NDLI: Feedback effects between similarity and social influence in online communities

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Feedback effects between similarity and social influence in online communities

Content Provider	ACM Digital Library
Author	Cosley, Dan Kleinberg, Jon Huttenlocher, Daniel Suri, Siddharth Crandall, David
Abstract	A fundamental open question in the analysis of social networks is to understand the interplay between similarity and social ties. People are similar to their neighbors in a social network for two distinct reasons: first, they grow to resemble their current friends due to social influence; and second, they tend to form new links to others who are already like them, a process often termed selection by sociologists. While both factors are present in everyday social processes, they are in tension: social influence can push systems toward uniformity of behavior, while selection can lead to fragmentation. As such, it is important to understand the relative effects of these forces, and this has been a challenge due to the difficulty of isolating and quantifying them in real settings. We develop techniques for identifying and modeling the interactions between social influence and selection, using data from online communities where both social interaction and changes in behavior over time can be measured. We find clear feedback effects between the two factors, with rising similarity between two individuals serving, in aggregate, as an indicator of future interaction -- but with similarity then continuing to increase steadily, although at a slower rate, for long periods after initial interactions. We also consider the relative value of similarity and social influence in modeling future behavior. For instance, to predict the activities that an individual is likely to do next, is it more useful to know the current activities of their friends, or of the people most similar to them?.
Starting Page	160
Ending Page	168
Page Count	9
File Format	PDF QT / MOV
ISBN	9781605581934
DOI	10.1145/1401890.1401914
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Social influence Social networks Online communities
Content Type	Video Text
Resource Type	Article

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