How people make friends in social networking sites - A microscopic perspective

We study the detailed growth of a social networking site with full temporal information by examining the creation process of each friendship relation that can collectively lead to the macroscopic properties of the network. We first study the reciprocal behavior of users, and find that link requests are quickly responded to and that the distribution of reciprocation intervals decays in an exponential form. The degrees of inviters/accepters are slightly negatively correlative with reciprocation time. In addition, the temporal feature of the online community shows that the distributions of intervals of user behaviors, such as sending or accepting link requests, follow a power law with a universal exponent, and peaks emerge for intervals of an integral day. We finally study the preferential selection and linking phenomena of the social networking site and find that, for the former, a linear preference holds for preferential sending and reception, and for the latter, a linear preference also holds for preferential acceptance, creation, and attachment. Based on the linearly preferential linking, we put forward an analyzable network model which can reproduce the degree distribution of the network. The research framework presented in the paper could provide a potential insight into how the micro-motives of users lead to the global structure of online social networks.Comment: 10 pages, 12 figures, 2 table

Everyday the Same Picture: Popularity and Content Diversity

Facebook is flooded by diverse and heterogeneous content, from kittens up to music and news, passing through satirical and funny stories. Each piece of that corpus reflects the heterogeneity of the underlying social background. In the Italian Facebook we have found an interesting case: a page having more than $40K$ followers that every day posts the same picture of a popular Italian singer. In this work, we use such a page as a control to study and model the relationship between content heterogeneity on popularity. In particular, we use that page for a comparative analysis of information consumption patterns with respect to pages posting science and conspiracy news. In total, we analyze about $2M$ likes and $190K$ comments, made by approximately $340K$ and $65K$ users, respectively. We conclude the paper by introducing a model mimicking users selection preferences accounting for the heterogeneity of contents

Online Networks, Social Interaction and Segregation: An Evolutionary Approach

We have developed an evolutionary game model, where agents can choose between two forms of social participation: interaction via online social networks and interaction by exclusive means of face-to-face encounters. We illustrate the societal dynamics that the model predicts, in light of the empirical evidence provided by previous literature. We then assess their welfare implications. We show that dynamics, starting from a world in which online social interaction is less gratifying than offline encounters, will lead to the extinction of the sub-population of online networks users, thereby making Facebook and alike disappear in the long run. Furthermore, we show that the higher the propensity for discrimination between the two sub-populations of socially active individuals, the greater the probability that individuals will ultimately segregate themselves, making society fall into a social poverty trap

Online networks and subjective well-being

We argue that the use of online networks may threaten subjective well-being in several ways, due to the inherent attributes of Internet-mediated interaction and through its effects on social trust and sociability. We test our hypotheses on a representative sample of the Italian population. We find a significantly negative correlation between online networking and well-being. This result is partially confirmed after accounting for endogeneity. We explore the direct and indirect effects of the use of social networking sites (SNS) on well-being in a SEM analysis. We find that online networking plays a positive role in subjective well-being through its impact on physical interactions, whereas SNS use is associated with lower social trust. The overall effect of networking on individual welfare is significantly negative.Comment: 40 page

Studying and Modeling the Connection between People's Preferences and Content Sharing

People regularly share items using online social media. However, people's decisions around sharing---who shares what to whom and why---are not well understood. We present a user study involving 87 pairs of Facebook users to understand how people make their sharing decisions. We find that even when sharing to a specific individual, people's own preference for an item (individuation) dominates over the recipient's preferences (altruism). People's open-ended responses about how they share, however, indicate that they do try to personalize shares based on the recipient. To explain these contrasting results, we propose a novel process model of sharing that takes into account people's preferences and the salience of an item. We also present encouraging results for a sharing prediction model that incorporates both the senders' and the recipients' preferences. These results suggest improvements to both algorithms that support sharing in social media and to information diffusion models.Comment: CSCW 201

The Limits of Popularity-Based Recommendations, and the Role of Social Ties

In this paper we introduce a mathematical model that captures some of the salient features of recommender systems that are based on popularity and that try to exploit social ties among the users. We show that, under very general conditions, the market always converges to a steady state, for which we are able to give an explicit form. Thanks to this we can tell rather precisely how much a market is altered by a recommendation system, and determine the power of users to influence others. Our theoretical results are complemented by experiments with real world social networks showing that social graphs prevent large market distortions in spite of the presence of highly influential users.Comment: 10 pages, 9 figures, KDD 201