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
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).
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 detailshttp://bit.ly/infoseminar
48. thank you mornaaman.com firstname.lastname@example.org @informor Rutgers SC&I Social Media Information Lab