%
75%
100%
0%
25%
50%
75%
100%
0%
25%
50%
75%
100%
Type of tweet OT with links and mentionsOT with links and no mentions
OT with mentions and no linksOT without links or mentions
RT with linksRT without links
Endogenous Conversationalists Generalists
Echoers Link Feeders
Evolution
[García-Gavilanes et al. Evolution of Target Audiences. SocInfo’14]
Q2) Users 2011 vs 2013
21
Majority of users remain in the same cluster except the echoers’ group.Increase in Generalists and Link Feeders.Mature users tend to use Twitter more as news media.
[García-Gavilanes et al. Evolution of Target Audiences. SocInfo’14]
22
Data Mining Cultural
All users
Q1) What is the effect on users from
Human generated recommendations?
Q2) How do user behavior evolve over time?
Q4) What cultural models to use?
Cross-countryQ3) Do all users from
different countries tweet the same?
Q4) Does culture influences the way we
tweet online?
Q5) Can culture influence online interactions with
users from other nations?
Thesis Structure
Cross-country
Q3) Cross-country comparison
• Data: analysis of one year of Tweets for 10 most active countries
• Content: languages, sentiment, structure (retweets, hashtags,..)
• Structure: network (modularity, average path length, reciprocity, connectivity)
23Cross-country
Q3) Activity and Engagement
24
[Garcia-Gavilanes et al. Microblogging without Borders: Differences and Similarities, WebSci’11]
Cross-country
12M active users6M with valid location4M user from 10 most active countries.5B tweets during 2010
.
25
Countries with more users notnecessarily the most engaged
Cross-country
[Garcia-Gavilanes et al. Microblogging without Borders: Differences and Similarities, WebSci’11]
Q3) Activity and Engagement
English
Portuguese
Japanese
Spanish
Bahasa−Indonesia
Bahasa−Malay
Korean
Dutch
German
Italian
Arabic
Users
50M 100M 200M 500M 1000M 2000M
Q3) Languages & Sentiment
26
Netherlands >10%,Indonesia >10%,Mexico >10%,South Korea >10%
English is the most common languageMore than 10% in non-english speaking countries
Non-western countries seem to be morePositive
Based in Dodds et al., 2011
Cross-country
[Garcia-Gavilanes et al. Microblogging without Borders: Differences and Similarities, WebSci’11]
Tweet function
27
Country URL (%) Hashtag (%) Mention (%) Retweet (%)Indonesia 14.95 7.63 58.24 9.71Japan 16.30 6.81 39.14 5.65
Brazil 19.23 13.41 45.57 12.80
Netherlands 24.40 18.24 42.33 9.12
UK 27.11 13.03 45.61 11.65
US 32.64 14.32 40.03 11.78
Australia 31.37 14.89 43.27 11.73
Mexico 17.49 12.38 49.79 12.61
South Korea 19.67 5.83 58.02 9.02
Canada 31.09 14.68 42.50 12.50
Some Asian countries seem to chat more (except Japan), use less URLs, hashtags.Asian countries seemed to retweet less.
Cross-country
[Garcia-Gavilanes et al. Microblogging without Borders: Differences and Similarities, WebSci’11]
Q3) Network
28
Country Reciprocity Avg. Clust. Coef
Modularity
Indonesia 0.27 0.06 0.54
Japan 0.32 0.06 0.46
Brazil 0.13 0.07 0.46
Netherlands 0.22 0.10 0.41
UK 0.17 0.10 0.39
US 0.19 0.07 0.42
Australia 0.24 0.10 0.45
Mexico 0.17 0.08 0.36
South Korea 0.28 0.09 0.31
Canada 0.26 0.10 0.56
0 5
10 15 20 25 30 35 40 45
BrazilUK MexicoUSA
NetherlandsAustraliaCanadaIndonesiaSouth_KoreaJapan
Countries
DiameterAvg. Path Length
Reciprocity seems to be significant specially for Asian countries High clustering coefficient and less reciprocity may indicate hierarchical links
Indonesia has highest diameter, which agrees with the modularity coefficient.
[w/ Poblete et al. Do all Birds Tweet the Same?, CIKM, 2011]
Cross-country
Q3) Connectivity
29Cross-country
[w/ Poblete et al. Do all Birds Tweet the Same?, CIKM, 2011]
Q3) Connectivity
30
More self Connected
Cross-country
[w/ Poblete et al. Do all Birds Tweet the Same?, CIKM, 2011]
Q3) Connectivity
31
More connected
Cross-country
[w/ Poblete et al. Do all Birds Tweet the Same?, CIKM, 2011]
• Need cultural models to understand differences across countries in
Microblogs
32Cross-country
33
` Data Mining Cultural
All users
Q1) What is the effect on users from
Human generated recommendations?
Q2) How do user behavior evolve over time?
Q4) What cultural models to use?
Cross-countryQ3) Do all users from
different countries tweet the same?
Q4) Does culture influences the way we tweet online?
Q5) Can culture influence online interactions with
users from other nations?
Thesis Structure
Cultural Models
Culture
34Cultural Models
WHAT IS CULTURE ?
35Cultural Models
CULTURE
Software of the mind that distinguishes members of one
group or category of people from others
36Cultural Models
37Cultural Models
MEASURE CULTURE• Geert Hofstede: Cultural dimensions
o Different cultural dimensions : Individualism, Power Distance and others.
• Robert Levine: Pace of Life (Geography of time)o Different perception of time
• Edward T. Hall: Monochronic vs Polychronic o Different ways of executing tasks
• Samuel Huntington: Clash of Civilizationso Politics of identity replacing politics of interest.
38Cultural Models
Pace of Life
IndividualismPower Distance
39
Levine
Hall
Cultural Models
Can such differences also be captured from online interactions?
40Cultural Models
41
Data Mining Cultural
All users
Q1) What is the effect on users from
Human generated recommendations?
Q2) How do user behavior evolve over time?
Q4) What cultural models to use?
Cross-countryQ3) Do all users from
different countries tweet the same?
Q5) Does culture influences the way we
tweet online?
Q6) Can culture influence online interactions with
users from other nations?
Thesis Structure
Culture
Q5) Culture in Tweeting Behavior
• Pace of Life o Predictability (tweets, mentions)o Measure entropy of posting tweets in working hours
• Individualism vs. Collectivism o Users interacting with others (mentions)
• Power Distance : Popularity o Follow, recommend and accept recommendationpreferentially from more popular users(in-degree imbalance).
42
[Garcia-Gavilanes et al. Cultural Dimensions in Twitter, ICWSM, 2013]
Culture
43
Tweets Correlation1. Pace of life1.1 The higher the pace of life the less fraction of users will tweet during working hours1.2 The higher the pace of life, the more predictability
1.1 Users **-0.581.2 Mentions **0.681.2 Tweets **0.62
2. Individualism2.1 User chat less with others in more individualistic countries
2.1 Conversation ***−0.55
3. Power Distance3.1 Users prefer to follow and 3.2 recommend more popular users than themselves in countries with a higher power distance
Users followees **0.62Users and recommended users
**0.56
p ≤ 0.005 (***), 0.005 < p ≤ 0.05 (**), and 0.05 < p ≤ 0.1 (*)
Q5) Correlations
Culture
[Garcia-Gavilanes et al. Cultural Dimensions in Twitter, ICWSM, 2013]
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IndonesiaVenezuela
MexicoJapanBrazilColombia
ChileSouth Korea ArgentinaPhilippines
Malaysia Spain NetherlandsTurkey UKSouth AfricaSingapore Ireland Canada
FranceBelgiumSweden
AustraliaUnited States
NorwayNew Zealand
Italy
Russia India
Germany
80
85
90
95
100
0 25 50 75
Individualism Index
Fracti
on of
Enga
geme
nt
Introduction 44
Q5) Individualism
[Hong et al.. “Languagematters in twitter: A large scale study” ICWSM 11]
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Indonesia
Venezuela
Norway
MalaysiaSingapore
Chile Mexico PhilippinesColombia
United StatesSouth Korea IndiaBrazilCanada ArgentinaAustralia
RussiaItalyNew Zealand SpainGermanyJapan FranceSouth AfricaUKIreland TurkeyNetherlands BelgiumSweden
−1000
0
1000
2000
3000
4000
5000
30 60 90
Power Distance Index
In−de
gree I
mbala
nce
Introduction 45
27% of all blog trends are about artists and celebrities [Silang et al, 2011]
Q5) Power
Q5) Why is this important?
46
Indicator Pace of Time: Predictibility
Individualism: Mentions
Power Distance
ImbalanceMentions Users (%)GDP per capita ***0.55 **-0.57 **-0.41 **-0.48
Education ***0.58 **-0.51 -0.24 ***-0.60Inequality ***-0.53 **0.49 *0.39 ***0.58
In almost all cases, the findings are are also correlated with GDP per capita, education and inequality
Culture
[Garcia-Gavilanes et al. Cultural Dimensions in Twitter, ICWSM, 2013]
p ≤ 0.005 (***), 0.005 < p ≤ 0.05 (**), and 0.05 < p ≤ 0.1 (*)
47
Data Mining Cultural
All users
Q1) What is the effect on users from
Human generated recommendations?
Q4) What cultural models to use?
Q2) How do user behavior evolve over time?
Cross-countryQ3) Do all users from
different countries tweet the same?
Q5) Does culture influences the way we tweet online?
Q6) Can culture influence online interactions with users from other
nations?
Thesis Structure
Communication
48Cultural Models
http://commons.wikimedia.org/wiki/File:Clash_of_Civilizations_mapn2.png
5K country – country pairs
interactions
see you next time @pedro
@John @pedro
49
10 weeks
Q6) Country-country Interactions [ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW 2014]
Communication
111 countries
3B Geolocated Tweets
Example:
13M Geolocated users
5K country – country pairsinteractions
50
10 weeks
Q6)Social, economic and cult. features
Communication
Distance
[ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW 2014]
51
Q6) Top 1000 strongest edges
Using the gravity model the network is largely clustered according to their geography
Communication
Asia
Latin America
Middle East
The West
Edges: gravity modelForce-directed algorithm
[ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW 2014]
Edges: Unique MentionsForce-directed algorithm
52
Q6) Top 1000 strongest edges
Communication
[ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW 2014]
Unique Mentions
53
Q6) Top 1000 strongest edges
Communication
[ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW 2014]
Introduction 54
Argentina
Australia
Brazil Canada
Chile
Colombia
Dominican Republic
France
Germany
India
Indonesia
Ireland
Italy
Japan
Malaysia
Mexico
Netherlands
New Zealand Nigeria
Philippines
Puerto Rico
Singapore
South Africa
South Korea
SpainSweden
United Kingdom
United States
Venezuela
Q6) Top 50 strongest edges[ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW, 2014]
5K country – country pairsinteractions
481 country – country pairs with social, economic and
cultural features
55
10 weeks
Q6)Social, economic and cult. features
Communication
Distance +Economics + Social +Cultural
[ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW 2014]
0.45%0.55% 0.57%
0.68%0.80%
0.00#0.10#0.20#0.30#0.40#0.50#0.60#0.70#0.80#0.90#
Gravita/onal%Model%
+Economics% +Social% +Cultural% +%Interac/ons%
r2Adjusted
56
Q5) Features
Higher accuracy at high communication volumes with worse performance as the communication decreases.
The combination of features improves the prediction
Communication
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0.0 0.2 0.4 0.6 0.8 1.0
0.00.2
0.40.6
0.81.0
communication volume
mode
l's pre
dictio
nM
odel
’s p
redi
ctio
ns
Communication Volume
[ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW 2014]
0.45%0.55% 0.57%
0.68%0.80%
0.00#0.10#0.20#0.30#0.40#0.50#0.60#0.70#0.80#0.90#
Gravita/onal%Model%
+Economics% +Social% +Cultural% +%Interac/ons%
r2
r2Adjusted
57
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0.1 0.2 0.5 1.0
0.10.2
0.51.0
communication volume
model
's pred
iction
Q5) Features
Higher accuracy at high communication volumes with worse performance as the communication decreases.
The combination of features improves the prediction
CommunicationCommunication Volume
Mod
el’s
pre
dict
ions
Communication Volume
[ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW 2014]
Log-scale
Predictor P-value
Trade 6.34 ***
Cultural Dimension 3.91 ***
Gravity Model x Exports 3.78 **Gravity Model 2.79 ***
Language 2.70 .
β(%)Culture
Distance
Economic
Social
Communication 58
Q6)Features
59
Data Mining Cultural
All users
Q1) What is the effect on users from
Human generated recommendations?
Q2) How do user behavior evolve over time?
Q4) What cultural models to use?
Cross-countryQ3) Do all users from
different countries tweet the same?
Q5) Does culture influences the way we
tweet online?
Q6) Can culture influence online interactions with
users from other nations?
Thesis Structure
Communication
Conclusions & Future Work
Conlusions 60
Human recommendations
Evolution of behavior
• Recommendations by users have a measurable effect on link formation
• Adoption of microblogs as a news media rather than as a social network
• Replicate studies in other platforms• Cross-cultural recommendation• Self-organized trends and monetary
consequences• Cross-cultural evolution
next
Conlusions 61
Cross-country comparison
Tweeting behavior
Communication
• The collective behavior differ in certain characteristics: chatting engagement, reciprocity, modularity, communities.
• National culture determine the temporal patterns with which Twitter users post, or the extent to which they mention, follow, recommend and befriend others.
• In addition to distance, socio-economic and cultural features also impact international communication.
Conclusions & Future Work
next
• Application to improve communication across- cultures like machine translation (already existent: WeChat)
• China and the rest of the world: two online worlds that will meet
The End 63
Acknowledgements: Ricardo Baeza-Yates, Daniele Quercia, Yelena MejovaNeil O’Hare, Luca Maria Aiello, Alejandro Jaimes, Barbara Poblete,Marcelo Mendoza, Andreas Kaltenbrunner, Diego Sa ́ez-Trumper, Pablo Aragón, David Laniado, Ilaria Bordino, Sara Haijan, Amin Mantrach .
Acknowledgements
Publications• Ruth García-Gavilanes, Barbara Poblete, Marcelo Mendoza, Alejandro Jaimes.
Microblogging without Borders: Differences and Similarities. In The 3rd International Conference on Information and Knowledge Management (Websci), ACM, 2011.
• Barbara Poblete, Ruth García-Gavilanes, Marcelo Mendoza, Alejandro Jaimes. Do All Birds Tweet the Same? Characterizing Twitter Around the World. In The 20th International Conference on Information and Knowledge Management (CIKM), ACM, 2011
• Ruth García-Gavilanes, Neil O’Hare, Luca Maria Aiello, Alejandro Jaimes. Follow My Friends This Friday! An Analysis of Human- generated Friendship Recommendations. In The 5th International Conference on Social Informatics (SocInfo), Springer 2013. [Best paper award]
• Ruth García-Gavilanes, Andreas Kaltenbrunner, Diego Sa ́ez-Trumper, Ricardo Baeza-Yates, Pablo Arago ̀n and David Laniado. Who are my Audiences? A Study of the Evolution of Target Audiences in Microblogs. In The 6th International Conference on Social Informatics (SocInfo), Springer 2014.
• Ruth García-Gavilanes, Daniele Quercia, Alejandro Jaimes. Cultural Dimensions in Twitter: Time, Individualism and Power. In The 7th International AAAI Conference on WebLogs and Social Media (ICWSM), 2013. [Honorable mention]
• Ruth García-Gavilanes, Yelena Mejova, Daniele Quercia. Twitter ain’t Without Frontiers: Economic, Social, and Cultural Boundaries in International Communication. In The 17th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW), 2014.
The End 64
Selected References• Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. What is Twitter,
a Social Network or a News Media? In Proceedings of the 19th international conference on World Wide Web, ACM 2010
• Meeyoung Cha, Hamed Haddadi, Fabricio Benevenuto, and Krishna P. Gummadi. Measuring User Influence in Twitter: The Million Follower Fallacy. In International AAAI Conference on Weblogs and Social Media (ICWSM)
• Katharina Reinecke, Minh Khoa Nguyen, Abraham Bernstein, Michael Naf, and Krzysztof Z. Gajos. Doodle Around the World: Online Scheduling Behavior Reflects Cultural Differences in Time Perception and Group Decision-Making. In Proceedings of the 16th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW’13)
• Peter S. Dodds, Kameron D. Harris, Isabel M. Kloumann, Catherine A. Bliss, and Christopher M. Danforth. Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter. PLOS ONE, 2011.
• Geert Hofstede, Gert Jan Hofstede, and Michael Minkov. Cultures and Organizations: Software of the Mind. McGraw-Hill, 2010.
The End 65