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This exploratory study investigates the role that bots and trolls played on social media in widening the gaps of political partisanship in sub-Saharan Africa. Taking the case of the 2018 Zambian by-elections, the authors examined the relationship between online social media content that propagates hate and organized trolling efforts in Zambia. The study used machine learning tools to identify the origin of the bots on Facebook and Twitter accounts (trolls) of the two major political parties in Zambia (PF and UPND). Online posts that accounted for the election campaigns and the aftermath in the year 2018 were considered for the study. Findings suggest that social-mediated conversations were divided along political lines and that the examined trolling accounts systematically took advantage of the existing echo chambers to create hate messages on Zambian social networks. In other words, the findings indicated that the online hate messages that accounted for violence were neither created by the PF or UPND political parties as earlier studies suggest but by bots and trolls.

Artificial neural networks are a machine learning method ideal for solving classification and prediction problems using Big Data. Online social networks and virtual communities provide a plethora of data. Artificial neural networks have been used to determine the emotional meaning of virtual community posts, determine age and sex of users, classify types of messages, and make recommendations for additional content. This article reviews and examines the utilization of artificial neural networks in online social network and virtual community research. An artificial neural network to predict the maintenance of online social network "friends" is developed to demonstrate the applicability of artificial neural networks for virtual community research.

The Latin America Kids Online network works in partnership with Global Kids Online and the EU Kids Online network. It seeks to enhance knowledge of Latin American children's online access, opportunities, risks and safety. It uses multiple methods to map children's and parents' experience of the internet, in dialogue with national and Latin American policy stakeholders.

Large-scale online communities rely on computer-mediated communication between participants, enabling them to sustain interactions and exchange on a scale hitherto unknown. Yet little research has focused on how these online communities sustain themselves and how their interactions are structured. In this paper, we theorize and empirically measure the network exchange patterns of long-duration sustainable online communities. We propose that participation dynamics follow specific forms of social exchange: direct reciprocity, indirect reciprocity, and preferential attachment. We integrate diverse findings about individual participation motivations by identifying how individual behavior manifests in network-level structures of online communities. We studied five online communities over 27 months and analyzed 38,483 interactions using exponential random graph (p*) models and mixed-effects analysis of covariance. In a test of competing models, we found that network exchange patterns in online community communication networks are characterized by direct reciprocity and indirect reciprocity patterns and, surprisingly, a tendency away from preferential attachment. Our findings undermine previous explanations that online exchange follows a power law distribution based on people wanting to connect to "popular" others in online communities. Our work contributes to theories of new organizational forms by identifying network exchange patterns that regulate participation and sustain online communities.

We analyze information diffusion using empirical data that tracks online communication around two instances of mass political mobilization that took place in Spain in 2011 and 2012. We also analyze protest-related communications during the year that elapsed between those protests. We compare the global properties of the topological and dynamic networks through which communication took place, as well as local changes in network composition. We show that changes in network structure underlie aggregated differences on how information diffused: an increase in network hierarchy is accompanied by a reduction in the average size of cascades. The increasing hierarchy affects not only the underlying communication topology but also the more dynamic structure of information exchange ; the increase is especially noticeable amongst certain categories of nodes (or users). Our findings suggest that the relationship between the structure of networks and their function in diffusing information is not as straightforward as some theoretical models of diffusion in networks imply.

"This book explores how online therapy communities offer an accessible space that is not confined by economic abilities, geographical barriers, or familial restraints. Scholars of communication, sociology, and psychology will find this book of particular interest"--

AbstractOnline community networks can help organizations improve collaboration. However, in spite of their potential value, there has been little empirical research into two important network factors that determine their success: social characteristics of users and changes in operations that result from network evolution. Our research addresses these deficiencies by using a cultural framework. Derived from anthropology, it extends previous system dynamics research on online community networks. The framework acts as a lens, enabling a better understanding of the effects that changes in these factors bring to online community networks. Using data collected from Wikipedia for model calibration, our findings suggest that, contrary to conventional wisdom, removing policies that focus on building group commitment does not lower performance. The results also show that online networks need structural control, otherwise their attractiveness, credibility and, subsequently, content value might all decrease. To ensure sustainability the network must be monitored, especially during the early stages of its evolution, so that rules and regulations that ensure value and validity can be selectively employed. Copyright © 2008 John Wiley & Sons, Ltd.

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields.

Text analysis, web scraping, and other computational techniques enable policy network researchers to efficiently obtain objective measures of network connections. However, the extent to which these observational methods differ from traditional survey instrument‐based measures remains an open question. Focusing on a large regional policy network of 221 organizations, this study compares a measure of collaboration generated via survey instrument to two different measures based upon internet hyperlinks and Twitter interactions between network actors. We address two questions: (1) To what extent do objective network measures based upon observed online interactions and subjective measures based upon self‐reported relationships reveal the same inter‐organizational partnerships and structural network dynamics? and (2) How useful are online network measures for supplementing survey‐based network measures? We find a significant, but substantively small, correlation between survey‐based measures and online interactions. Thus, online network measures may complement survey‐based measures, but likely reflect different aspects of the overall policy network. We conclude by discussing the potential for multiplex measures of policy networks that draw upon multiple measures to more fully understand policy network landscapes. These results bridge and help to contextualize prior work on policy network measures and virtual policy networks within the broader context of complex governance systems.