Synthesized Social Signals: Computationally-Derived Social Signals from Account Histories

要旨

Social signals are crucial when we decide if we want to interact with someone online. However, social signals are typically limited to the few that platform designers provide, and most can be easily manipulated. In this paper, we propose a new idea called synthesized social signals (S3s): social signals computationally derived from an account's history, and then rendered into the profile. Unlike conventional social signals such as profile bios, S3s use computational summarization to reduce receiver costs and raise the cost of faking signals. To demonstrate and explore the concept, we built Sig, an extensible Chrome extension that computes and visualizes S3s. After a formative study, we conducted a field deployment of Sig on Twitter, targeting two well-known problems on social media: toxic accounts and misinformation. Results show that Sig reduced receiver costs, added important signals beyond conventionally available ones, and that a few users felt safer using Twitter as a result. We conclude by reflecting on the opportunities and challenges S3s provide for augmenting interaction on social platforms.

キーワード
social computing
social signals
social platform
social media
著者
Jane Im
University of Michigan – Ann Arbor, Ann Arbor, MI, USA
Sonali Tandon
University of Michigan – Ann Arbor, Ann Arbor, MI, USA
Eshwar Chandrasekharan
Georgia Institute of Technology, Atlanta, GA, USA
Taylor Denby
University of Michigan – Ann Arbor, Ann Arbor, MI, USA
Eric Gilbert
University of Michigan – Ann Arbor, Ann Arbor, MI, USA
DOI

10.1145/3313831.3376383

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems

セッション: Speech & language

Paper session
Paper
312 NI'IHAU
2020-04-29 20:00:00
2020-04-29 21:15:00
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