Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
User Behavior in Microblogs
with a Cultural Emphasis
Ruth García-Gavilanes
Advisor :
Ricardo Baeza-Yates
Web Research Grou...
2
•  The study of the trails left behind by users when they use the web:
Interactions, choices, searches, purchases, etc.
...
CSS AGENDA
PRESENT
FUTURE
Develop new instruments to tap into the
potential of found data and crowds ‘:
building a telesco...
Social Science
Research
Computer Science
and Information
Science
Survey Design and
Methodology
Objective of CSS
4
Computat...
5
User behavior
Microblogs Culture
•  All users: human
recommendations,
behavior evolution
•  Cross-country comparisons
Cu...
6
Friendship links in Twitter do not need to be reciprocal
I follow you
information
Case Study : Twitter
Introduction
#HAS...
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 I...
9Introduction
Goals
•  Study the effect of recommendations made by users
•  Compare the evolution of user behavior through...
Contributions
•  Providing a study of human generated recommendation on Twitter and its effect.
o  García-Gavilanes et al....
11
Data Mining Cultural
All users
Q1) What is the effect
on users from
Human generated
recommendations?
Q2) How do user be...
12
Data Mining Cultural
All users
Q1) What is the effect
on users from
Human generated
recommendations?
Q2) How do user be...
Q1) Human Recommendations
Recommendations 13
[Garcia-Gavilanes et al. Follow My Friends This Friday!, SocInfo’13]
Friendsh...
Q1) Acceptance
Recommendations 14
Total
Recommendation Instances 59,055,205
Accepted Recommendation
Instances
354,687
Soci...
Recommendations 15
Follow Friday recommendations
outperform the two alternative
conditions.
Q1) Acceptance
The accepted re...
Q1) Results
Recommendations 16
Features MAP
All 0.496
User-based 0.074
Relation-based 0.398
Recommendation-based 0.062
Use...
17
Data Mining Cultural
All users
Q1) What is the effect
on users from
Human generated
recommendations?
Q2) How do user be...
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 pe...
Q2)Tweeting Behavior
19
No
Mentions
Tweets
With links
Original tweets (OT)
Without links
Mentions
Re-tweets (RT)
No
Mentio...
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 o...
Q2) Users 2011 vs 2013
21
Majority of users remain in the same cluster except the echoers’ group.
Increase in Generalists ...
22
Data Mining Cultural
All users
Q1) What is the effect
on users from
Human generated
recommendations?
Q2) How do user be...
Q3) Cross-country comparison
•  Data: analysis of one year of Tweets for 10 most active
countries
•  Content: languages, s...
Q3) Activity and Engagement
24
[Garcia-Gavilanes et al. Microblogging without Borders: Differences and Similarities, WebSc...
25
Countries with more users not
necessarily the most engaged
Cross-country
[Garcia-Gavilanes et al. Microblogging without...
English
Portuguese
Japanese
Spanish
Bahasa−Indonesia
Bahasa−Malay
Korean
Dutch
German
Italian
Arabic
Users
50M 100M 200M 5...
Tweet function
27
Country URL (%) Hashtag (%) Mention (%) Retweet (%)
Indonesia 14.95 7.63 58.24 9.71
Japan 16.30 6.81 39....
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...
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 ...
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 an...
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 be...
Q5) Culture in Tweeting Behavior
•  Pace of Life
o  Predictability (tweets, mentions)
o  Measure entropy of posting tweets...
43
Tweets Correlation
1.  Pace of life
1.1 The higher the pace of life the less
fraction of users will tweet during workin...
●
●
●
●●●
●
● ●
●● ● ●●
●
●
●
● ●
● ●● ●●
●
●
●
● ●
●
Indonesia
Venezuela
Mexico
JapanBrazilColombia
Chile
South Korea Arg...
●
●
●
●
●
●
●
●
●
●
●
●●
●
●● ●●
● ●● ● ●●●●
●
●
●
●
Indonesia
Venezuela
Norway
Malaysia
Singapore
Chile
Mexico Philippine...
Q5) Why is this important?
46
Indicator Pace of Time:
Predictibility
Individualism:
Mentions
Power
Distance
ImbalanceMenti...
47
Data Mining Cultural
All users
Q1) What is the effect
on users from
Human generated
recommendations?
Q4) What cultural
...
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...
5K country – country pairs
interactions
50
10 weeks
Q6)Social, economic and cult. features
Communication
Distance
[ Garcia...
51
Q6) Top 1000 strongest edges
Using the gravity model the network is largely
clustered according to their geography
Comm...
Edges: Unique Mentions
Force-directed algorithm
52
Q6) Top 1000 strongest edges
Communication
[ Garcia-Gavilanes et al. Tw...
Unique Mentions
53
Q6) Top 1000 strongest edges
Communication
[ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, C...
Introduction 54
Argentina
Australia
Brazil Canada
Chile
Colombia
Dominican Republic
France
Germany
India
Indonesia
Ireland...
5K country – country pairs
interactions
481 country – country pairs
with social, economic and
cultural features
55
10 week...
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%...
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%...
Predictor P-value
Trade
6.34	
  
***
Cultural Dimension
3.91	
  
***
Gravity Model x Exports 3.78	
   **
Gravity Model
2.7...
59
Data Mining Cultural
All users
Q1) What is the effect
on users from
Human generated
recommendations?
Q2) How do user be...
Conclusions & Future Work
Conlusions 60
Human recommendations
Evolution of behavior
•  Recommendations by users have a
mea...
Conlusions 61
Cross-country comparison
Tweeting behavior
Communication
•  The collective behavior differ in certain
charac...
Thank you

QUESTIONS?

@ruthygarcia
ruth.garcia@upf.edu
(Ph.D survivor)
62The End
The End 63
Acknowledgements: Ricardo Baeza-Yates, Daniele Quercia, Yelena Mejova
Neil O’Hare, Luca Maria Aiello, Alejandro...
Publications
•  Ruth García-Gavilanes, Barbara Poblete, Marcelo Mendoza, Alejandro Jaimes.
Microblogging without Borders: ...
Selected References
•  Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. What is Twitter,
a Social Network or a News...
Upcoming SlideShare
Loading in …5
×

USER BEHAVIOR IN MICROBLOGS WITH A CULTURAL EMPHASIS

561 views

Published on

PhD defense contributions:
Providing a study of human generated recommendation on Twitter and its effect.
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.
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.
García-Gavilanes et al. Microblogging without Borders: Differences and Similarities. Websci’11.
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
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.
García-Gavilanes et al. Twitter ain’t Without Frontiers: Economic, Social, and Cultural Boundaries in International Communication. CSCW’14.

  • Be the first to comment

  • Be the first to like this

USER BEHAVIOR IN MICROBLOGS WITH A CULTURAL EMPHASIS

  1. 1. User Behavior in Microblogs with a Cultural Emphasis Ruth García-Gavilanes Advisor : Ricardo Baeza-Yates Web Research Group Universitat Pompeu Fabra & Yahoo Labs PhD Thesis Defense February 26, 2015
  2. 2. 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
  3. 3. 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
  4. 4. Social Science Research Computer Science and Information Science Survey Design and Methodology Objective of CSS 4 Computational Social Science Introduction
  5. 5. 5 User behavior Microblogs Culture •  All users: human recommendations, behavior evolution •  Cross-country comparisons Cultural Emphasis Introduction
  6. 6. 6 Friendship links in Twitter do not need to be reciprocal I follow you information Case Study : Twitter Introduction #HASHTAGS @Mentions & Retweets
  7. 7. 7 #Disasters Teheran, Iran Introduction
  8. 8. 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.
  9. 9. 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 :
  10. 10. 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. 11. 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-country Q3) 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. 12. 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-country Q3) 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
  13. 13. 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
  14. 14. Q1) Acceptance Recommendations 14 Total Recommendation 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
  15. 15. Recommendations 15 Follow Friday recommendations outperform the two alternative conditions. Q1) Acceptance The accepted recommendations have more longevity than other links. [Garcia-Gavilanes et al. Follow My Friends This Friday!, SocInfo’13]
  16. 16. Q1) Results Recommendations 16 Features MAP All 0.496 User-based 0.074 Relation-based 0.398 Recommendation-based 0.062 User + Relation 0.518 User + Format 0.079 Relation + 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 140 features [Garcia-Gavilanes et al. Follow My Friends This Friday!, SocInfo’13]
  17. 17. 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-country Q3) 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
  18. 18. 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 1.1M 2011 2013 2011 2013 Q2) DATA 18 Inactive < 1 tweet per day Hyperactive > 22 per day 530K 570K 1.3M [García-Gavilanes et al. Evolution of Target Audiences. SocInfo’14] Evolution
  19. 19. 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]
  20. 20. 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 mentions OT with links and no mentions OT with mentions and no links OT without links or mentions RT with links RT without links Endogenous Conversationalists Generalists Echoers Link Feeders Evolution [García-Gavilanes et al. Evolution of Target Audiences. SocInfo’14]
  21. 21. 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. 22. 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-country Q3) 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
  23. 23. 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
  24. 24. Q3) Activity and Engagement 24 [Garcia-Gavilanes et al. Microblogging without Borders: Differences and Similarities, WebSci’11] Cross-country 12M active users 6M with valid location 4M user from 10 most active countries. 5B tweets during 2010 .
  25. 25. 25 Countries with more users not necessarily the most engaged Cross-country [Garcia-Gavilanes et al. Microblogging without Borders: Differences and Similarities, WebSci’11] Q3) Activity and Engagement
  26. 26. 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 language More than 10% in non-english speaking countries Non-western countries seem to be more Positive Based in Dodds et al., 2011 Cross-country [Garcia-Gavilanes et al. Microblogging without Borders: Differences and Similarities, WebSci’11]
  27. 27. Tweet function 27 Country URL (%) Hashtag (%) Mention (%) Retweet (%) Indonesia 14.95 7.63 58.24 9.71 Japan 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]
  28. 28. 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 Brazil UK Mexico USA NetherlandsAustralia Canada Indonesia South_KoreaJapan Countries Diameter Avg. 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
  29. 29. Q3) Connectivity 29Cross-country [w/ Poblete et al. Do all Birds Tweet the Same?, CIKM, 2011]
  30. 30. Q3) Connectivity 30 More self Connected Cross-country [w/ Poblete et al. Do all Birds Tweet the Same?, CIKM, 2011]
  31. 31. Q3) Connectivity 31 More connected Cross-country [w/ Poblete et al. Do all Birds Tweet the Same?, CIKM, 2011]
  32. 32. •  Need cultural models to understand differences across countries in Microblogs 32Cross-country
  33. 33. 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-country Q3) 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
  34. 34. Culture 34Cultural Models
  35. 35. WHAT IS CULTURE ? 35Cultural Models
  36. 36. CULTURE Software of the mind that distinguishes members of one group or category of people from others 36Cultural Models
  37. 37. 37Cultural Models
  38. 38. 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 Civilizations o  Politics of identity replacing politics of interest. 38Cultural Models
  39. 39. Pace of Life IndividualismPower Distance 39 Levine Hall Cultural Models
  40. 40. Can such differences also be captured from online interactions? 40Cultural Models
  41. 41. 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-country Q3) 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
  42. 42. 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 recommendation preferentially from more popular users (in-degree imbalance). 42 [Garcia-Gavilanes et al. Cultural Dimensions in Twitter, ICWSM, 2013] Culture
  43. 43. 43 Tweets Correlation 1.  Pace of life 1.1 The higher the pace of life the less fraction of users will tweet during working hours 1.2 The higher the pace of life, the more predictability 1.1 Users **-0.58 1.2 Mentions **0.68 1.2 Tweets **0.62 2. Individualism 2.1 User chat less with others in more individualistic countries 2.1 Conversation ***−0.55 3. Power Distance 3.1 Users prefer to follow and 3.2 recommend more popular users than themselves in countries with a higher power distance Users followees **0.62 Users 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]
  44. 44. ● ● ● ●●● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● Indonesia Venezuela Mexico JapanBrazilColombia Chile South Korea Argentina Philippines Malaysia Spain NetherlandsTurkey UKSouth Africa Singapore Ireland Canada FranceBelgium Sweden Australia United States Norway New Zealand Italy Russia India Germany 80 85 90 95 100 0 25 50 75 Individualism Index FractionofEngagement Introduction 44 Q5) Individualism [Hong et al.. “Language matters in twitter: A large scale study” ICWSM 11]
  45. 45. ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ●● ● ●●●● ● ● ● ● Indonesia Venezuela Norway Malaysia Singapore Chile Mexico Philippines Colombia United States South Korea IndiaBrazil Canada ArgentinaAustralia RussiaItalyNew Zealand SpainGermany Japan FranceSouth AfricaUKIreland TurkeyNetherlands BelgiumSweden −1000 0 1000 2000 3000 4000 5000 30 60 90 Power Distance Index In−degreeImbalance Introduction 45 27% of all blog trends are about artists and celebrities [Silang et al, 2011] Q5) Power
  46. 46. 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 ***-0.60 Inequality ***-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. 47. 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-country Q3) 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
  48. 48. 48Cultural Models http://commons.wikimedia.org/wiki/File:Clash_of_Civilizations_mapn2.png
  49. 49. 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
  50. 50. 5K country – country pairs interactions 50 10 weeks Q6)Social, economic and cult. features Communication Distance [ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW 2014]
  51. 51. 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 model Force-directed algorithm [ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW 2014]
  52. 52. Edges: Unique Mentions Force-directed algorithm 52 Q6) Top 1000 strongest edges Communication [ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW 2014]
  53. 53. Unique Mentions 53 Q6) Top 1000 strongest edges Communication [ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW 2014]
  54. 54. 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 Spain Sweden United Kingdom United States Venezuela Q6) Top 50 strongest edges [ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW, 2014]
  55. 55. 5K country – country pairs interactions 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]
  56. 56. 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 Adjusted 56 Q5) Features Higher accuracy at high communication volumes with worse performance as the communication decreases. The combination of features improves the prediction Communication ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 communication volume model'sprediction Model’spredictions Communication Volume [ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW 2014]
  57. 57. 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 r2 Adjusted 57 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.1 0.2 0.5 1.0 0.10.20.51.0 communication volume model'sprediction Q5) Features Higher accuracy at high communication volumes with worse performance as the communication decreases. The combination of features improves the prediction Communication Communication Volume Model’spredictions Communication Volume [ Garcia-Gavilanes et al. Twitter ain’t without Frontiers, CSCW 2014] Log-scale
  58. 58. 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. 59. 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-country Q3) 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
  60. 60. 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
  61. 61. 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
  62. 62. Thank you
 QUESTIONS?
 @ruthygarcia ruth.garcia@upf.edu (Ph.D survivor) 62The End
  63. 63. The End 63 Acknowledgements: Ricardo Baeza-Yates, Daniele Quercia, Yelena Mejova Neil O’Hare, Luca Maria Aiello, Alejandro Jaimes, Barbara Poblete, Marcelo Mendoza, Andreas Kaltenbrunner, Diego Sáez-Trumper, Pablo Aragón, David Laniado, Ilaria Bordino, Sara Haijan, Amin Mantrach . Acknowledgements
  64. 64. 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 Sáez-Trumper, Ricardo Baeza- Yates, Pablo Aragòn 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
  65. 65. 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

×