<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bakshy, Eytan</style></author><author><style face="normal" font="default" size="100%">Itamar Rosenn</style></author><author><style face="normal" font="default" size="100%">Cameron Marlow</style></author><author><style face="normal" font="default" size="100%">Adamic, Lada</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Role of Social Networks in Information Diffusion</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the ACM Conference on the World Wide Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><pub-location><style face="normal" font="default" size="100%">Lyon, France</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends' information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed.</style></abstract></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ugander, Johan</style></author><author><style face="normal" font="default" size="100%">Lars Backstrom</style></author><author><style face="normal" font="default" size="100%">Cameron Marlow</style></author><author><style face="normal" font="default" size="100%">Kleinberg, Jon</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Structural diversity in social contagion</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the National Academy of Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.pnas.org/content/early/2012/03/27/1116502109.abstract</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The concept of contagion has steadily expanded from its original grounding in epidemic disease to describe a vast array of processes that spread across networks, notably social phenomena such as fads, political opinions, the adoption of new technologies, and financial decisions. Traditional models of social contagion have been based on physical analogies with biological contagion, in which the probability that an individual is affected by the contagion grows monotonically with the size of his or her ``contact neighborhood''–-the number of affected individuals with whom he or she is in contact. Whereas this contact neighborhood hypothesis has formed the underpinning of essentially all current models, it has been challenging to evaluate it due to the difficulty in obtaining detailed data on individual network neighborhoods during the course of a large-scale contagion process. Here we study this question by analyzing the growth of Facebook, a rare example of a social process with genuinely global adoption. We find that the probability of contagion is tightly controlled by the number of connected components in an individual's contact neighborhood, rather than by the actual size of the neighborhood. Surprisingly, once this ``structural diversity'' is controlled for, the size of the contact neighborhood is in fact generally a negative predictor of contagion. More broadly, our analysis shows how data at the size and resolution of the Facebook network make possible the identification of subtle structural signals that go undetected at smaller scales yet hold pivotal predictive roles for the outcomes of social processes.</style></abstract></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Keith Hampton</style></author><author><style face="normal" font="default" size="100%">Goulet, Lauren</style></author><author><style face="normal" font="default" size="100%">Cameron Marlow</style></author><author><style face="normal" font="default" size="100%">Rainie, Lee</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Why Most Facebook Users Get More Than They Give: The Effect of Facebook &quot;Power Users&quot; on Everybody Else</style></title><secondary-title><style face="normal" font="default" size="100%">Pew Internet &amp; American Life Project</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://pewinternet.org/Reports/2012/Facebook-users.aspx</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Half the adults and three-quarters of the teenagers in America use social networking sites (SNS) and Facebook by far is the most popular of these sites. The Pew Research Center's Internet &amp; American Life Project fielded a nationally representative phone survey about the social and civic lives of SNS users and reported the findings in June 2011 in a report entitled ``Social networking sites and our lives.'' 1 During the phone survey, 269 of 877 original respondents who were Facebook users gave us permission to access data on their use of Facebook so that it could be matched with their survey responses. We partnered with Facebook to match individual responses from the survey with profile information and computer logs of how those same people used Facebook services over a one-month period in November 2010 that overlapped when the survey was in the field. The results of that special analysis of 269 Facebook users identified in and recruited from a random, representative telephone survey are reported here.</style></abstract></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ugander, J.</style></author><author><style face="normal" font="default" size="100%">Karrer, B.</style></author><author><style face="normal" font="default" size="100%">Backstrom, L.</style></author><author><style face="normal" font="default" size="100%">Marlow, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Anatomy of the Facebook Social Graph</style></title><secondary-title><style face="normal" font="default" size="100%">Arxiv preprint arXiv:1111.4503</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1111.4503</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We study the structure of the social graph of active Facebook users, the largest social network ever analyzed. We compute numerous features of the graph including the number of users and friendships, the degree distribution, path lengths, clustering, and mixing patterns. Our results center around three main observations. First, we characterize the global structure of the graph, determining that the social network is nearly fully connected, with 99.91% of individuals belonging to a single large connected component, and we confirm the `six degrees of separation' phenomenon on a global scale. Second, by studying the average local clustering coefficient and degeneracy of graph neighborhoods, we show that while the Facebook graph as a whole is clearly sparse, the graph neighborhoods of users contain surprisingly dense structure. Third, we characterize the assortativity patterns present in the graph by studying the basic demographic and network properties of users. We observe clear degree assortativity and characterize the extent to which `your friends have more friends than you'. Furthermore, we observe a strong effect of age on friendship preferences as well as a globally modular community structure driven by nationality, but we do not find any strong gender homophily. We compare our results with those from smaller social networks and find mostly, but not entirely, agreement on common structural network characteristics.</style></abstract></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Moira Burke</style></author><author><style face="normal" font="default" size="100%">Kraut, Robert</style></author><author><style face="normal" font="default" size="100%">Cameron Marlow</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Social capital on Facebook: Differentiating uses and users</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 2011 annual conference on Human factors in computing systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">571–580</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Though social network site use is often treated as a monolithic activity, in which all time is equally ``social'' and its impact the same for all users, we examine how Facebook affects social capital depending upon: (1) types of site activities, contrasting one-on-one communication, broadcasts to wider audiences, and passive consumption of social news, and (2) individual differences among users, including social communication skill and self-esteem. Longitudinal surveys matched to server logs from 415 Facebook users reveal that receiving messages from friends is associated with increases in bridging social capital, but that other uses are not. However, using the site to passivelyconsume news assists those with lower social fluency draw value from their connections. The results inform site designers seeking to increase social connectedness and the value of those connections.</style></abstract></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chang, Jonathan</style></author><author><style face="normal" font="default" size="100%">Itamar Rosenn</style></author><author><style face="normal" font="default" size="100%">Lars Backstrom</style></author><author><style face="normal" font="default" size="100%">Cameron Marlow</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ePluribus: Ethnicity on Social Networks</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Fourth International Conference on Weblogs and Social Media (ICWSM-10)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">race</style></keyword><keyword><style  face="normal" font="default" size="100%">social networks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">AAAI Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Washington DC</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose an approach to determine the ethnic breakdown of a population based solely on people's names and data provided by the U.S. Census Bureau. We demonstrate that our approach is able to predict the ethnicities of individuals as well as the ethnicity of an entire population better than natural alternatives. We apply our technique to the population of U.S. Facebook users and uncover the demographic characteristics of ethnicities and how they relate. We also discover that while Facebook has always been diverse, diversity has increased over time leading to a population that today looks very similar to the overall U.S. population. We also find that different ethnic groups relate to one another in an assortative manner, and that these groups have different profiles across demographics, beliefs, and usage of site features.
</style></abstract></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lars Backstrom</style></author><author><style face="normal" font="default" size="100%">Eric Sun</style></author><author><style face="normal" font="default" size="100%">Cameron Marlow</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Find me if you can: Improving geographical prediction with social and spatial proximity</style></title><secondary-title><style face="normal" font="default" size="100%">WWW 2010: Proceeding of the 19th international conference on World Wide Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">New York, NY, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Geography and social relationships are inextricably intertwined; the people we interact with on a daily basis almost always live near us. As people spend more time online, data regarding these two dimensions – geography and social relationships – are becoming increasingly precise, allowing us to build reliable models to describe their interaction. These models have important implications in the design of location-based services, security intrusion detection, and social media supporting local communities.

Using user-supplied address data and the network of associations between members of the Facebook social network, we can directly observe and measure the relationship between geography and friendship. Using these measurements, we introduce an algorithm that predicts the location of an individual from a sparse set of located users with performance that exceeds IP-based geolocation. This algorithm is efficient and scalable, and could be run on hundreds of millions of users.</style></abstract></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Moira Burke</style></author><author><style face="normal" font="default" size="100%">Cameron Marlow</style></author><author><style face="normal" font="default" size="100%">Thomas Lento</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Social network activity and social well-being</style></title><secondary-title><style face="normal" font="default" size="100%">CHI '10: Proceedings of the 28th international conference on Human factors in computing systems</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">social capital</style></keyword><keyword><style  face="normal" font="default" size="100%">social well-being</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pub-location><style face="normal" font="default" size="100%">Atlanta, GA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Previous research has shown a relationship between use of social networking sites and feelings of social capital. However, most studies have relied on self-reports by college students. The goals of the current study are to (1) validate the common self-report scale using empirical data from Facebook, (2) test whether previous findings generalize to older and international populations, and (3) delve into the specific activities linked to feelings of social capital and loneliness. In particular, we investigate the role of directed interaction between pairs—such as wall posts, comments, and “likes”— and consumption of friends’ content, including status updates, photos, and friends’ conversations with other friends. We find that directed communication is associated with greater feelings of bonding social capital and lower loneliness, but has only a modest relationship with bridging social capital, which is primarily related to overall friend network size. Surprisingly, users who consume greater levels of content report reduced bridging and bonding social capital and increased loneliness. Implications for designs to support well-being are discussed.</style></abstract></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Eric Sun</style></author><author><style face="normal" font="default" size="100%">Itamar Rosenn</style></author><author><style face="normal" font="default" size="100%">Cameron Marlow</style></author><author><style face="normal" font="default" size="100%">Thomas Lento</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gesundheit! Modeling Contagion through Facebook News Feed</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Third International Conference on Weblogs and Social Media</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">diffusion</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">AAAI Press</style></publisher><pub-location><style face="normal" font="default" size="100%">San Jose, CA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Whether they are modeling bookmarking behavior in Flickr or cascades of failure in large networks, models of diffusion often start with the assumption that a few nodes start long chain reactions, resulting in large-scale cascades. While rea-sonable under some conditions, this assumption may not hold for social media networks, where user engagement is high and information may enter a system from multiple dis-connected sources. 

Using a dataset of 262,985 Facebook Pages and their as-sociated fans, this paper provides an empirical investigation of diffusion through a large social media network. Although Facebook diffusion chains are often extremely long (chains of up to 82 levels have been observed), they are not usually the result of a single chain-reaction event. Rather, these dif-fusion chains are typically started by a substantial number of users. Large clusters emerge when hundreds or even thousands of short diffusion chains merge together.

This paper presents an analysis of these diffusion chains using zero-inflated negative binomial regressions. We show that after controlling for distribution effects, there is no meaningful evidence that a start node’s maximum diffusion chain length can be predicted with the user’s demographics or Facebook usage characteristics (including the user’s number of Facebook friends). This may provide insight into future research on public opinion formation.</style></abstract></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Moira Burke</style></author><author><style face="normal" font="default" size="100%">Cameron Marlow</style></author><author><style face="normal" font="default" size="100%">Thomas Lento</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Feed me: motivating newcomer contribution in social network sites</style></title><secondary-title><style face="normal" font="default" size="100%">CHI '09: Proceedings of the 27th international conference on Human factors in computing systems</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">incentives</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Boston, MA</style></pub-location><pages><style face="normal" font="default" size="100%">945–954</style></pages><isbn><style face="normal" font="default" size="100%">978-1-60558-246-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Social networking sites (SNS) are only as good as the content their users share. Therefore, designers of SNS seek to improve the overall user experience by encouraging members to contribute more content. However, user motivations for contribution in SNS are not well understood. This is particularly true for newcomers, who may not recognize the value of contribution. Using server log data from approximately 140,000 newcomers in Facebook, we predict long-term sharing based on the experiences the newcomers have in their first two weeks. We test four mechanisms: social learning, singling out, feedback, and distribution.

In particular, we find support for social learning: newcomers who see their friends contributing go on to share more content themselves. For newcomers who are initially inclined to contribute, receiving feedback and having a wide audience are also predictors of increased sharing. On the other hand, singling out appears to affect only those newcomers who are not initially inclined to share. The paper concludes with design implications for motivating newcomer sharing in online communities.</style></abstract></record></records></xml>
