Social Interaction Based Audience Segregation for Online Social Networks.

Online social networking is the latest craze that has captured the attention of masses, people use these sites to communicate with their friends and family. These sites o er attractive means of social interac- tions and communications, but also raise privacy concerns. This paper examines user's abilities to control access to their personal information posted in online social networks. Online social networks lack common mechanism used by individuals in their real life to manage their privacy. The lack of such mechanism signi cantly a ects the level of user control over their self presentation in online social networks. In this paper, we present social interaction based audience segregation model for online so- cial networks. This model mimics real life interaction patterns and makes online social networks more privacy friendly. Our model uses type, fre- quency, and initiation factor of social interactions to calculate friendship strength. The main contribution of the model is that it considers set of all possible interactions among friends and assigns a numerical weight to each type of interaction in order to increase or decrease its contribu- tion in calculation of friendship strength based on its importance in the development of relationship ties

Pathways to Fragmentation:User Flows and Web Distribution Infrastructures

This study analyzes how web audiences flow across online digital features. We construct a directed network of user flows based on sequential user clickstreams for all popular websites (n=1761), using traffic data obtained from a panel of a million web users in the United States. We analyze these data to identify constellations of websites that are frequently browsed together in temporal sequences, both by similar user groups in different browsing sessions as well as by disparate users. Our analyses thus render visible previously hidden online collectives and generate insight into the varied roles that curatorial infrastructures may play in shaping audience fragmentation on the web

Privileged Mexican migrants in Europe: Distinctions and cosmopolitanism on social networking sites

This article examines the ways in which classed distinctions are related to the construction of increasingly cosmopolitan identities on Social Networking Sites (SNSs) amongst Mexican migrants from relatively privileged backgrounds living in Europe. It centres on how user demographics shape many of the concerns and outcomes pertaining to the use of SNSs. It considers the implications of the fact that SNSs are predominantly used by a demographic considered as non-marginalized, mobile and as possessing relatively privileged economic, cultural and social backgrounds. It analyses the ways in which online identities are constructed on SNS profiles using multimedia content to represent specific lifestyles and cultural practices that are used to make distinctions amongst participants, and are related to social, cultural and economic capital. A critical analysis is presented as to how users represent cosmopolitan identities online through the display of tastes and lifestyles in SNS content and into how these representations relate to users’ privileged positions in Mexican society. Bourdieu’s concept of distinction is used to emphasize the utility of considering different forms of capital in analysing the use of SNSs and profile content generated by a specific demographic. This article demonstrates how the analysis of SNS use may contribute towards an understanding of how classed distinctions are made based on this use and of how users negotiate the posting of profile content according to these distinctions and manage (select, edit and share) their representations

A Guide for Newcomers to Agent-Based Modeling in the Social Sciences

This guide provides pointers to introductory readings, software, and other materials to help newcomers become acquainted with agent-based modeling in the social sciences. Related work can be accessed at: http://www.econ.iastate.edu/tesfatsi/ace.htmagent-based modeling; social sciences

Quantifying echo chamber effects in information spreading over political communication networks

Echo chambers in online social networks, in which users prefer to interact only with ideologically-aligned peers, are believed to facilitate misinformation spreading and contribute to radicalize political discourse. In this paper, we gauge the effects of echo chambers in information spreading phenomena over political communication networks. Mining 12 million Twitter messages, we reconstruct a network in which users interchange opinions related to the impeachment of the former Brazilian President Dilma Rousseff. We define a continuous {political position} parameter, independent of the network's structure, that allows to quantify the presence of echo chambers in the strongly connected component of the network, reflected in two well-separated communities of similar sizes with opposite views of the impeachment process. By means of simple spreading models, we show that the capability of users in propagating the content they produce, measured by the associated spreadability, strongly depends on their attitude. Users expressing pro-impeachment sentiments are capable to transmit information, on average, to a larger audience than users expressing anti-impeachment sentiments. Furthermore, the users' spreadability is correlated to the diversity, in terms of political position, of the audience reached. Our method can be exploited to identify the presence of echo chambers and their effects across different contexts and shed light upon the mechanisms allowing to break echo chambers.Comment: 9 pages, 4 figures. Supplementary Information available as ancillary fil

Social Media and Fake News in the 2016 Election

Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories ("fake news"), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online sur-vey, we find:(i) social media was an important but not dominant source of election news, with14 percent of Americans calling social media their "most important" source;(ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared8 million times;(iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and(iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks

Partisan Asymmetries in Online Political Activity

We examine partisan differences in the behavior, communication patterns and social interactions of more than 18,000 politically-active Twitter users to produce evidence that points to changing levels of partisan engagement with the American online political landscape. Analysis of a network defined by the communication activity of these users in proximity to the 2010 midterm congressional elections reveals a highly segregated, well clustered partisan community structure. Using cluster membership as a high-fidelity (87% accuracy) proxy for political affiliation, we characterize a wide range of differences in the behavior, communication and social connectivity of left- and right-leaning Twitter users. We find that in contrast to the online political dynamics of the 2008 campaign, right-leaning Twitter users exhibit greater levels of political activity, a more tightly interconnected social structure, and a communication network topology that facilitates the rapid and broad dissemination of political information.Comment: 17 pages, 10 figures, 6 table