research-article

Mining smartphone data to classify life-facets of social relationships

Authors:

John ZimmermanAuthors Info & Claims

CSCW '13: Proceedings of the 2013 conference on Computer supported cooperative work

Pages 285 - 294

https://doi.org/10.1145/2441776.2441810

Published: 23 February 2013 Publication History

Abstract

People engage with many overlapping social networks and enact diverse social roles across different facets of their lives. Unfortunately, many online social networking services reduce most people's contacts to "friend". A richer computational model of relationships would be useful for a number of applications such as managing privacy settings and organizing communications. In this paper, we take a step towards a richer computational model by using call and text message logs from mobile phones to classifying contacts according to life facet (family, work, and social). We extract various features such as communication intensity, regularity, medium, and temporal tendency, and classify the relationships using machine-learning techniques. Our experimental results on 40 users showed that we could classify life facets with up to 90.5% accuracy. The most relevant features include call duration, channel selection, and time of day for the communication.

References

[1]

Ackerman, J. M., Kenrick, D. T., and Schaller, M. Is friendship akin to kinship? Evolution and Human Behavior, 28 (2007), 365--374.

[2]

Altshuler, Y., Fire, M., Aharony, N., Elovici, Y., and Pentland, A. How many makes a crowd? On the evolution of learning as a factor of community coverage. In Proc. Int. Conf. on Social Computing, Behavioral-Cultural Modeling, and Prediction (2012), 43--52.

Digital Library

[3]

Backstrom, L., Huttenlocher, D., Kleinberg, J., and Lan, X. Group formation in large social networks: membership, growth, and evolution. In Proc. KDD '06 (2006), 44--54.

Digital Library

[4]

Battestini, A., Setlur, V., and Sohn, T. A large scale study of text-messaging use. In Proc. MobileHCI '10 (2010), 229--238.

Digital Library

[5]

Burke, M. Reading, Writing, Relationships: The Impact of Social Network Sites on Relationships and Well-Being. Carnegie Mellon University, Pittsburgh, Pennsylvania, 2011.

[6]

Chau, M. and Xu, J. Mining communities and their relationships in blogs: A study of online hate groups. Int. J. of Human-Computer Studies, 65 (2007), 57--70.

Digital Library

[7]

Chittaranjan, G., Blom, J., and Gatica-Perez, D. Mining large-scale smartphone data for personality studies. Personal and Ubiquitous Computing, (in Press).

Digital Library

[8]

Christakis, N. A. and Fowler, J. H. Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives. Little, Brown and Company, New York, 2009.

[9]

Clark, S. C. Work/family border theory: A new theory of work/family balance. Human Relations, 53, (2000), 747--770.

[10]

Cranshaw, J., Toch, E., Hong, J., Kittur, A., and Sadeh, N. Bridging the gap between physical location and online social networks. In Proc. Ubicomp '10 (2010), 119--128.

Digital Library

[11]

Cummings, J. N., Lee, J. B., and Kraut, R. Communication technology and friendship during the transition from high school to college. New York: Oxford University Press, City, 2006.

[12]

DiMicco, J. M. and Millen, D. R. Identity management: Multiple presentations of self in Facebook. In Proc. GROUP '07 (2007), 383--386.

Digital Library

[13]

Do, T. M. T. and Gatica-Perez, D. Human interaction discovery in smartphone proximity networks. Personal and Ubiquitous Computing, (in Press).

Digital Library

[14]

Dong, W., Lepri, B., and Pentland, A. Modeling the co-evolution of behaviors and social relationships using mobile phone data. In Proc. MUM '11 (2011), 134--143.

Digital Library

[15]

Eagle, N. and Pentland, A. Reality mining: Sensing complex social systems. Personal and Ubiquitous Computing, 10 (2006), 255--268.

Digital Library

[16]

Eagle, N., Pentland, A., and Lazer, D. Inferring social network structure using mobile phone data. Proc. National Academy of Sciences, 106, 36 (2009), 15274--15278.

[17]

Farnham, S. and Churchill, E. F. Faceted identity, faceted lives: Social and technical issues with being yourself online. In Proc. CSCW 2011 (2011), 359--368.

Digital Library

[18]

Gilbert, E. and Karahalios, K. Predicting tie strength with social media. In Proc. Int. Conf. on Human Factors in Computing Systems (2009), 211--220.

Digital Library

[19]

Granovetter, M. S. The strength of weak ties. The American Journal of Sociology, 78, 6 (1973), 1360--1380.

[20]

Grob, R., Kuhn, M., Wattenhofer, R., and Wirz, M. Cluestr: mobile social networking for enhanced group communication. In. Proc. Group '09 (2009), 81--90.

Digital Library

[21]

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. The WEKA data mining software: An update. SIGKDD Explorations, 11, 1 (2009), 10--18.

Digital Library

[22]

Hill, R. A. and Dunbar, R. I. M. Social network size in humans. Human Nature, 14 (2003), 53--72.

[23]

Http://www.cbsnews.com/8301-501465_162-20105561-501465.html

[24]

Jones, S. and O'Neill, E. Feasibility of structural network clustering for group-based privacy control in social networks. In Proc. SOUPS (2010), 1--13.

Digital Library

[25]

Kelley, P. G., Brewer, R., Mayer, Y., Cranor, L. F., and Sadeh, N. M. An Investigation into Facebook Friend Grouping. In Proc. INTERACT (2011), 216--233.

Digital Library

[26]

Lampinen, A., Tamminen, S., and Oulasvirta, A. All my people right here, right now: Management of group co-presence on a social networking site. In Proc. GROUP '09 (2009), 281--290.

Digital Library

[27]

Madan, A., Cebrian, M., Lazer, D., and Pentland, A. Social sensing for epidemiological behavior change. In Proc. Ubicomp '10 (2010), 291--300.

Digital Library

[28]

McCarty, C. Structure in personal networks. J. of Social Structure, 3 (2005).

[29]

Mesch, G. S. Social context and communication channels choice among adolescents. Computers in Human Behavior, 25 (2009), 244--251.

Digital Library

[30]

Min, J.-K. and Cho, S.-B. Mobile human network management and recommendation by probabilistic social mining. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 41, 3 (2011), 761--771.

Digital Library

[31]

Mok, D., Wellman, B., and Basu, R. Did distance matter before the Internet? Interpersonal contact and support in the 1970s. Social Networks, 29 (2007), 430--461.

[32]

Newman, M. E. J. Detecting community structure in networks. The European Physical Journal B - Condensed Matter and Complex Systems, 38 (2004), 321--330.

[33]

Nippert-Eng, C. E. Home and Work. The University of Chicago Press, 1995.

[34]

Olson, J., Grudin, J., and Horvitz, E. A study of preferences for sharing and privacy. In Proc. CHI '05 (2005), 1985--1988.

Digital Library

[35]

Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., Kertész, J., and Barabási, A.-L. Structure and tie strengths in mobile communication networks. Proc. National Academy of Sciences, 104 (2007), 7332--7336.

[36]

Ozenc, F. K. and Farnham, S. D. Life "modes" in social media. In Proc. CHI '11 (2011), 561--570.

Digital Library

[37]

Roberts, S. G. B. and Dunbar, R. I. M. Communication in social networks: Effects of kinship, network size, and emotional closeness. Personal Relationships, 18 (2011), 439--452.

[38]

Skeels, M. M. and Grudin, J. When social networks cross boundaries: A case study of workplace use of Facebook and LinkedIn. In Proc. GROUP '09 (2009), 95--104.

Digital Library

[39]

Spencer, L. and Pahl, R. E. Rethinking Friendship: Hidden Solidarities Today. Princeton University Press, 2006.

[40]

Tajfel, H. Differentiation Between Social Groups: Studies in the Social Psychology of Intergroup Relations. London: Academic Press, 1978.

[41]

Wiese, J., Kelley, P. G., Cranor, L. F., Dabbish, L., Hong, J. I., and Zimmerman, J. Are you close with me? Are you nearby? Investigating social groups, closeness, and willingness to share. In Proc. UbiComp '11 (2011), 197--206.

Digital Library

[42]

Wilson, C., Boe, B., Sala, A., Puttaswamy, K. P. N., and Zhao, B. Y. User interactions in social networks and their implications. In Proc. the 4th ACM European Conference on Computer Systems (2009), 205--218.

Digital Library

Cited By

Kqiku LReinhardt D(2023)Lengthy early morning instant messages reveal more than you think: Analysing interpersonal relationships using mobile communication metadataPervasive and Mobile Computing10.1016/j.pmcj.2023.10178191(101781)Online publication date: Apr-2023
https://doi.org/10.1016/j.pmcj.2023.101781
Lee HChiang YChou YLin KChang Y(2023)What makes IM users (un)responsiveInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2022.102983172:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.ijhcs.2022.102983
Honeycutt DSablon WJung DRedmond CSerna KLucas BCampisi J(2022)Lack of access to a phone and high social media use was associated with elevated physiological arousal in college students exposed to an acute laboratory stressorBIOS10.1893/BIOS-D-21-0001093:3Online publication date: 1-Sep-2022
https://doi.org/10.1893/BIOS-D-21-00010
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Index Terms

Mining smartphone data to classify life-facets of social relationships
1. Human-centered computing

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cover image ACM Conferences

CSCW '13: Proceedings of the 2013 conference on Computer supported cooperative work

February 2013

1594 pages

ISBN:9781450313315

DOI:10.1145/2441776

General Chairs:
Amy Bruckman
Georgia Institute of Technology, USA
,
Scott Counts
Microsoft Research, USA
,
Program Chairs:
Cliff Lampe
University of Michigan, USA
,
Loren Terveen
University of Minnesota, USA

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 February 2013

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CSCW '13: Computer Supported Cooperative Work

February 23 - 27, 2013

Texas, San Antonio, USA

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Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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Cited By

Kqiku LReinhardt D(2023)Lengthy early morning instant messages reveal more than you think: Analysing interpersonal relationships using mobile communication metadataPervasive and Mobile Computing10.1016/j.pmcj.2023.10178191(101781)Online publication date: Apr-2023
https://doi.org/10.1016/j.pmcj.2023.101781
Lee HChiang YChou YLin KChang Y(2023)What makes IM users (un)responsiveInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2022.102983172:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.ijhcs.2022.102983
Honeycutt DSablon WJung DRedmond CSerna KLucas BCampisi J(2022)Lack of access to a phone and high social media use was associated with elevated physiological arousal in college students exposed to an acute laboratory stressorBIOS10.1893/BIOS-D-21-0001093:3Online publication date: 1-Sep-2022
https://doi.org/10.1893/BIOS-D-21-00010
Wetzel BPryss RBaumeister HEdler JGonçalves ACohrdes C(2021)“How Come You Don’t Call Me?” Smartphone Communication App Usage as an Indicator of Loneliness and Social Well-Being across the Adult Lifespan during the COVID-19 PandemicInternational Journal of Environmental Research and Public Health10.3390/ijerph1812621218:12(6212)Online publication date: 8-Jun-2021
https://doi.org/10.3390/ijerph18126212
Lin KLee HChou YShih FChang Y(2021)Distinguishing IM Communication Patterns with Relationship and Conversation TopicsCompanion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3462204.3481752(121-125)Online publication date: 23-Oct-2021
https://dl.acm.org/doi/10.1145/3462204.3481752
Wiese J(2021)Personal Context from Mobile Phones—Case Study 5Intelligent Computing for Interactive System Design10.1145/3447404.3447424(341-376)Online publication date: 23-Feb-2021
https://dl.acm.org/doi/10.1145/3447404.3447424
Liu TNicholas JTheilig MGuntuku SKording KMohr DUngar L(2020)Machine Learning for Phone-Based Relationship EstimationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33698203:4(1-23)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.1145/3369820
Zhang JWang WXia FLin YTong H(2020)Data-driven Computational Social Science: A SurveyBig Data Research10.1016/j.bdr.2020.100145(100145)Online publication date: Aug-2020
https://doi.org/10.1016/j.bdr.2020.100145
NIU KJIAO HCHENG CZHANG HXU X(2019)NFRR: A Novel Family Relationship Recognition Algorithm Based on Telecom Social Network SpectrumIEICE Transactions on Information and Systems10.1587/transinf.2018DAP0008E102.D:4(759-767)Online publication date: 1-Apr-2019
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Lee HChen KLin CChang YHarle RFarrahi KLane N(2019)Connecting IM pattern and selective perceived responsiveness to relationshipAdjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers10.1145/3341162.3344841(1070-1074)Online publication date: 9-Sep-2019
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