Social Influence Bias : A Randomized Experiment

By Yanqin Li, Xu Zhang, Chun Liu, Jingyang Guan, Honggang Li, Ting Zhao, Junqing Ye, Wei-feng Yang, Kang Liu, Jian Ge and 3 more. 2014

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “The gold standard for such causal analyses are randomized controlled experiments (Aral and Walker, 2011; Muchnik et al., 2013).”
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].”
Why Amazon's Ratings Might Mislead You: The Story of Herding Effects (2014, neutral): “7,8 Through carefully designed control experiments in different settings, these studies demonstrate that disclosing prior collective opinions distorts individuals’ decision making as well as their perceptions of quality and value, creating herding that is irrational and pervasive, yet consequential…”
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.”
Identity and Opinion: A Randomized Experiment (2014, unknown): “…in the impact of inﬂuence mediating messages on diﬀerent types of people among millions of Facebook users (Aral and Walker 2012) and whether opinion change or selective turnout creates social inﬂuence bias in online ratings that were seen tens of millions of times (Muchnik et al. 2013).”
Emoticons vs. Emojis on Twitter: A Causal Inference Approach (2015, methodology): “Although causal inference framework has been widely used in ﬁelds such as epidemiology and political science to design and analyze randomized experiments, this approach was rare in social media research until recently (Muchnik, Aral, and Taylor 2013; King, Pan, and Roberts 2014).”
Research Dilemmas with Behavioral Big Data (2017, unknown): “in BBD experiments include manipulating ratings of news article comments 2 ; providing only the treatment group with a prestige good 1 ; gifting an anonymous browsing feature in an online dating site to a treatment group, while the control group continued to browse nonanonymously 11 and…”
Quantifying herding effects in crowd wisdom (2014, background): “These observations are consistent with the ﬁnding of the asymmetric herding eﬀects of positive and negative prior opinions as reported in [16].”
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 in-ﬂuence of an item’s actual content (Salganik et al., 2006; Wu and Huberman, 2008; Muchnik et al., 2013; Weninger et al., 2015).”
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).”