The role of social networks in information diffusion

By Eytan Bakshy, Itamar Rosenn, Cameron A. Marlow, Lada A. Adamic. 2012

1,651 citations recorded by the source corpus.

Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends' information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “, social networks structure and evolution, social influence, and information diffusion phenomena (Wilson et al., 2009; Cha et al., 2010; Bakshy et al., 2012).”
The diffusion of misinformation on social media: Temporal pattern, message, and source (2018, background): “Research shows that the majority of messages on Facebook and Twitter are shared within the first day of the original post (Bakshy et al., 2012, Kwak et al., 2010).”
Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science (2015, background): “Nevertheless, few studies found that retweetability is positively associated with the number of followers [40, 41], number of followees [41], the age of the account, and those users belonging to several groups who act as brokers [42].”
Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment (2014, background): “Macy 2011) and to conduct experiments that address endogeneity (e.g., Aral and Walker 2011a; Bakshy et al. 2012).”
Diffusion of Lexical Change in Social Media (2012, background): “A wave of recent research has shown how social media datasets can enable large-scale analysis of patterns of communication [10,11], sentiment [12–14], and influence [15–19].”
Design and Analysis of Experiments in Networks: Reducing Bias from Interference (2014, background): “Much of the empirical literature that considers peer effects in networks, whether field experiments (e.g., Aral and Walker, 2011; Bakshy et al., 2012; Bapna and Umyarov, 2012) or observational studies (e.g., Aral, Muchnik and Sundararajan, 2009; Goldsmith-Pinkham and Imbens, 2013) has aimed to…”
The Simple Rules of Social Contagion (2013, neutral): “A new message starts at the top of the queue, where it is highly likely to be seen, because users usually start browsing from the top of a page [5].”
Social big data: Recent achievements and new challenges (2015, background): “• Studying the dynamics of individual and group behaviour, characterising patterns of information diffusion, and identifying influential individuals in social networks and other social media-based applications [147] .”
Sharing with Friends versus Strangers: How Interpersonal Closeness Influences Word-of-Mouth Valence (2016, background): “…(Frenzen and Nakamoto 1993; Wilcox and Stephen 2013), subsequent empirical work has primarily viewed and assessed tie strength in terms of frequency of observable interactions between both sides of a social dyad (Bakshy et al. 2012; Brown and Reingen 1987; Godes and Mayzlin 2004; Weimann 1983).”
News Sharing in Social Media: A Review of Current Research on News Sharing Users, Content, and Networks (2015, neutral): “…opinion leadership (e.g., Hu et al., 2012; Ma, Lee, & Goh, 2013, 2014; Wu, Hofman, Mason, & Watts, 2011), tie strength and homophily (e.g., Bakshy, Rosenn, Marlow, & Adamic, 2012; Choudhury, Sundaram, John, Seligmann, & Kelliher, 2010; Ma et al., 2013, 2014; Susarla, Oh, & Tan, 2012),…”
Bias in algorithmic filtering and personalization (2013, background): “Facebook performed one of the few studies that actually studies bias in social networks (Bakshy 2012).”
Peer Effects in Product Adoption (2019, background): “1A vast, related literature has studied the diffusion process of beliefs or information through social networks (e.g., Valente, 1996; Jackson and Yariv, 2005; Young, 2009; Christakis and Fowler, 2013; Bakshy et al., 2012).”
Predicting emerging social conventions in online social networks (2012, background): “Even more importantly, Bakshy and colleagues [4] conducted an experiment on Facebook to demonstrate the role of social networks in information diffusion.”
Identity and Opinion: A Randomized Experiment (2014, neutral): “The procedure is fast, parallelizable, and was previously used in Bakshy et al. (2012a) to eﬃciently analyze an experiment with a similar dependency structure between observations.”
Social influence in social advertising: evidence from field experiments (2012, background): “For these reasons, responses may be increased when multiple social signals are presented with ads, and when ads are af.liated with peers who are strong, rather than weak, ties.”
Large-scale group decision making model based on social network analysis: Trust relationship-based conflict detection and elimination (2019, background): “On the contrary, if the relationship is weak, the efficiency of information propagation is likely to be lower [42, 43, 44].”
Information diffusion in online social networks: a survey (2013, background): “Online social networks play a major role in the diffusion of information by increasing the spread of novel information and diverse viewpoints [3].”
Social Networks and Labor Markets: How Strong Ties Relate to Job Finding on Facebook’s Social Network (2017, background): “5 In fact, there are a number of studies looking beyond job finding that support the idea that weak ties are most useful in the diffusion of information, which reinforces Granovetter’s original “strength of weak ties” claim (e.g., see Lin, Dayton, and Greenwald 1978; Bakshy et al. 2012; Grabowicz et”
Measuring Online Social Bubbles (2015, background): “On the other hand, the abundance of weak ties between individuals in different communities (Bakshy et al., 2012) could lead to highly diverse exposure.”