Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the theme of tie strength. Our work bridges this gap between theory and practice. In this paper, we present a predictive model that maps social media data to tie strength. The model builds on a dataset of over 2,000 social media ties and performs quite well, distinguishing between strong and weak ties with over 85 % accuracy. We complement these quantitative findings with interviews that unpack the relationships we could not predict. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization. Author Keywords Social media, social networks, relationship modeling, ties,

We present a study of anonymized data capturing a month of high-level communication activities within the whole of the Microsoft Messenger instant-messaging system. We examine characteristics and patterns that emerge from the collective dynamics of large numbers of people, rather than the actions and characteristics of individuals. The dataset contains summary properties of 30 billion conversations among 240 million people. From the data, we construct a communication graph with 180 million nodes and 1.3 billion undirected edges, creating the largest social network constructed and analyzed to date. We report on multiple aspects of the dataset and synthesized graph. We find that the graph is well-connected and robust to node removal. We investigate on a planetary-scale the oft-cited report that people are separated by “six degrees of separation” and find that the average path length among Messenger users is 6.6. We also find that people tend to communicate more with each other when they have similar age, language, and location, and that cross-gender conversations are both more frequent and of longer duration than conversations with the same gender.

University through a grant from the Pew Charitable Trusts is gratefully acknowledged, as is support from NSF grant # Z362401 and assistance from the Russell Sage Foundation. The authors ’ names are in alphabetical order, each having contributed equally to the paper. The authors can be reached at

This is an outgrowth of an invited paper presented at the American Economic Association Meetings,

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this article, we draw on theory from the field of social network analysis to explore the role of Internet support versus real-world support. We use empirical evidence from a random sample survey and follow-up survey of an online discussion group for people with hearing loss. We show that although compensatory online support is associated with benefits in this group, leveraged online support---a mixing of real world and online support---is associated with even more benefits

We develop a model of friendship formation that sheds light on segregation pat-terns observed in social and economic networks. Individuals have types and see type-dependent benefits from friendships. We examine the properties of a steady-state equilibrium of a matching process of friendship formation. We use the model to under-stand three empirical patterns of friendship formation: (i) larger groups tend to form more same-type ties and fewer other-type ties than small groups, (ii) larger groups form more ties per capita, and (iii) all groups are biased towards same-type relative to demographics, with the most extreme bias coming from middle-sized groups. We trace these empirical observations to different aspects of the model, and outline different roles for biases in preferences and biases in meetings. We also illustrate some welfare implications of the model.

Much of the increase in female labor force participation in the post-war period has come from the entry of married women with young children. Accompanying this change has been a rise in cultural acceptance of maternal employment. We argue that the concurrent S-shaped rise in maternal participation and its cultural acceptance comes from generations of women engaged in Bayesian learning about the effects of maternal employment on children. Each generation updates their parents’ beliefs by observing the children of employed women. When few women participate in the labor force, most observations are uninformative and participation rises slowly. As information accumulates and the effects of labor force participation become less uncertain, more women participate, learning accelerates and labor force participation rises faster. As beliefs converge to the truth, participation flattens out. Survey data, wage data and participation data support our mechanism and distinguish it from alternative explanations.

A basic premise behind the study of large networks is that interaction leads to complex collective behavior. In our work we found very interesting and counterintuitive patterns for time evolving networks, which change some of the basic assumptions that were made in the past. We then develop models that explain processes which govern the network evolution, fit such models to real networks, and use them to generate realistic graphs or give formal explanations about their properties. In addition, our work has a wide range of applications: it can help us spot anomalous graphs and outliers, forecast future graph structure and run simulations of network evolution. Another important aspect of our research is the study of “local ” patterns and structures of propagation in networks. We aim to identify building blocks of the networks and find the patterns of influence that these blocks have on information or virus propagation over the network. Our recent work included the study of the spread of influence in a large person-to-person

This paper presents a data collection effort designed to incorporate the social dimension in social activity-travel behaviour, explicitly studying the link between individuals ’ social activities and their social networks. Using survey and interview instruments, the data collects the respondents ’ social networks using an egocentric approach, constituted by the interplay between their individual social structure and their social activity-behaviour. More explicitly, individuals ’ networks are studied in their relationship with social activity-travel generation, spatial distribution, and information communication technology use (ICT). The resultant data set links in novel ways aspects that have been rarely studied together, providing a sound base of theory and method to study and hopefully give new insights about social activity-travel behaviour. Carrasco, Hogan, Wellman, and Miller 3 1.