Visualizing Communication
on Social Media:
Making Big Data Accessible
Karissa McKelvey, Alex Rudnick
Michael Conover and F...
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Can we use social media as
laboratories for social
science?
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Social Scientists
Computer Scientists
?
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Enables the study of discourse in large-
scale social media by collecting,
analyzing, classifying, visu...
#tcot
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Meme
Hashtag
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Meme
Hashtag
User
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Meme
Hashtag
User
URL
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Meme
Hashtag
User
URL
Phrase
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celebrities
spam
astroturf
politics
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politics
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#tcot
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celebrities
politics
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@barackobama
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celebrities
spam
astroturf
politics
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@bek_rt
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network structure
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political affiliation
network structure
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political affiliation
language detection
network structure
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sentiment
political affiliation
language detection
network structure
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Existing visualizations
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New Visualizations
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New Visualizations
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Interactive Meme Diffusion Network
#tcot
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#tcot#tcottruthy.indiana.edu 39
#tcot#tcot 40
#tcot#tcot 41
#tcot#tcot 42
#tcot#tcot 43
#tcot#tcot 44
#tcot#tcot 45
#usa
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New Visualizations
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User Data Exploration Interface
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Future Work
Time
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Future Work
Space
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Future Work
Feedback
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Thanks
Papers at cnets.indiana.edu/groups/nan/truthy
Sandro Flammini
Bruno Conçalves
Jacob Ratkiewicz
Lilian Weng
Mike Con...
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Karissa McKelvey
krmckelv@indiana.edu
rissarae.net
@krmckelv
Content injection
• Please follow @username for an outstanding progressive voice!
#p2 #dems #prog #democrats #tcot
• Coupl...
Predicting Political Alignment
1000 manually labeled users
Features Accuracy
Text (TF-IDF) 79%
Hashtags 91%
Retweet networ...
Cross-ideological interactions
Valence of a hashtag
#dems
#p2
#healthcare
#dreamact
#sgp
#twisters
#foxnews
#abortiontruth...
Competition for attention
• Some memes go viral, most do not
• Is this due to intrinsic value of memes?
User activity, pop...
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Visualizing Communication on Social Media: Making Big Data Acessible

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Presented at CSCW 2012 Workshop on Community Discourse and Action

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  • When abrahamlincoln gave a speech
  • The information travelled by word of mouth, by newspaper.
  • In history, we’ve seen that even newspapers can be wrong; incorrect information can spread even in carefully crafted and controlled, for-profit media outlets.
  • Politicians could try to mostly control what was said about them; communities found most information from central media, such as TV.
  • In the arab world last spring, the world was captivated by hundreds of thousands of people entering the streets in protest.
  • In October, the grass-roots Occupy protests sprang up in thousands of cities around the world. How did these protests organize?
  • Social media were catalysts for these protests, for a creation of international networks between millions of amateur “citizen journalists”.
  • But, who are the citizen journalists? What are they talking about? Are they influential?
  • With easy access to the data we create on social media, social scientists can study human social behavior on a larger scale than has ever been possible in human history.
  • Are there well-defined communication behaviors that characterizethe activities of influential actors?What is the role of bridging users in facilitating informationtransfer between ideologically opposed communities?Who are the opinion leaders, and how dothey engage in frame-making and agenda-setting?
  • These are difficult questions to answer by themselves, because collecting the massive amount of data from social media can be a challenge, even for expert computer scientistsqualitative analysis of rhetoric, argument, knowledge, judgment and opinion is best done by researchers trained in that realm.Thus,collaboration between computer andsocial scientists is key.
  • So what is this data? – People, videos, images, posts, links, etcA retweet is a form of aggregation; it is the action of one user forwarding content of another to all their followersUsers can also mention each other in posts – we call these ‘mentions’
  • visualizations of social media communication which can show us the network topology.Each person is represented by a nodeA line represents an information transfer between those two peopleOrange lines are mentions, and blue lines are retweetsThe width of the edge is a measure of the strength of that relationship; computed by how many events of that type occurred between those two peopleWe can see different characteristics in these networks for different types of communication. A network centered around a celebrity like Barack Obama , for example, has thousands of people mentioning and retweeting that person. A meme that is propagated by spammers will look much different, for example when a few users constantly retweeteachother
  • The meme is the account in the center. A bunch of accounts collude by retweeting each other (as displayed by the thick blue edges) to promote a particular website
  • Truthy is a system which computes analytics and metrics daily about data collected from social media to create visualizations of the data to users on its website.
  • Computes the static diffusion networks you saw earlier
  • Predicts the political affiliation of a given user based the hashtags he or she uses
  • Infers the language that the person uses on a per-meme basis using a
  • Detects the sentiment of the user using OpinionFinder
  • Building on a previous research by Michael Conover and team, we are able to predict the political alignment in relation to US politics of a given twitter account with 90% accuracy using hashtags.
  • Building on a previous research by Michael Conover and team, we are able to predict the political alignment in relation to US politics of a given twitter account with 90% accuracy using hashtags.
  • Building on a previous research by Michael Conover and team, we are able to predict the political alignment in relation to US politics of a given twitter account with 90% accuracy using hashtags.
  • Building on a previous research by Michael Conover and team, we are able to predict the political alignment in relation to US politics of a given twitter account with 90% accuracy using hashtags.
  • Building on a previous research by Michael Conover and team, we are able to predict the political alignment in relation to US politics of a given twitter account with 90% accuracy using hashtags.
  • Building on a previous research by Michael Conover and team, we are able to predict the political alignment in relation to US politics of a given twitter account with 90% accuracy using hashtags.
  • Building on a previous research by Michael Conover and team, we are able to predict the political alignment in relation to US politics of a given twitter account with 90% accuracy using hashtags.
  • Visualizing Communication on Social Media: Making Big Data Acessible

    1. 1. Visualizing Communication on Social Media: Making Big Data Accessible Karissa McKelvey, Alex Rudnick Michael Conover and Filippo Menczer Center for Complex Networks and Systems Research School of Informatics and Computing Indiana University, Bloomington truthy.indiana.edu 1
    2. 2. truthy.indiana.edu 2
    3. 3. truthy.indiana.edu 3
    4. 4. truthy.indiana.edu 4
    5. 5. truthy.indiana.edu 5
    6. 6. truthy.indiana.edu 6
    7. 7. truthy.indiana.edu 7
    8. 8. 8
    9. 9. truthy.indiana.edu 9
    10. 10. truthy.indiana.edu 10
    11. 11. Can we use social media as laboratories for social science? truthy.indiana.edu 11
    12. 12. Social Scientists Computer Scientists ? truthy.indiana.edu 12
    13. 13. truthy.indiana.edu Enables the study of discourse in large- scale social media by collecting, analyzing, classifying, visualizing, and modeling massive streams of public micro-blogging data 13
    14. 14. #tcot truthy.indiana.edu 14
    15. 15. Meme Hashtag truthy.indiana.edu 15
    16. 16. Meme Hashtag User truthy.indiana.edu 16
    17. 17. Meme Hashtag User URL truthy.indiana.edu 17
    18. 18. Meme Hashtag User URL Phrase truthy.indiana.edu 18
    19. 19. celebrities spam astroturf politics 19
    20. 20. politics truthy.indiana.edu 20
    21. 21. #tcot truthy.indiana.edu 21
    22. 22. celebrities politics truthy.indiana.edu 22
    23. 23. @barackobama truthy.indiana.edu 23
    24. 24. celebrities spam astroturf politics 24
    25. 25. @bek_rt truthy.indiana.edu 25
    26. 26. truthy.indiana.edu 26
    27. 27. network structure truthy.indiana.edu 27
    28. 28. political affiliation network structure truthy.indiana.edu 28
    29. 29. political affiliation language detection network structure truthy.indiana.edu 29
    30. 30. sentiment political affiliation language detection network structure truthy.indiana.edu 30
    31. 31. Existing visualizations truthy.indiana.edu 31
    32. 32. 32
    33. 33. 33
    34. 34. 34
    35. 35. 35
    36. 36. New Visualizations truthy.indiana.edu 36
    37. 37. New Visualizations truthy.indiana.edu 37 Interactive Meme Diffusion Network
    38. 38. #tcot truthy.indiana.edu 38
    39. 39. #tcot#tcottruthy.indiana.edu 39
    40. 40. #tcot#tcot 40
    41. 41. #tcot#tcot 41
    42. 42. #tcot#tcot 42
    43. 43. #tcot#tcot 43
    44. 44. #tcot#tcot 44
    45. 45. #tcot#tcot 45
    46. 46. #usa truthy.indiana.edu 46
    47. 47. truthy.indiana.edu 47
    48. 48. truthy.indiana.edu 48
    49. 49. New Visualizations truthy.indiana.edu 49 User Data Exploration Interface
    50. 50. truthy.indiana.edu 50
    51. 51. 51
    52. 52. 52
    53. 53. 53
    54. 54. Future Work Time truthy.indiana.edu 54
    55. 55. Future Work Space truthy.indiana.edu 55
    56. 56. Future Work Feedback truthy.indiana.edu 56
    57. 57. truthy.indiana.edu 57
    58. 58. Thanks Papers at cnets.indiana.edu/groups/nan/truthy Sandro Flammini Bruno Conçalves Jacob Ratkiewicz Lilian Weng Mike Conover Johan Bollen Przemek Grabowicz Mark Meiss Alex Vespignani Alex Rudnick Luca Aiello Filippo Menczer truthy.indiana.edu 58
    59. 59. truthy.indiana.edu 59 Karissa McKelvey krmckelv@indiana.edu rissarae.net @krmckelv
    60. 60. Content injection • Please follow @username for an outstanding progressive voice! #p2 #dems #prog #democrats #tcot • Couple Aborts Twin Boys For Being Wrong Gender .. http://bit.ly/xyz #tcot #hhrs #christian #tlot #teaparty #sgp #p2 #prolife Importance of content stream (hashtagtag) t in group A truthy.indiana.edu 60
    61. 61. Predicting Political Alignment 1000 manually labeled users Features Accuracy Text (TF-IDF) 79% Hashtags 91% Retweet network 95% Tags + Network 95% LSA (users-tags) truthy.indiana.edu 61
    62. 62. Cross-ideological interactions Valence of a hashtag #dems #p2 #healthcare #dreamact #sgp #twisters #foxnews #abortiontruthy.indiana.edu 62
    63. 63. Competition for attention • Some memes go viral, most do not • Is this due to intrinsic value of memes? User activity, popularity, influence? Topic persistence? Homophily? External media?... #jan25 truthy.indiana.edu 63

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