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

Elite Tweets: Analysing the Twitter Communication Patterns of Labour Party Peers in the House of Lords

The micro-blogging platform Twitter has gained notoriety for its status as both a communication channel between private individuals, and as a public forum monitored by journalists, the public, and the state. Its potential application for political communication has not gone unnoticed; politicians have used Twitter to attract voters, interact with constituencies and advance issue-based campaigns. This article reports on the preliminary results of the research team’s work with 21 peers sitting on the Labour frontbench. It is based on the monitoring and archival of the peers’ activity on Twitter for a period of 100 days from 16th May to 28th September 2012. Using a sample of more than 4,363 tweets and a mixed methodology combining semantic analysis, social network analysis and quantitative analysis, this paper explores the peers’ patterns of usage and communication on Twitter. Key findings are that as a tweeting community their behavior is consistent with others, however there is evidence that a coherent strategy is lacking. Labour peers tend to work in ego networks of self-interest as opposed to working together to promote party polic

Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis

Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 201

Journalism ethics in a digital environment: How journalistic codes of ethics have been adapted to the Internet and ICTs in countries around the world

Journalism is facing new ethical issues because of the emergence of the Internet and Information and Communication Technologies (ICTs). In this article, we examine how journalistic codes of ethics have been updated to address this new reality. Three research questions are explored through a systematic analysis of 99 codes from around the world. Results show that, of the 99 codes analyzed, only 9 include references to the Internet and ICTs. We conclude with proposals for changes in the codes that would help journalists resolve these new moral issues

Discursive Equality and Everyday Talk Online: The Impact of “Superparticipants”

Empirical studies of online debate almost universally observe a “dominant” minority of posters. Informed by theories of deliberative democracy, these are typically framed negatively—yet research into their impact on debate is scant. To address this, a typology of what we call super-participation (super-posters, agenda-setters and facilitators) is developed and applied to the http://www.moneysavingexpert.com/ forum. Focusing on the first of these, we found 2,052 superposters (0.4%) contributing 47% of 25m+ posts. While superposters were quantitatively dominant, qualitative content analysis of the discursive practices of 25 superposters (n=40,044) found that most did not attempt to stop other users from posting (curbing) or attack them (flaming). In fact, in contradiction to the received wisdom, super-posters discursively performed a range of positive roles

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

Stance detection on social media: State of the art and trends

Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents an exhaustive review of stance detection techniques on social media, including the task definition, different types of targets in stance detection, features set used, and various machine learning approaches applied. The survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, this study explores the emerging trends and different applications of stance detection on social media. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this paper. Please withdraw this article before we finish the new versio

An investigation into customer perception and behaviour through social media research – an empirical study of the United Airline overbooking crisis

Airlines have been adopting yield management to optimise the perishable seat control problem and overbooking is a common strategy. This study outlines the connections between yield management, crises, and crisis communication. Using big data captured on a social media platform, this study aims to combine traditional yield management with emerging social big data analytics. As part of this, we use the twitter data on the 2017 United Airline (UA) to analyse the overbooking crisis. Our findings shed light on the importance of a more effective orchestration of yield management to avoid the escalation of crises during crisis communication phases