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

We investigate the extent to which social ties between people can be inferred from co-occurrence in time and space: Given that two people have been in approximately the same geographic locale at approximately the same time, on multiple occasions, how likely are they to know each other? Furthermore, how does this likelihood depend on the spatial and temporal proximity of the co-occurrences? Such issues arise in data originating in both online and offline domains as well as settings that capture interfaces between online and offline behavior. Here we develop a framework for quantifying the answers to such questions, and we apply this framework to publicly available data from a social media site, finding that even a very small number of co-occurrences can result in a high empirical likelihood of a social tie. We then present probabilistic models showing how such large probabilities can arise from a natural model of proximity and co-occurrence in the presence of social ties. In addition to providing a method for establishing some of the first quantifiable estimates of these measures, our findings have potential privacy implications, particularly for the ways in which social structures can be inferred from public online records that capture individuals' physical locations over time.computer science | privacy | probabilistic models | social networks E very day, we make inferences about the social world from incomplete observations of events around us. A particular category of such inferences draws on co-occurrences in space and time-basing estimates of a social tie between two people on the fact that they were in the same geographic locale at roughly the same time. In addition to its intuitive accessibility, such reasoning has been employed in psychological studies of urban life (1) and legal analyses of the dangers of "guilt by association" (2, 3). These issues also arise naturally in online domains, including those that reflect spatio-temporal traces of their users' activities in the physical world. Despite the broad relevance of the underlying questions, however, there has been essentially no precise basis for quantifying the significance of these effects. Here we study this issue in an online setting and find that geographic co-occurrences can in fact have significant power in forming inferences about social ties: The knowledge that two people were proximate at just a few distinct locations at roughly the same times can indicate a high conditional probability that they are directly linked in the underlying social network, in the data we consider. Our results use publicly accessible spatial and temporal information from a large social media site to derive estimates of links in the online social network of the site. We also develop a probabilistic model to account for the high probabilities that are observed. In addition to providing a quantitative basis for the power of these inferences, our results have implications for the unintended leakage of private information via participation in such sites.Our analysis uses...

A tagging community's vocabulary of tags forms the basis for social navigation and shared expression. We present a user-centric model of vocabulary evolution in tagging communities based on community influence and personal tendency. We evaluate our model in an emergent tagging system by introducing tagging features into the MovieLens recommender system. We explore four tag selection algorithms for displaying tags applied by other community members. We analyze the algorithms' effect on vocabulary evolution, tag utility, tag adoption, and user satisfaction.

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Under-contribution is a problem for many online communities. Social psychology theories of social loafing and goal-setting can provide mid-level design principles to address this problem. We tested the design principles in two field experiments. In one, members of an online movie recommender community were reminded of the uniqueness of their contributions and the benefits that follow from them. In the second, they were given a range of individual or group goals for contribution. As predicted by theory, individuals contributed when they were reminded of their uniqueness and when they were given specific and challenging goals, but other predictions were not borne out. The paper ends with suggestions and challenges for mining social science theories as well as implications for design.