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Features and Classes Enhancing Technology-Mediated Communication: Tools, Analyses, and Predictive Models Daniel Avrahami דניאל אברהמי September 2007 CMU-HCII-07-102 Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Scott E. Hudson (Chair), Carnegie Mellon University Susan Fussell, Carnegie Mellon University Robert Kraut, Carnegie Mellon University Eric Horvitz, Microsoft Research Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Copyright © 2007 Daniel Avrahami. All rights reserved. This work was supported by a Graduate Fellowship from Carnegie Mellon University, and supported in part by the National Science Foundation under grants IIS-9800597, IIS 0121560 and IIS 0325351, and by the Defense Advanced Research Projects Agency (DARPA) under Contract No. NBCHD030010. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author and do not necessarily reflect those of the funding agencies. Keywords: Interpersonal communication, technology-mediated communication, computer-supported cooperative work, CSCW, computer-mediated communication, CMC, interruption, responsiveness, availability, interpersonal relationships, predictive statistical models, task-switching. ii Abstract For most of us, interpersonal communication is at the center of our professional and personal lives. With the growing distribution of business organizations and of our social networks, so grows the need for and use of communication technologies. Many of today’s communication tools, however, suffer from a number of shortcomings. For example, the inherent discrepancy between one’s desire to initiate communication and another’s ability or desire to receive it, often leads to unwanted interruptions on the one hand, or failed communication on the other. I have taken an interdisciplinary approach to address these shortcomings, and also in order to provide a better understanding of human behavior and the use of communication tools, combining tool-building and the creation of predictive models, with investigation and analysis of large volumes of field data. At the focus of this dissertation is my research on Instant Messaging (IM) communication, a popular, interesting, and highly observable point on the continuum between synchronous and asynchronous communication mediums. I present the creation of a set of statistical models that are able to predict, with high accuracy, users’ responsiveness to incoming communication. A quantitative analysis complements these models by revealing major factors that influence responsiveness, illuminating its role in IM communication. I then describe an investigation of the effect of interpersonal relationships on communication, and statistical models that can predict these relationships. Finally, I describe a tool I have created that allows users to balance their responsiveness to IM with their ability to stay on task. iii iv Acknowledgements I will start by thanking my advisor Scott, for mentoring me throughout my time in graduate school. Scott has taught me many things about research and being a researcher. He taught me about academia, and taught me about teaching. Finally, I thank him for showing patience when I was searching for my own research path. His advising style, which I was lucky to experience, comes directly from his being a good and kind person. I have enjoyed working with him over the past years and I hope that we will be able to collaborate again in the future. I also thank my other committee members, Robert Kraut, Eric Horvitz, and Sue Fussell for comments and suggestions that have undoubtedly improved this dissertation. I am indebted to Sue Fussell. When Scott was on sabbatical and I needed help, she was always willing to spend her time, providing me with support and great advice. Thank you Sue. To my mother Judith Avrahami and my step-father Yaakov Kareev, I owe my interest in research and my pursuit of graduate studies (yes, I also owe them for their help with statistics). By introducing me to computers and to cognitive psychology they can be credited (or blamed) for my belonging to the field of Human-Computer Interaction. A big thank you to Darren Gergle. First and foremost for his friendship -- he played a big part in making graduate school a fun and memorable experience. I should also thank Darren for his advice and help with the work and writing, and for his words of encouragement when I (as one is bound to) got tired of the process. We should write another journal paper together so it can be printed when his first grandchild is born. v To the HCII faculty and staff for providing an engaging and challenging research environment which I was glad to be part of. Acknowledging that experts may be located at other departments across campus, encouraging interdisciplinary collaborations, is important and admirable. A special thank you goes to Marian D’Amico. Had it not been for her competence and swift communication, I would have gone to a different university, and for that I thank her. Many others have helped me with this work, either directly, through good advice, or through suggestions on unfinished manuscripts. These include my friends, peers, and future colleagues Aaron Bauer, Daniel Boyarski, Laura Dabbish, James Fogarty, Jodi Forlizzi, Gary Hsieh, Sara Kiesler, Andy Ko, Jennifer Lai, Alon Lavie, Johnny Lee, Joonhwan Lee, Jeff Nichols, Roni Rosenfeld, Mike Terry, Irina Shklovski, and Jake Wobbrock. Thank you to Anind Day and Jen Mankoff. Their comments on the presentation of this work were instrumental in my pursuit of a research position. This long process wouldn’t have been possible without the support of many good friends. In particular, thank you Carl DiSalvo, Gillian Hayes, Julie Kientz, Yarden Kedar and the clan, Guy Itzhaki, Yoav Etsion, Jess Bauer, Peter Scupelli, Kelly Scupelli, Joe Tullio, Ian Li, and Paul Burkhead. A big thank you to my family. Ari, Frieda, Ruth, Emanuel, Doda Rachel, Safta Elise, Safta Riva, Saba Adi, Daniella, Moshe, Soroko, Noa, and Ami. I love you all very much. Finally, a thank you to my wife Thi. For her support, for her patience, for her comments and suggestions (always right), for making me happy, for her love, and for Noam. vi To my family באהבה viii Table of Contents Abstract .......................................................................................... iii Acknowledgements ...........................................................................v List of Figures .................................................................................xv List of Tables .................................................................................xix 1 Introduction.............................................................................. 1 1.1 Dissertation Outline............................................................................................. 3 2 Background ............................................................................... 5 2.1 The Disruptive Nature of Communication .......................................................... 6 2.2 Combating Interruptions ................................................................................... 10 2.2.1 Interruption Timing and Task Boundaries........................................................ 10 2.2.2 Interruption Presentation ................................................................................. 12 2.2.3 Awareness and Contextual Information............................................................. 13 2.3 Systems and Modeling Approaches .................................................................... 16 2.4 Between Asynchronous and Synchronous Communication................................ 21 3 Predicting Responsiveness to IM.............................................. 29 3.1 Motivation ......................................................................................................... 29 3.1.1 From Availability to Responsiveness .................................................................. 31 3.1.2 Behavior as Ground Truth............................................................................... 32 3.2 Data Collection Method .................................................................................... 35 3.2.1 Privacy of Data ............................................................................................... 38 ix 3.3 Participants ........................................................................................................ 39 3.4 Data Overview ................................................................................................... 41 3.5 Defining IM Sessions ......................................................................................... 44 3.6 Session Initiation Attempts (SIA) ....................................................................... 44 3.7 Features and Classes ........................................................................................... 47 3.7.1 Features .......................................................................................................... 47 3.7.2 What is Predicted? (Classes).............................................................................. 48 3.8 Models Performance .......................................................................................... 48 3.8.1 Buddy-Independent Models .............................................................................. 51 3.9 A Closer Look at Selected
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