Cyber - Project - Folk Theories of Cyber-Social Systems and their Implications for Privacy

Folk Theories of Cyber-Social Systems and their Implications for Privacy

As people interact with complex cyber-social systems such as Facebook’s ranked news feed and Uber’s hiring algorithms, they build up folk theories of how these systems work. These theories, however, can often be wrong. For example, many people believed the Facebook news feed to be an unfiltered window of their friends’ behavior, leading to widespread surprise and news coverage when a Facebook experiment on emotional contagion highlighted that Facebook manipulates the content of users’ feeds. We propose to investigate the folk theories that people hold about complex cyber-social systems, and determine whether users’ privacy behaviors on these systems are direct reflections of their folk theories. We then propose targeted design interventions to nudge users’ folk theories. This research highlights how systems and algorithms impact society not only through their direct outputs, but also through the (potentially problematic) understandings that people form of them.

Publications:

Miner AS, Milstein A, Hancock JT. Talking to Machines About Personal Mental Health Problems. JAMA.2017;318(13):1217–1218. doi:10.1001/jama.2017.14151
Annabell Ho, Jeff Hancock, Adam S Miner; Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations With a Chatbot, Journal of Communication, , jqy026, https://doi.org/10.1093/joc/jqy026
French, Megan and Hancock, Jeff, What's the Folk Theory? Reasoning About Cyber-Social Systems (February 2, 2017). Available at SSRN: https://ssrn.com/abstract=2910571
Amy S. Bruckman, Casey Fiesler, Jeff Hancock, and Cosmin Munteanu. 2017. CSCW Research Ethics Town Hall: Working Towards Community Norms. In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW '17 Companion). ACM, New York, NY, USA, 113-115. DOI: https://doi.org/10.1145/3022198.3022199
Xiao Ma, Jeff Hancock, and Mor Naaman. 2016. Anonymity, Intimacy and Self-Disclosure in Social Media. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16). ACM, New York, NY, USA, 3857-3869. DOI: https://doi.org/10.1145/2858036.2858414
Markowitz, David and Hancock, Jeff and Bailenson, Jeremy N. and Reeves, Byron, The Media Marshmallow Test: Psychological and Physiological Effects of Applying Self-Control to the Mobile Phone (November 14, 2017). Available at SSRN: https://ssrn.com/abstract=3086140 or http://dx.doi.org/10.2139/ssrn.3086140

Researchers

https://comm.stanford.edu/faculty-hancock/

Jeff Hancock

Professor of Communication

Jeff Hancock is founding director of the Stanford Social Media Lab and is a Professor in the Department of Communication at Stanford University. Professor Hancock and his group work on understanding psychological and interpersonal processes in social media. The team specializes in using computational linguistics and experiments to understand how the words we use can reveal psychological and social dynamics, such as deception and trust, emotional dynamics, intimacy and relationships, and social support. Recently Professor Hancock has begun work on understanding the mental models people have about algorithms in social media, as well as working on the ethical issues associated with computational social science.

http://hci.stanford.edu/msb/

Michael Bernstein

Assistant Professor of Computer Science

Michael Bernstein is an Assistant Professor of Computer Science at Stanford University, where he is a member of the Human-Computer Interaction group. His research focuses on the design of crowdsourcing and social computing systems. This work has received seven Best Paper awards and fourteen honorable mentions at premier venues in human-computer interaction. Michael has been recognized as a Robert N. Noyce Family Faculty Scholar, and has received an NSF CAREER award, Alfred P. Sloan Fellowship, and Outstanding Academic Title citation from the American Library Association. He holds a bachelor's degree in Symbolic Systems from Stanford University, and a master's and Ph.D. in Computer Science from MIT.