Identifying Influential and Susceptible Members of Social Networks

By Sinan Aral, Dylan Walker. Science, 2012

1,057 citations recorded by the source corpus.

Subfields: Communication, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/7945aa5d69fd0a516c321f005a89a2d0b9a72fa8

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In Communication

In Statistical and Nonlinear Physics

Citations in the corpus

Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks (2014, background): “…side of the equation—whether better connected adopters exert more inﬂuence than do less connected ones—we position ourselves on the susceptibility to inﬂuence side of that equation, since peer inﬂuence also depends on the susceptibility of the individual being inﬂuenced Aral and Walker (2012).”
The Diffusion of Microfinance (2013, positive): “For experimental approaches to controlling for and analyzing the role of homophily in diffusion, see (26, 27).”
Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment (2017, background): “This tested the ‘‘influentials hypothesis’’, that influential individuals are crucial for driving changes in norms of behavior in society (Aral and Walker 2012).”
Characterizing the 2016 Russian IRA influence campaign (2018, background): “Social media have been proven to be an effective tool to influence individuals’ opinions and behaviors Aral and Walker (2012); Aral et al. (2009); Bakshy et al.”
Avatar characteristics induce users’ behavioral conformity with small-to-medium effect sizes: a meta-analysis of the proteus effect (2019, background): “Namely, just as with age, research suggests that susceptibility to social influence may differ by gender (Aral & Walker, 2012; Whittaker, 1965), especially as related to technology use (Venkatesh & Morris, 2000).”
Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment (2014, methodology): “Prior research has demonstrated that, in spreading processes, not all individuals are created equal (by analyzing how individuals’ attributes moderate diffusion; Aral and Walker 2012).”
“Chatting with ChatGPT”: Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model (2023, result): “This is in tune with the prior literature, which suggested that social media more influence youngsters than older users [123].”
Neural Correlates of Susceptibility to Group Opinions in Online Word-of-Mouth Recommendations (2015, background): “A successful campaign launch depends on having a good product or idea coupled with the right community of people to spread and reinforce information in the most direct way (Aral and Walker 2011, 2012; Hinz et al. 2011; Van der Lans et al. 2010; Watts and Dodds 2007).”
Detecting Emotional Contagion in Massive Social Networks (2014, methodology): “This problem has been addressed by using experimental methods to estimate network effects [17,18,19,20,21,22], but these methods are either limited in scale and external validity, or they require very close collaboration with private companies, which means there are limited opportunities to conduct ”
Disinformation and social bot operations in the run up to the 2017 French presidential election (2017, background): “…less followers and more followees), the number of times a user has been added to a public list – human users are often considered more influential (Aral, & Walker, 2012) and their content more “contagious” (Kramer, et al., 2014; Ferrara & Yang, 2015a; Ferrara & Yang, 2015b; Mønsted, et al.,…”
Peer Effects in Product Adoption (2019, positive): “…shown to influence consumption choices (e.g., Goolsbee and Klenow, 2002; Mobius, Niehaus, and Rosenblat, 2005; Kuhn et al., 2011; Moretti, 2011; Aral and Walker, 2012; Gilchrist and Sands, 2016; De Giorgi, Frederiksen, and Pistaferri, 2016; Han, Hirshleifer, and Walden, 2016) as well as a…”
Introduction to the Special Issue - Social Media and Business Transformation: A Framework for Research (2013, background): “…have been integral to recent advances in relational inference about consumer preferences (Hill et al. 2006, Aral et al. 2009, Trusov et al. 2010), novel peer-to-peer and targeted marketing techniques (Aral and Walker 2011, 2012), and demand prediction (Asur and Huberman 2010, Bollen et al. 2011).”