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Stanford Info Seminar: Unfollowing and Emotion on Twitter
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Stanford Info Seminar: Unfollowing and Emotion on Twitter


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  • 1. Emotion, Tie Persistence,and Network Structure onTwitterMor NaamanRutgers SC&I | School Media Information Lab
  • 2. social media information lab?
  • 3. social media research:1. what are people doing (and why)?
  • 4. social media research:2. understanding social systems at scale
  • 5. social media research:3. creating new experiences
  • 6. social media awareness streams networks
  • 7. today’s big storygenerate a better understanding of thesocial dynamicsvalidate theories from social sciences inthese new and important settings
  • 8. today’s more specific storyTwitter and networks:Part 1. social sharing of emotion andnetworks on TwitterPart 2. unfollowing on Twitter
  • 9. study 1emotion & social networksKivran-Swaine & Naaman. NetworkProperties and Social Sharing ofEmotions in Social AwarenessStreams. (CSCW 2011).
  • 10. main questionHow does users’ social sharing of emotion in SAS relate to the properties of their social networks? picture by palo
  • 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. 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. 1.5 step ego-centric network
  • 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. theory backgroundexpression of emotion  network densityexpression of emotion  reciprocity rate ( + ) intimacy ( - ) curbing
  • 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. pilot studymanually coded 940 tweets from 97 (randomlyselected) users into:joy, sadness, fear, trust, anger, disgust, anticipation,and surprise.
  • 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. study detailsautomated analysis of the users’ tweets based onLIWC“expression of emotion” => “existence of emotivewords”
  • 20. some gender differences joy sadness other emotions
  • 21. results…some gender differences number of followees clustering coefficient
  • 22. results…some gender differences gain reciprocity rate
  • 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. results… explaining number of followers (R2 = .22) @follower … joy-interactions .35 ** @follower … sadness-interactions .20 ** ** p < .01
  • 25. results… explaining network density (R2 = .33) yay! joy-updates -.10 ** @follower … sadness-interactions -.18 ** number of followers -.50 ** ** p < .01
  • 26. limitations & future workbetter emotion classifierimprove sampling, increase datasetculture dependentdyad-level analysis
  • 27. today’s more specific storyTwitter and networks:Part 1. social sharing of emotion andnetworks on TwitterPart 2. unfollowing on Twitter
  • 28. study 2unfollowing on TwitterKivran-Swaine, Govindan & Naaman.The Impact of Network Structure onBreaking Ties in Online SocialNetworks: Unfollowing on Twitter.(CHI 2011).
  • 29. blue=unfollow
  • 30. main question:what structural properties of the social network of nodes and dyads predict the breaking of ties (unfollows) on Twitter?
  • 31. theory backgroundtie strengthembeddedness within networkspower & status
  • 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. 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. <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. effect of number of followers (none):
  • 36. effect of reciprocity (large):
  • 37. effect of reciprocity (another view):
  • 38. effect of follow-back rate
  • 39. effect of common neighbors
  • 40. </disclaimer>back to scientific results (made R break sweat)sparing you the details, though
  • 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. some resultseffect of tie strength on breaking of ties *** dyadic reciprocity (-) *** network density (-) *** highly statistically significant
  • 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. some resultseffect of embeddedness on breaking of ties *** common neighbors (-) *** highly statistically significant
  • 45. limitations & future workonly two snapshots: add moreadditional (non-structural) variables (e.g.,frequency of posting!)emotion and tie breaks
  • 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. for more details
  • 48. thank you @informor Rutgers SC&I Social Media Information Lab
  • 49. NYC vs. Washington DC