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Evolution of Conversations in the Age of Email Overload

Published: 18 May 2015 Publication History

Abstract

Email is a ubiquitous communications tool in the workplace and plays an important role in social interactions. Previous studies of email were largely based on surveys and limited to relatively small populations of email users within organizations. In this paper, we report results of a large-scale study of more than 2 million users exchanging 16 billion emails over several months. We quantitatively characterize the replying behavior in conversations within pairs of users. In particular, we study the time it takes the user to reply to a received message and the length of the reply sent. We consider a variety of factors that affect the reply time and length, such as the stage of the conversation, user demographics, and use of portable devices. In addition, we study how increasing load affects emailing behavior. We find that as users receive more email messages in a day, they reply to a smaller fraction of them, using shorter replies. However, their responsiveness remains intact, and they may even reply to emails faster. Finally, we predict the time to reply, length of reply, and whether the reply ends a conversation. We demonstrate considerable improvement over the baseline in all three prediction tasks, showing the significant role that the factors that we uncover play, in determining replying behavior. We rank these factors based on their predictive power. Our findings have important implications for understanding human behavior and designing better email management applications for tasks like ranking unread emails.

References

[1]
L. M. Aiello, R. Schifanella, and B. State. Reading the source code of social ties. In Proceedings of the 2014 ACM Conference on Web Science, WebSci '14, pages 139--148, New York, NY, USA, 2014. ACM.
[2]
L. Backstrom, J. Kleinberg, L. Lee, and C. Danescu-Niculescu-Mizil. Characterizing and Curating Conversation Threads: Expansion, Focus, Volume, Re-entry. In WSDM, 2013.
[3]
A. L. Barabasi. The origin of bursts and heavy tails in human dynamics. Nature, 435:207--211, 2005.
[4]
Y. Bengio, H. Schwenk, J.-S. Senécal, F. Morin, and J.-L. Gauvain. Neural probabilistic language models. In Innovations in Machine Learning, pages 137--186. Springer, 2006.
[5]
C. Bird, A. Gourley, P. Devanbu, M. Gertz, and A. Swaminathan. Mining email social networks. In MSR, pages 137--143, New York, NY, USA, 2006. ACM.
[6]
d. boyd, S. Golder, and G. Lotan. Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter. In HICSS, 2010.
[7]
C. Budak and R. Agrawal. On Participation in Group Chats on Twitter. In WWW, 2013.
[8]
C. S. Campbell, P. P. Maglio, A. Cozzi, and B. Dom. Expertise identification using email communications. In CIKM, pages 528--531, New York, NY, USA, 2003. ACM.
[9]
F. Celli and L. Rossi. The Role of Emotional Stability in Twitter Conversations. In Workshop on Semantic Analysis in Social Media, 2012.
[10]
T. Correa, A. W. Hinsley, and H. G. de Zúñiga. Who Interacts on the Web? The Intersection of Users' Personality and Social Media Use. Computers in Human Behaviour, 26(2), 2010.
[11]
L. A. Dabbish and R. E. Kraut. Email overload at work: An analysis of factors associated with email strain. In CSCW, pages 431--440, 2006.
[12]
L. A. Dabbish, R. E. Kraut, S. Fussell, and S. Kiesler. Understanding email use: Predicting action on a message. In CHI, 2005.
[13]
L. A. Dabbish, R. E. Kraut, S. Fussell, and S. Kiesler. Understanding email use: Predicting action on a message. In CHI, pages 691--700, New York, NY, USA, 2005. ACM.
[14]
C. Danescu-Niculescu-Mizil, L. Lee, B. Pang, and J. Kleinberg. Echoes of power: Language effects and power differences in social interaction. In WWW, pages 699--708. ACM, 2012.
[15]
M. De Choudhury, H. Sundaram, A. John, and D. D. Seligmann. What Makes Conversations Interesting? Themes, Participants and Consequences of Conversations in Online Social Media. In WWW, 2009.
[16]
J. Diesner, T. Frantz, and K. Carley. Communication Networks from the Enron Email Corpus: "It's Always About the People. Enron is no Different.". Computational & Mathematical Organization Theory, 11(3):201--228, 2005.
[17]
J.-P. Eckmann, E. Moses, and D. Sergi. Entropy of dialogues creates coherent structures in e-mail traffic. Proceedings of the National Academy of Sciences of the United States of America, 101(40):14333--14337, Oct. 2004.
[18]
D. Fisher, A. J. Brush, E. Gleave, and M. A. Smith. Revisiting whittaker & sidner's "email overload" ten years later. In CSCW, pages 309--312, New York, NY, USA, 2006. ACM.
[19]
S. Gao, G. Ver Steeg, and A. Galstyan. Explaining away stylistic coordination in dialogues. In WIN, 2013.
[20]
M. Gomez-Rodriguez, K. P. Gummadi, and B. Sch\:olkopf. Quantifying information overload in social media and its impact on social contagions. In ICWSM, 2014.
[21]
B. Gonçalves, N. Perra, and A. Vespignani. Modeling users' activity on twitter networks: Validation of dunbar's number. PLoS ONE, 6(8), 2011.
[22]
M. Grbovic, G. Halawi, Z. Karnin, and Y. Maarek. How many folders do you really need?: Classifying email into a handful of categories. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pages 869--878. ACM, 2014.
[23]
F. M. Harper, D. Frankowski, S. Drenner, Y. Ren, S. Kiesler, L. Terveen, R. Kraut, and J. Riedl. Talk Amongst Yourselves: Inviting Users to Participate in Online Conversations. In IUI, 2007.
[24]
N. O. Hodas and K. Lerman. How limited visibility and divided attention constrain social contagion. In SocialCom, 2012.
[25]
T. N. Jagatic, N. A. Johnson, M. Jakobsson, and F. Menczer. Social phishing. Commun. ACM, 50(10):94--100, Oct. 2007.
[26]
A. Java, X. Song, T. Finin, and B. Tseng. Why We Twitter: Understanding Microblogging Usage and Communities. In WebKDD/SNA-KDD, 2007.
[27]
S. Kim, J. Bak, and A. Oh. Do You Feel what I Feel? Social Aspects of Emotions in Twitter Conversations. In ICWSM, 2012.
[28]
B. Klimt and Y. Yang. The enron corpus: A new dataset for email classification research. In J.-F. Boulicaut, F. Esposito, F. Giannotti, and D. Pedreschi, editors, ECML, volume 3201, pages 217--226. Springer Berlin Heidelberg, 2004.
[29]
B. Klimt and Y. Yang. Introducing the enron corpus. In CEAS, 2004.
[30]
R. Kumar, M. Mahdian, and M. McGlohon. Dynamics of Conversations. In KDD, 2010.
[31]
Q. V. Le and T. Mikolov. Distributed representations of sentences and documents. arXiv preprint arXiv:1405.4053, 2014.
[32]
R. D. Malmgren, D. B. Stouffer, A. E. Motter, and L. A. N. Amaral. A poissonian explanation for heavy tails in e-mail communication. Proceedings of the National Academy of Sciences, 105(47):18153--18158, 2008.
[33]
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In NIPS, pages 3111--3119, 2013.
[34]
C. Neustaedter, A. J. B. Brush, and M. A. Smith. Beyond from and received: Exploring the dynamics of email triage. In CHI, 2005.
[35]
M. E. J. Newman, S. Forrest, and J. Balthrop. Email networks and the spread of computer viruses. Phys. Rev. E, 66:035101, Sep 2002.
[36]
J. E. Phelps, R. Lewis, L. Mobilio, D. Perry, and N. Raman. Viral marketing or electronic word-of-mouth advertising: Examining consumer responses and motivations to pass along email. Journal of Advertising Research, 44:333--348, 12 2004.
[37]
H. Purohit, Y. Ruan, D. Fuhry, S. Parthasarathy, and A. Sheth. On Understanding Divergence of Online Social Group Discussion. In ICWSM, 2014.
[38]
J. Shetty and J. Adibi. The Enron email dataset database schema and brief statistical report. Information Sciences Institute Technical Report, University of Southern California, 2004.
[39]
J. Tyler and J. Tang. When can i expect an email response? a study of rhythms in email usage. In K. Kuutti, E. Karsten, G. Fitzpatrick, P. Dourish, and K. Schmidt, editors, ECSCW, pages 239--258. Springer Netherlands, 2003.
[40]
S. Whittaker and C. Sidner. Email overload: Exploring personal information management of email. In CHI, pages 276--283, New York, NY, USA, 1996. ACM.
[41]
L. Zhuang, J. Dunagan, D. R. Simon, H. J. Wang, and J. D. Tygar. Characterizing botnets from email spam records. In LEET, pages 2:1--2:9, Berkeley, CA, USA, 2008. USENIX Association.

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    WWW '15: Proceedings of the 24th International Conference on World Wide Web
    May 2015
    1460 pages
    ISBN:9781450334693

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    • IW3C2: International World Wide Web Conference Committee

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    International World Wide Web Conferences Steering Committee

    Republic and Canton of Geneva, Switzerland

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    Published: 18 May 2015

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    Author Tags

    1. emailing behavior
    2. information overload
    3. prediction

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    • AFOSR
    • NSF
    • DARPA

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    WWW '15
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    • IW3C2

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    WWW '15 Paper Acceptance Rate 131 of 929 submissions, 14%;
    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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