Communication Characteristics of IM : Effects and Predictions of Interpersonal Relationships

1. Communication Characteristics of IM:Effects and Predictions of Interpersonal Relationships Daniel Avrahami & Scott E. Hudson Human-Computer Interaction InstituteSchool of Computer ScienceCarnegie Mellon University

2. Background • Instant Messaging, or IM, is one of the most popular communication mediums today • No longer a medium only for social communication • Useful for work in many ways [Bradner’99; Nardi’00; Handel’02; Herbsleb’02; Isaacs’02] • Investigate the effect of interpersonal relationships on IM interaction

3. Related Work • Relationship type has significant effects on communication, including the quality, purpose and perceived value [Duck’91] • Cues, such as tempo, pauses, speech rates and the frequency of turns, affect the way in which conversation partners perceive each other [Feldstein’94] • Frequency affects communication [FTF:Whittaker’94, IM:Isaacs’02]

4. Outline • Data Collection and Segmentation • Measures and Analysis Method • Results • Statistical Predictive Models • Summary and Limitations • Conclusions

5. Data Collection

6. Data collection • Created a plugin for Trillian Pro (written in C) • Non-intrusive collection of IM and desktop events • Why this setup?

7. Data collection (cont) • What gets collected: • IM Events • Message sent or received, Trillian start or stop, Message window open or close, Starting to type a message, Status changes (online, away, occupied, etc.), Incoming message indicator is blinking • Desktop Events (not relevant for this talk) • Applications, mouse events, etc. • Each participants records for at least 4 weeks

8. Data collection (cont.) • Privacy of data • Masking messages • for example, the message:“This is my secret number: 1234 :-)” was recorded as “AAAA AA AA AAAAAA AAAAAA: DDDD :-)”. • Temporary masking • Alerting buddies • Hashing buddy-names

9. Buddy Coder • Co-worker (Senior) • Co-worker (Peer) • Co-worker (Junior) • Co-worker (Other) • Friend • Family • Spouse • Significant Other • Acquaintance • Friend & Co-worker • Self • Bot • [Unknown/Unused]

10. Buddy Coder • Co-worker (Senior) • Co-worker (Peer) • Co-worker (Junior) • Co-worker (Other) • Friend • Family • Spouse • Significant Other • Acquaintance • Friend & Co-worker • Self • Bot • [Unknown/Unused]

11. Buddy Coder • Co-worker (Senior) • Co-worker (Peer) • Co-worker (Junior) • Co-worker (Other) • Friend • Family • Spouse • Significant Other • Acquaintance • Friend & Co-worker • Self • Bot • [Unknown/Unused]

12. Buddy Coder • Co-worker (Senior) • Co-worker (Peer) • Co-worker (Junior) • Co-worker (Other) • Friend • Family • Spouse • Significant Other • Acquaintance • Friend & Co-worker • Self • Bot • [Unknown/Unused]

13. Buddy Coder • Co-worker (Senior) • Co-worker (Peer) • Co-worker (Junior) • Co-worker (Other) • Friend • Family • Spouse • Significant Other • Acquaintance • Friend & Co-worker • Self • Bot • [Unknown/Unused]

14. Participants • 16 participants • Researchers: 6 full-time employees at an industrial research lab (mean age=40.33) • Interns: 2 summer interns at the industrial research lab (mean age=34.5) • Students: 8 Masters students (mean age=24.5) • (4 of the participants provided full text)

15. Participants • Nearly 5,200 hours recorded • Over 90,000 messages • Over 400 buddies • On average, participants exchanged a message every: 3.4 minutes • (researchers avg=8.1)

16. Relationships distribution

17. Session-level measures

18. session Session-level measures 5 minutes threshold (similar to Isaacs’02)

19. Session-level measures

20. Session-level measures

21. The effect of relationships • Used a repeated-measures ANOVA • Relationship Category (Work, Mix, Social) and Participation Group were repeated • Participants and BuddyID modeled as random effects • Participants nested in Group • BuddyID nested first in Participants, then in Group • N=3297 sessions

22. Results

23. Summary of Results • Sessions with Social contacts were longer and with more messages BUT at a significantly slower pace • Sessions with Work contacts were at a faster pace with longer messages

24. Results: Session length • Significant effect on Session Duration (p<.001) • Social significantly longer sessions than both Mix and Work (Work and Mix n.s.) • Similar effects for • Number of TurnsNumber of MessagesNumber of Characters • Duration correlated at >.85

25. Results: Messaging rate • Significant effect on Messaging Rate (p<.01) • Social significantly slower than Mix (p=.003) • Social marginally slower than Work (p=.078) • Maximum-Gap (p<.05)Social longer than Work(p=.013)

26. Results: Length of messages • Significant effect on Message Length (Characters-per-Message) (p<.001) • Work significantly longer than both Social (p<.001) and Mix (p=.002)

27. Discussion • Why longer messages with work contacts? • Less casual • Harder to reach common ground • Concepts more complex

28. Discussion • Why slower pace with social contacts? (Message length does not account for the effect) • Maybe giving less attention to these sessions • Is it a result of multiple ongoing conversations? • Not a bad guess! • IM Activity with Others • Number of open IM windows • BUT, does not explain the results • All the effects of Relationship Type are still there

29. Predicting Relationships

30. Predicting relationships • How can it be used? • Augmenting IM systems • Indicators of unavailability • Differential alerts • Shared with other mediums • E.g. Email • Provide organizational overview

31. Predicting relationships Nominal Logistic Regression Cross-validation with 16 models (omitting one participant each time)

32. Models performance • Results from pairs with 2 sessions or more (78% of the data)

33. Models performance • Results from pairs with 2 sessions or more (78% of the data) • Both significantly better than the prior probability

34. Summary • Showed that interpersonal relationships have a significant effect on IM communication • Results suggest that users have longer sessions with social contacts, but devote less focus • The results informed the creation of two predictive models of interpersonal relationships • (2-way and 3-way)

35. Limitations • Only 412 participant-buddy pairs • Currently collecting data from 11 additional participants • Looked at high-level relationship types • May be able to look at finer level with additional data • Hard for Model to distinguish Mix from Social • Looking at a cascading prediction approach

36. Future Work • Examine the effects of additional aspects of relationships on communication • The effects of buddy-familiarity • The effects of task criticality • The effects of physical distance • Move from predictions of relationships that span sessions, to predictions of individual sessions: • Work vs. Not work • Task oriented • Information sharing

37. Conclusions

38. Conclusions • Instant Messaging is maturing and with it, its users • The young adults who have been using IM for their social communication for over a decade are now joining the workforce • A better understanding is needed of the factors affecting IM communication • This work is a step towards reaching this goal

39. Acknowledgements • Laura Dabbish • Sue Fussell • Darren Gergle • Eric Horvitz • Bob Kraut • Jennifer Lai • Roni Rosenfeld

40. for more info visit:www.cs.cmu.edu/~nx6 or email:nx6@cmu.edu thank you this work was funded in part by NSF Grants IIS-0121560, IIS-0325351, and by DARPA Contract No. NBCHD030010