Information flow in social groups

By Fang Wu, Bernardo A. Huberman, Lada A. Adamic, Joshua R. Tyler. 2003

347 citations recorded by the source corpus.

We present a study of information flow that takes into account the observation that an item relevant to one person is more likely to be of interest to individuals in the same social circle than those outside of it. This is due to the fact that the similarity of node attributes in social networks decreases as a function of the graph distance. An epidemic model on a scale-free network with this property has a finite threshold, implying that the spread of information is limited. We tested our predictions by measuring the spread of messages in an organization and also by numerical experiments that take into consideration the organizational distance among individuals.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a5438cccda214e6e2af9495bb76efd2a1d91a179

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In Experimental and Cognitive Psychology

In Statistical and Nonlinear Physics

Citations in the corpus

Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks (2010, neutral): “email chain letters (Wu et al. 2004; Liben-Nowell andKleinberg 2008) and blog posts (Gruhl and Liben-nowell 2004; Leskovec et al.”
Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake (2011, background): “…have not fully explained the information diffusion patterns observed in empirical studies, such as the short lengths of email forwarding chains [26] and product recommendation chains [14], and the diffusion tree of chain-letters with long paths but few branches reconstructed by Liben-Nowell…”
Topic and Role Discovery in Social Networks (2005, background): “With the recent availability of large data sets of human interactions (Shetty & Adibi, 2004; Wu, Huberman, Adamic, & Tyler, 2003 ), the popularity of services like MySpace.com and LinkedIn.com, and the salience of the connections among the 9/11 hijackers, there has been growing interest in social n”
Information diffusion through blogspace (2004, background): “[33] consider the flow of information through real and synthetic email networks (generated according to a power-law distribution) under a model in which the probability that a nodeu will forward a meme to a neighbor v of u decays as the graph distanced(s, u) from the original source node s of the me”
WHATSUP: A Decentralized Instant News Recommender (2013, background): “Relying on explicitly declared friends, as in Digg, is known to limit the content that can be received [8] by substantially influencing decision making [9].”
How to search a social network (2003, background): “This is precisely the degree distribution of the unfiltered HP Labs email network, where all communication, including unidirectional and infrequent correspondence, is taken into account (Wu et al., 2004).”
Influence and passivity in social media (2010, background): “Earlier work focused on the eﬀects that scale-free networks and the aﬃnity of their members for certain topics had on the propagation of information [6].”
Using a model of social dynamics to predict popularity of news (2010, background): “Researchers are using this data to study a variety of topics, including detecting [1, 20] and influencing [6, 12] trends in public opinion, and dynamics of information flow in groups [25, 19].”
The Diversity-Bandwidth Trade-off1 (2011, neutral): “…flows in proportion to the distribution of information in the network (e.g., Granovetter 1978; Schelling 1978; Kempe, Kleinberg, and Tardos 2003),5 information exchange is fundamentally a social process and knowledge transfer a discretionary activity (Reagans and McEvily 2003; Wu et al. 2004).”
Identification of influencers - Measuring influence in customer networks (2008, background): “[62], who provided a study in another domain of textual similarity of Stanford student homepages.”
A framework for quantitative analysis of cascades on networks (2010, background): “They are created, for example, when an individual forwards an email she receives to her contacts [ 16 , 17], or retweets a news item to her followers on Twitter [18].”
Echoes of power: language effects and power differences in social interaction (2011, background): “In the opening sections, we have discussed some of the ways in which earlier work used text content to analyze on-line networks [49, 31, 11, 9, 16], as well as background on language coordination and the exchange-theoretic notions of power from status and dependence.”
Statistical physics of social dynamics (2007, background): “In this way one of the fundamental properties of the voter model, conservation of the global magnetization, is violated (Suchecki et al., 2005a; Wu et al., 2004).”
Design Considerations for Collaborative Visual Analytics (2007, positive): “Wu et al’s [52] study of organizational information flow found that information spreads efficiently among homophilous group members but not across community boundaries, further suggesting the value of identifying structural holes and directing bridging individuals in the social network towards parti”
The “New” Science of Networks (2004, background): “…(Liljeros et al. 2001), however, has subsequently been questioned (Jones & Handcock 2003), as has the absence of the epidemic threshold when factors such attenuation of infectiousness over time (Wu et al. 2004), and the ﬁnite size of real networks (May & Lloyd 2001) are taken into account.”
Creating Social Contagion through Viral Product Design: A Randomized Trial of Peer Influence in Networks (2011, positive): “Second, information flows between individuals in a network typically decay rapidly with network distance (Wu et al. 2004, Aral et al. 2007).”
Do social networks of female northern long-eared bats vary with reproductive period and age? (2010, background): “…et al. 2007) and benefits (e.g., nepotism, reciprocity, and information transfer; Hamilton 1964a, b; Krützen et al. 2005; Ohtsuki et al. 2006; Trivers 1971; Wu et al. 2004), which also vary with group size and composition, thus affecting individual tradeoffs of social living (Alexander 1974).”