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Responsiveness in IM: Predictive Models Supporting Inter-Personal Communication Daniel Avrahami, Scott E. Hudson Carnegie Mellon University www.cs.cmu.edu/~nx6.

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Presentation on theme: "Responsiveness in IM: Predictive Models Supporting Inter-Personal Communication Daniel Avrahami, Scott E. Hudson Carnegie Mellon University www.cs.cmu.edu/~nx6."— Presentation transcript:

1 Responsiveness in IM: Predictive Models Supporting Inter-Personal Communication Daniel Avrahami, Scott E. Hudson Carnegie Mellon University

2 Q: if an instant message were to arrive right now, would the user respond to it? in how long?  collected field data  5200 hours  90,000 messages  IM and desktop events  models predicting responsiveness  as high as 90.1%

3 why should we care?

4  IM is one of the most popular communication mediums  no longer a medium just for kids (work / parents)  sending messages is “cheap” but the potential for interruptions is great  unsuccessful communication can have a negative effect on both sender and receiver  can disrupt the receiver’s work  can leave the sender waiting for information  true not only for IM

5 how can such models help? senderreceiver q intercept w alert e mask r enhance awareness message

6 sender how can such models help? message receiver q intercept w alert e mask r enhance q

7 sender how can such models help? message receiver q intercept w alert e mask r enhance q

8 sender how can such models help? message receiver q intercept w alert e mask r enhance w q

9 sender how can such models help? awareness receiver e q intercept w alert e mask r enhance shhhh q w

10 sender how can such models help? awareness receiver r q intercept w alert e mask r enhance (carefully) not now q e w

11 related work  instant messaging  [Nardi’00, Isaacs’02, Voida’02]  interruptions and disruptions  [Gillie’89, Cutrell’01, Hudson’02, Dabbish’04]  models of presence and interruptibility  [Horvitz’02, Begole’02, Hudson’03, Begole’04, Horvitz’04, Fogarty’05, Iqbal’06]

12 coming up…  data collection  participants  responsiveness overview  predictive models  how (features and classes)  results  a closer look (new! not in the paper)  future work

13 data collection  a plugin for Trillian Pro (written in C)  non-intrusive collection of IM and desktop events

14 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 :-)”.  alerting buddies  hashing buddy-names  4 participants provided full content

15 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)  nearly 5200 hours recorded  over 90,000 messages

16 responsiveness 50%

17 responsiveness 92% 50%

18 defining “IM Sessions” session 92%

19 defining “Session Initiation Attempts” used two subsets: 5 minutes (similar to Isaacs’02) and 10 minutes session

20 features  for every message:  features describing IM state. including:  Day of week  Hour  Is the Message-Window open  Buddy status (e.g., “Away”)  Buddy status duration  Time since msg to buddy  Time since msg from another buddy  Any msg from other in the last 5 minutes  log(time since msg with any buddy)  Is an SIA-5

21 features (cont.)  for every message:  features describing desktop state (following Horvitz et al. Fogarty et al. and others). including:  Application in focus  Application in focus duration  Previous application in focus  Previous application in focus duration  Most used application in past m minutes  Duration for most used application in past m minutes  Number of application switches in past m minutes  Amount of keyboard activity in past m minutes  Amount of mouse activity in past m minutes  Mouse movement distance in past m minutes

22 what are we predicting?  “Seconds until Response”  computed, for every incoming message from a buddy, by noting the time it took until a message was sent to the same buddy  examined five responsiveness thresholds  30 seconds, 1, 2, 5, and 10 minutes

23 modeling method  features selected using a wrapper-based selection technique  AdaBoosting on Decision-Tree models  10-fold cross-validation  10 trials: train on 90%, test on 10%  next we report combined accuracy

24 results

25 results (full feature-set models) all significantly better than the prior probability (p<.001)

26 results (user-centric models)  previous models used information about the buddy (e.g., time since messing that buddy)  can predict different responsiveness for different buddies  but what if you wanted just one level of responsiveness?  built models that did not use any buddy- related features

27 results (user-centric models) all significantly better than the prior probability (p<.001)

28 a closer look (new! not in the paper)

29  analysis of the continuous measure:  log(Time Until Response)  repeated measures ANOVA  Independent Variables: features subset  ParticipantID [Group] as random effect

30 DFDFDenFp Group HourMinutes log(ownStat_dur) log(timeSincOMsgBdy) * log(timeSincOOther) log(buddyStat_dur) BaseRelationship MessageWindowsCount * FocusedWindowType FocusedWindowDur PrevFocusedWinFeatureDur * MostFocusedWinTime(30) MostFocusedWinTime(600) WinSwitchesCountFeature(30) * WinSwitchesCountFeature(600) MostFocusedWinType(60) MostFocusedWinType(300) * MouseEventCountFeature(30) MouseDistanceFeature(60) * MouseDistanceFeature(600) KBCountFeature(30) * KBCountFeature(600)

31 EstimateDFDFDenFp Group HourMinutes log(ownStat_dur) log(timeSincOMsgBdy) * log(timeSincOOther) log(buddyStat_dur) BaseRelationship MessageWindowsCount * FocusedWindowType FocusedWindowDur PrevFocusedWinFeatureDur * MostFocusedWinTime(30) MostFocusedWinTime(600) WinSwitchesCountFeature(30) * WinSwitchesCountFeature(600) MostFocusedWinType(60) MostFocusedWinType(300)Nom * MouseEventCountFeature(30) MouseDistanceFeature(60) * MouseDistanceFeature(600) KBCountFeature(30) * KBCountFeature(600)

32 EstimateDFDFDenFp Group HourMinutes log(ownStat_dur) log(timeSincOMsgBdy) * log(timeSincOOther) log(buddyStat_dur) BaseRelationship MessageWindowsCount * FocusedWindowType FocusedWindowDur PrevFocusedWinFeatureDur * MostFocusedWinTime(30) MostFocusedWinTime(600) WinSwitchesCountFeature(30) * WinSwitchesCountFeature(600) MostFocusedWinType(60) MostFocusedWinType(300)Nom * MouseEventCountFeature(30) MouseDistanceFeature(60) * MouseDistanceFeature(600) KBCountFeature(30) * KBCountFeature(600) “those in the back can’t see, and those in the front can’t understand…” Robert Kraut

33 a closer look (new! not in the paper)  work fragmentation  longer time in previous app …. slower  more switching (30sec) …. faster  longer mouse movements (60sec) …. faster  more keyboard activity (30 sec) …. faster  more message windows …. slower  longer time since messaging with buddy… faster  buddy ID had significant effect

34 implications for practice (in the paper)

35 implications for practice  preserving plausible deniability  making predictions about the receiver, visible to the receiver  multiple concurrent levels of responsiveness

36  presented statistical models that accurately predict responsiveness to incoming IM based on naturally occurring behavior  we plan to examine using message-content to improve modeling summary & future work awareness message  intercept  alert  mask  enhance

37 we would like to thank  Mike T (Terry)  James Fogarty  Darren Gergle  Laura Dabbish, and  Jennifer Lai

38 this work was funded in part by NSF Grants IIS , IIS , and by DARPA Contract No. NBCHD thank you for more info visit: or

39 FeatureEstimateFp buddyName[Group,SN] * log(timeSincOMsgBdy) * PrevFocusedWinFeatureDur * MessageWindowsCount * WinSwitchesCountFeature(30) * MouseDistanceFeature(60) * KBCountFeature(30) * MostFocusedWinType(300) *


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