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Feedback effects between similarity and social influence in online communities

Published on Aug 24, 2008 in KDD (Knowledge Discovery and Data Mining)
· DOI :10.1145/1401890.1401914
David J. Crandall31
Estimated H-index: 31
(Cornell University),
Dan Cosley35
Estimated H-index: 35
(Cornell University)
+ 2 AuthorsSiddharth Suri25
Estimated H-index: 25
(Cornell University)
Sources
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?
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