Inferring colocation and conversation networks from privacy-sensitive audio with implications for computational social science

Abstract

New technologies have made it possible to collect information about social networks as they are acted and observed in the wild, instead of as they are reported in retrospective surveys. These technologies offer opportunities to address many new research questions: How can meaningful information about social interaction be extracted from automatically recorded raw data on human behavior? What can we learn about social networks from such fine-grained behavioral data? And how can all of this be done while protecting privacy? With the goal of addressing these questions, this article presents new methods for inferring colocation and conversation networks from privacysensitive audio. These methods are applied in a study of face-to-face interactions among 24 students in a graduate school cohort during an academic year. The resulting analysis shows that networks derived from colocation and conversation inferences are quite different. This distinction can inform future research in computational social science, especially work that only measures colocation or employs colocation data as a proxy for conversation networks. Permission to make digital or hard copies of part or all 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 show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) Wyatt, D., Choudhury, T., Bilmes, J., and Kitts, J. A. 2011. Inferring colocation and conversation networks from privacy-sensitive audio with implications for computational social science. ACM

Sensing, Understanding, and Shaping Social Behavior

“…Call and SMS interactions on the contrary, require explicit actions by the interacting sides. These results corroborate the findings of [94] that networks derived from co-location and face-to-face conversations may be quite different. Table 3 summarizes the results of the 16 experiments together with the results of the four ZeroR experiments.…”

A Survey on Mobile Social Signal Processing

“…The first layer of the model infers regarding voice existence and the second layer speech occurrence. This technique was adopted by [Choudhury and Basu 2004], Vibefones [Madan and Pentland 2006], StressSense , MeetingMediator [Kim et al 2008], [Wyatt et al 2011]. Another technique widely used by systems such as SpeakerSense and Auditeur [Nirjon et al 2013] is to calculate the ZCR of an audio frame and then apply a classification method to infer if the segment contains speech [Saunders 1996].…”

Sensing the "Health State" of a Community

“…Some recent examples include automatically inferring co-location and conversational networks [21], linking social diversity and economic progress [10], automatic activity and event classification for mass market phones [17], identifying transportation modes [19], as well as feedback tools for improving health and fitness [8] and for modeling human mobility patterns [13].…”