Quantifying Social Influence in an Online Cultural Market

By Coco Krumme, Manuel Cebrián, Galen Pickard, Sandy Pentland. 2012

71 citations recorded by the source corpus.

We revisit experimental data from an online cultural market in which 14,000 users interact to download songs, and develop a simple model that can explain seemingly complex outcomes. Our results suggest that individual behavior is characterized by a two-step process–the decision to sample and the decision to download a song. Contrary to conventional wisdom, social influence is material to the first step only. The model also identifies the role of placement in mediating social signals, and suggests that in this market with anonymous feedback cues, social influence serves an informational rather than normative role.

Subfields: Safety Research, Sociology and Political Science, Statistical and Nonlinear Physics

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Citations in the corpus

How algorithmic popularity bias hinders or promotes quality (2017, background): “Furthermore, knowledge of what is popular can be construed as a form of social influence; an individual’s behavior may be guided by choices of peers or neighbors [23, 19, 20, 18, 29, 2, 6].”
Leveraging Position Bias to Improve Peer Recommendation (2014, background): “, Digg [17]), and online cultural marketplaces [16,18].”
Aligning Popularity and Quality in Online Cultural Markets (2021, methodology): “The computational results use settings that model the MUSICLAB experiments discussed in (Salganik, Dodds, and Watts 2006; Krumme et al. 2012; Abeliuk et al. 2015a).”
The Benefits of Social Influence in Optimized Cultural Markets (2015, methodology): “These experimental results received further theoretical support by the development of a generative model that reproduces the experimental data [14].”
Popularity and Quality in Social News Aggregators: A Study of Reddit and Hacker News (2015, methodology): “Prior work (Hogg and Lerman 2014), (Muchnik, Aral, and Taylor 2013),(Krumme et al. 2012), (Salganik, Dodds, and Watts 2006) shows these signals can have signiﬁcant effects on user viewing and voting behavior.”
Disentangling the Effects of Social Signals (2014, methodology): “Second, they may change individual preferences in users’ evaluation of stories (Krumme et al., 2012).”
Taming the Unpredictability of Cultural Markets with Social Influence (2017, methodology): “As a result, item ranking can create as much inequality and unpredictability as was observed in the MusicLab study, even in the absence of social influence.”