Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters

Conversations on Twitter create networks with identifiable contours as people reply to and mention one another in their tweets. These conversational structures differ, depending on the subject and the people driving the conversation. Six structures are regularly observed: divided, unified, fragmented, clustered, and inward and outward hub and spoke structures. These are created as individuals choose whom to reply to or mention in their Twitter messages and the structures tell a story about the nature of the conversatio

Just a guy in pajamas? Framing the blogs in mainstream US newspaper coverage (1999—2005)

When new technologies are introduced to the public, their widespread adoption is dependent, in part, on news coverage (Rogers, 1995).Yet, as weblogs began to play major role in the public spheres of politics and journalism, journalists faced a paradox: how to cover a social phenomenon that was too large to ignore and posed a significant threat to their profession. This article examines how blogs were framed by US newspapers as the public became more aware of the blogging world. A content analysis of blog-related stories in major US newspapers from 1999 to 2005 was conducted. Findings suggest that newspaper coverage framed blogs as more beneficial to individuals and small cohorts than to larger social entities such as politics, business and journalism. Moreover, only in the realm of journalism were blogs framed as more of a threat than a benefit, and rarely were blogs considered an actual form of journalism.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

A Social Networks Approach to Online Social Movement: Social Mediators and Mediated Content in #FreeAJStaff Twitter Network

The movement to free Al Jazeera journalists (#FreeAJStaff), imprisoned by Egyptian authorities, utilized social media over almost 2 years, between 2013 and 2015. #FreeAJStaff movement emerged as a unique blend of social movement and news media, taking place primarily on Twitter. This study applied a social networks approach to examine patterns of information flow within the #FreeAJStaff movement on Twitter: the emergence of information siloes and social mediators, who bridge them. Twitter data of 22 months were collected, resulting in social networks created by 71,326 users who included the hashtag #FreeAJStaff in their tweets, and 149,650 social ties (mentions and replies) among them. Analysis found social mediators to be primarily core movement actors (e.g., Al Jazeera) or elites (e.g., politicians), rather than grassroots actors. Furthermore, core actors exhibited more reciprocal relationship with other users than elite actors. In contrast, elite actors evoked denser exchange of messages. Finally, this study identified the mechanism used to create a Spillover Effect between social movements (such as #FreeAJStaff and #FreeShawkan), finding that mediated content, which travels across clusters, was more likely to include non-FreeAJStaff movement hashtags, than siloed content, which remains within a cluster. Theoretical and practical implications are discussed

Free interactions, hierarchical structure: Factors explaining replies attraction in online discussions

Given the opportunity to interact freely, individuals conform to a structure, in which a few actors attract a large and disproportionate number of ties or relationships. Drawing from literature on preferential attachment and scholarship about online discussions, this study examines patterns of replies, which are one aspect of the disproportionate attraction of replies in forums, as predicted by two factors: number of existing replies and content of posted messages. In two 2X2 experimental designs conducted via a custom developed online discussion platform, 198 subjects participated. Findings show an interaction, where the number of replies increased replies attraction only for the high–interest messages, illustrating the balance between the individual and group dynamics levels in evoking discussions

Virtual Zika Transmission after the first US Case: Who said What and How It Spread on Twitter

Background: This paper goes beyond detecting specific themes within Zika-related chatter on Twitter, to identify the key actors who influence the diffusive process through which some themes are more amplified than others are. Methods: We collected all Zika-related tweets during the three months immediately after the first U.S. case of Zika. Following categorization into twelve themes, a cross-section of tweets were grouped into weekly datasets to capture 12 amplifier/user groups and analysed by four amplification modes: mentions, retweets, talkers, and twitter-wide amplifiers. Results: We analysed 3,057,130 tweets in the US and categorized 4,997 users. The most talked about theme was Zika transmission (~58%). News media, public health institutions and grassroots users were the most visible and frequent sources and disseminators of Zika-related Twitter content. Grassroots users were the primary source and disseminators of conspiracy theories. Discussion & Conclusions: Social media analytics enable public health institutions to quickly learn what information is being disseminated, and by whom, regarding infectious diseases. Such information can help public health institutions identify and engage with news media and other active information providers. It also provides insights into media and public concerns, accuracy of information on Twitter, and information gaps that may exist. The study identifies implications for pandemic preparedness and response in the digital era and presents the agenda for future research and practice

Classifying Twitter Topic-Networks Using Social Network Analysis

As users interact via social media spaces, like Twitter, they form connections that emerge into complex social network structures. These connections are indicators of content sharing, and network structures reflect patterns of information flow. This article proposes a conceptual and practical model for the classification of topical Twitter networks, based on their network-level structures. As current literature focuses on the classification of users to key positions, this study utilizes the overall network structures in order to classify Twitter conversation based on their patterns of information flow. Four network-level metrics, which have established as indicators of information flow characteristics—density, modularity, centralization, and the fraction of isolated users—are utilized in a three-step classification model. This process led us to suggest six structures of information flow: divided, unified, fragmented, clustered, in and out hub-and-spoke networks. We demonstrate the value of these network structures by segmenting 60 Twitter topical social media network datasets into these six distinct patterns of collective connections, illustrating how different topics of conversations exhibit different patterns of information flow. We discuss conceptual and practical implications for each structure