Responsiveness in instant messaging: predictive models supporting inter-personal communication

  • Authors:
  • Daniel Avrahami;Scott E. Hudson

  • Affiliations:
  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2006

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Abstract

For the majority of us, inter-personal communication is an essential part of our daily lives. Instant Messaging, or IM, has been growing in popularity for personal and work-related communication. The low cost of sending a message, combined with the limited awareness provided by current IM systems result in messages often arriving at inconvenient or disruptive times. In a step towards solving this problem, we created statistical models that successfully predict responsiveness to incoming instant messages -- simply put: whether the receiver is likely to respond to a message within a certain time period. These models were constructed using a large corpus of real IM interaction collected from 16 participants, including over 90,000 messages. The models we present can predict, with accuracy as high as 90.1%, whether a message sent to begin a new session of communication would get a response within 30 seconds, 1, 2, 5, and 10 minutes. This type of prediction can be used, for example, to drive online-status indicators, or in services aimed at finding potential communicators.