Social Influence Bias: A Randomized Experiment

By Lev Muchnik, Sinan Aral, Sean J. Taylor. 2013

813 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics, AI Ethics, Fairness & Society, Social Sciences & Humanities

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “To do so, the gold standard are randomized controlled experiments [Aral and Walker 2011; Bakshy et al. 2012; Muchnik et al. 2013], but when experimentation is impractical or violates ethical standards, many studies resort to observational data collected from social platforms.”
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): “We experimentally evaluate the effect of position bias, in contrast to previous studies of social influence [2,3,5,16].”
Searching for superspreaders of information in real-world social media (2014, background): “Information spreading is an ubiquitous process in society which describes a wide variety of phenomena ranging from the adoption of innovations [1], the success of commercial promotions [2], the rise of political movements [3], and the spread of news, opinions and brand new products in society [4, 5]”
The Benefits of Social Influence in Optimized Cultural Markets (2015, result): “In particular, Salganik et al suggested that social influence contributes to both unpredictability and inequality of success, with follow-up experiments confirming these initial findings [9–12] (see also [13] for a perspective on the MUSICLAB paper).”
Navigating by the Stars: Investigating the Actual and Perceived Validity of Online User Ratings (2016, background): “…not actually used the product (Anderson and Simester 2014), and raters that have actually used the product are influenced by previously posted ratings from other consumers and experts, creating herding effects (Jacobsen 2015; Moe and Trusov 2011; Muchnik, Aral, and Taylor 2013; Schlosser 2005).”
Evaluating Machines by their Real-World Language Use (2020, background): “This suggests that our workers are intrinsically mo- 5This is somewhat of a simplification, as other factors also influence what gets upvoted (Anderson et al., 2012; Lakkaraju et al., 2013; Muchnik et al., 2013; Jaech et al., 2015).”
Popularity and Quality in Social News Aggregators: A Study of Reddit and Hacker News (2015, background): “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.”
Evidence of Online Performance Deterioration in User Sessions on Reddit (2016, background): “, in light of potential social influence bias (herding) effects [27].”
Quantifying herding effects in crowd wisdom (2014, background): “Thanks to the availability of Web-based experiments, recent studies offered convincing evidence that social influence exerts important but counterintuitive effects on collective judgement [16, 19].”
Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment (2014, background): “, Aral and Walker 2011a, 2012); Amazon Mechanical Turk enables the development of complex environments in which users can engage in precisely defined experimental microtasks (Mason and Watts 2012, Rand and Nowak 2011, Suri and Watts 2011); and formal collaboration with platform developers and websit”
What makes a great MOOC? An interdisciplinary analysis of student retention in online courses (2013, background): “Finally, the hypothesis that user-generated reviews affect user choices has also received strong support in empirical studies (Dhar and Chang 2009; Ghose et al. 2012; Liu 2001; Muchnik et al. 2013).”
Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions (2017, methodology): “An experiment that manipulated the initial votes an article received found that initial downvotes tended to be corrected by the community [64].”
Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features (2019, background): “Indeed, experiments have shown that, to a certain extent, the influence of early opinions on subsequent opinion dynamics can override the influence of an item’s actual content (Salganik et al., 2006; Wu and Huberman, 2008; Muchnik et al., 2013; Weninger et al., 2015).”