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Emotion, Tie Persistence,and Network Structure onTwitterMor NaamanRutgers SC&I | School Media Information Lab
social media information lab?
social media research:1. what are people doing   (and why)?
social media research:2. understanding social  systems at scale
social media research:3. creating new experiences
social   media         awareness streams         networks
today’s big storygenerate a better understanding of thesocial dynamicsvalidate theories from social sciences inthese new a...
today’s more specific storyTwitter and networks:Part 1. social sharing of emotion andnetworks on TwitterPart 2. unfollowing...
study 1emotion & social networksKivran-Swaine & Naaman. NetworkProperties and Social Sharing ofEmotions in Social Awarenes...
main questionHow does users’ social sharing of emotion in  SAS relate to the properties of their social                   ...
research questionsRQ1What is the association between people’stendency to express emotion (joy, sadness, other)in their pos...
research questionsRQ2What is the association between people’stendency to express emotion (joy, sadness, other)in their pos...
1.5 step ego-centric network
theory backgroundexpression of emotion  number of followers      ( - ) people who mostly post about      themselves have ...
theory backgroundexpression of emotion  network densityexpression of emotion  reciprocity rate                          ...
datacontent dataset from Naaman, Boase, Lai (2010)social network dataset from Kwak et al. (2010)105,599 messages from 628 ...
pilot studymanually coded 940 tweets from 97 (randomlyselected) users into:joy, sadness, fear, trust, anger, disgust, anti...
pilot study       joy       on average 23% of a user’s updates       “Fireworks at the Cumming fairgrounds were Yay!”     ...
study detailsautomated analysis of the users’ tweets based onLIWC“expression of emotion” => “existence of emotivewords”
some gender differences        joy        sadness        other emotions
results…some gender differences                number of followees                clustering coefficient
results…some gender differences                gain                reciprocity rate
analysisindependent variables:   joy (interactions-updates),   sadness (interactions-updates),   emo (interactions-updates...
results… explaining number of followers (R2 = .22)    @follower …     joy-interactions .35 **    @follower …     sadness-i...
results… explaining network density (R2 = .33)       yay!         joy-updates -.10 **    @follower …     sadness-interacti...
limitations & future workbetter emotion classifierimprove sampling, increase datasetculture dependentdyad-level analysis
today’s more specific storyTwitter and networks:Part 1. social sharing of emotion andnetworks on TwitterPart 2. unfollowing...
study 2unfollowing on TwitterKivran-Swaine, Govindan & Naaman.The Impact of Network Structure onBreaking Ties in Online So...
blue=unfollow
main question:what structural properties of the social network of nodes and dyads predict the breaking of ties (unfollows)...
theory backgroundtie strengthembeddedness within networkspower & status
datacontent dataset from Naaman, Boase, Lai (2010)social network dataset from Kwak et al. (2010)Twitter API – connections ...
analysis* independent variables (computed for our 245K dyads)   seed properties      follower-count, follower-to-followee ...
<disclaimer>the following slides are NOT scientific evidenceand are shown here for illustration purposesno control for intr...
effect of number of followers (none):
effect of reciprocity (large):
effect of reciprocity (another view):
effect of follow-back rate
effect of common neighbors
</disclaimer>back to scientific results (made R break sweat)sparing you the details, though
in-depth analysisthe details you did not want to know…multi-level logistic regression (dyads/edgesnested within seed nodes...
some resultseffect of tie strength on breaking of ties      *** dyadic reciprocity (-)      *** network density (-)      **...
some resultseffect of power & status on breaking of ties      ***   prestige ratio (+)      ***   follow-back rate (-)     ...
some resultseffect of embeddedness on breaking of ties      *** common neighbors (-)      *** highly statistically significant
limitations & future workonly two snapshots: add moreadditional (non-structural) variables (e.g.,frequency of posting!)emo...
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Stanford Info Seminar: Unfollowing and Emotion on Twitter

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Transcript of "Stanford Info Seminar: Unfollowing and Emotion on Twitter"

  1. 1. Emotion, Tie Persistence,and Network Structure onTwitterMor NaamanRutgers SC&I | School Media Information Lab
  2. 2. social media information lab?
  3. 3. social media research:1. what are people doing (and why)?
  4. 4. social media research:2. understanding social systems at scale
  5. 5. social media research:3. creating new experiences
  6. 6. social media awareness streams networks
  7. 7. today’s big storygenerate a better understanding of thesocial dynamicsvalidate theories from social sciences inthese new and important settings
  8. 8. today’s more specific storyTwitter and networks:Part 1. social sharing of emotion andnetworks on TwitterPart 2. unfollowing on Twitter
  9. 9. study 1emotion & social networksKivran-Swaine & Naaman. NetworkProperties and Social Sharing ofEmotions in Social AwarenessStreams. (CSCW 2011).
  10. 10. main questionHow does users’ social sharing of emotion in SAS relate to the properties of their social networks? picture by palo
  11. 11. research questionsRQ1What is the association between people’stendency to express emotion (joy, sadness, other)in their posts (updates or interactions) and theirnumber of followers?
  12. 12. research questionsRQ2What is the association between people’stendency to express emotion (joy, sadness, other)in their posts (updates or interactions) and theirnetwork characteristics like density and reciprocityrate?
  13. 13. 1.5 step ego-centric network
  14. 14. theory backgroundexpression of emotion  number of followers ( - ) people who mostly post about themselves have significantly lower number of followers* ( + ) emotional broadcaster theory* Naaman, Boase, Lai (CSCW 2010)
  15. 15. theory backgroundexpression of emotion  network densityexpression of emotion  reciprocity rate ( + ) intimacy ( - ) curbing
  16. 16. datacontent dataset from Naaman, Boase, Lai (2010)social network dataset from Kwak et al. (2010)105,599 messages from 628 users who: had no more than 5,000 followers or followees posted at least one Twitter update in July 2009 in English still had public profile in April 2010
  17. 17. pilot studymanually coded 940 tweets from 97 (randomlyselected) users into:joy, sadness, fear, trust, anger, disgust, anticipation,and surprise.
  18. 18. pilot study joy on average 23% of a user’s updates “Fireworks at the Cumming fairgrounds were Yay!” “Just snagged last copy of wii sports resort. awesome. Sophia had a blast. Lucy said, “ooooh,” over and over. Good times with my family.!” sadness on average 10% of a user’s updates “RIP Kathy. Live life for today. You never know how long you have.!”
  19. 19. study detailsautomated analysis of the users’ tweets based onLIWC“expression of emotion” => “existence of emotivewords”
  20. 20. some gender differences joy sadness other emotions
  21. 21. results…some gender differences number of followees clustering coefficient
  22. 22. results…some gender differences gain reciprocity rate
  23. 23. analysisindependent variables: joy (interactions-updates), sadness (interactions-updates), emo (interactions-updates)3 linear regression models for dependent variables: number of followers network density reciprocity rate
  24. 24. results… explaining number of followers (R2 = .22) @follower … joy-interactions .35 ** @follower … sadness-interactions .20 ** ** p < .01
  25. 25. results… explaining network density (R2 = .33) yay! joy-updates -.10 ** @follower … sadness-interactions -.18 ** number of followers -.50 ** ** p < .01
  26. 26. limitations & future workbetter emotion classifierimprove sampling, increase datasetculture dependentdyad-level analysis
  27. 27. today’s more specific storyTwitter and networks:Part 1. social sharing of emotion andnetworks on TwitterPart 2. unfollowing on Twitter
  28. 28. study 2unfollowing on TwitterKivran-Swaine, Govindan & Naaman.The Impact of Network Structure onBreaking Ties in Online SocialNetworks: Unfollowing on Twitter.(CHI 2011).
  29. 29. blue=unfollow
  30. 30. main question:what structural properties of the social network of nodes and dyads predict the breaking of ties (unfollows) on Twitter?
  31. 31. theory backgroundtie strengthembeddedness within networkspower & status
  32. 32. datacontent dataset from Naaman, Boase, Lai (2010)social network dataset from Kwak et al. (2010)Twitter API – connections still exist 9 months later? 715 seed nodes 245,586 “following” connections to seed nodes 30.6% dropped between 07/2009 & 04/2010
  33. 33. analysis* independent variables (computed for our 245K dyads) seed properties follower-count, follower-to-followee ratio, network density, reciprocity rate, follow-back rate follower properties follower-count, follower-to-followee ratio dyad properties reciprocity, common neighbors, common followers, common friends, right transitivity, left transitivity, mutual transitivity, prestige ratio
  34. 34. <disclaimer>the following slides are NOT scientific evidenceand are shown here for illustration purposesno control for intra-seed effects; no inter-variableeffectsno R installation was harmed in the making of thefollowing figures
  35. 35. effect of number of followers (none):
  36. 36. effect of reciprocity (large):
  37. 37. effect of reciprocity (another view):
  38. 38. effect of follow-back rate
  39. 39. effect of common neighbors
  40. 40. </disclaimer>back to scientific results (made R break sweat)sparing you the details, though
  41. 41. in-depth analysisthe details you did not want to know…multi-level logistic regression (dyads/edgesnested within seed nodes)three models; full one includes seed, follower, anddyadic/edge variablescomplete details: in the paper
  42. 42. some resultseffect of tie strength on breaking of ties *** dyadic reciprocity (-) *** network density (-) *** highly statistically significant
  43. 43. some resultseffect of power & status on breaking of ties *** prestige ratio (+) *** follow-back rate (-) *** f’s follower-to followee ratio (-) *** dyadic reciprocity (-) *** highly statistically significant
  44. 44. some resultseffect of embeddedness on breaking of ties *** common neighbors (-) *** highly statistically significant
  45. 45. limitations & future workonly two snapshots: add moreadditional (non-structural) variables (e.g.,frequency of posting!)emotion and tie breaks
  46. 46. …and even broaderwhat can we learn from social dynamics onTwitter (and Facebook) about: our relationships? our language? our society and culture? our interests and activities?
  47. 47. for more detailshttp://bit.ly/infoseminar
  48. 48. thank you mornaaman.com mor@rutgers.edu @informor Rutgers SC&I Social Media Information Lab
  49. 49. NYC vs. Washington DC
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