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Since the beginning of 2020, while societies and economies around the world have struggled to cope with the realities of the COVID-19 pandemic, cyberspace has given governments, businesses, and general end-users the ability to work, play, and connect in new and innovative ways. With everything from workspaces and classrooms to family gatherings and exercise routines forced online, the Internet has enabled people across the globe to carry on and maintain a sense of normalcy during very abnormal times. However, at the same time, while the world has been focused on the health, economic, political, and social ramifications of the pandemic, terrorist organizations, fringe groups, and extremist communities around the world have become emboldened, finding opportunity to exploit the situation, incite hate, (re)mobilize, and promote their ideologies online in novel ways. These groups—which we loosely classify as malicious nonstate actors for the purposes of this research —have been primarily focused on exploiting and contributing to the diffusion of information during the pandemic for their own strategic gain.

Ephemeral digital trace data can decrease the completeness, reproducibility, and reliability of social media datasets. Systematic post deletions thus potentially bias the results of computational methods used to map actors, content, and online information diffusion. Therefore, the aim of this study was to assess the extent and distribution of message deletion across different data types using data from the hybrid messenger service Telegram, which has experienced an influx of deplatformed users from mainstream social media platforms. A repeatedly scraped sample of messages from public Telegram groups and channels was used to investigate the effect of message ephemerality on the consistency of Telegram datasets. The findings revealed that message deletion introduces biases to the computational collection and analysis of Telegram data. Further, message ephemerality reduces dataset consistency, the quality of social network analyses, and the results of computational content analysis methods, such as topic modeling or dictionaries. The implications of these findings for scholars aiming to use Telegram data for computational research, possible solutions, and contributions to the methodological advancement of studying online political communication are discussed further in this article.

This thesis aimed to address these limitations by developing a cross-platform analysis framework for online-hate researchers to gain a clearer understanding of the dynamics of the global hate ecosystem. More specifically, the designing of this framework involved examining the main functionalities of existing online-hate analysis frameworks, and the extent to which they address cross-platform hate. The strengths and limitations of these approaches then informed the functional requirements of the cross-platform analysis framework. To demonstrate how the framework can provide novel insights into online-hate research, this thesis also details its application to various case studies, including online hate from white supremacy-supporting users and environments spread during the 2020 US election and the COVID-19 pandemic.

The purpose of this scoping review is to highlight the machine learning tools used in research to address and prevent violent extremism. To achieve this goal, the following objectives guide this study: (1) describe outcomes that have been studied; (2) summarize the data sources used; and (3) determine whether the reporting of machine learning predictive models aligns with the established reporting guidelines for reporting of prediction models. ProQuest, Compendex, IEEE, JStor and PubMed were searched from June to July 2022. Based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, databases were searched for articles related to machine learning models applied to the address and prevention of violent extremism. Following standards established by reporting guidelines, findings were extracted from published articles, including general study characteristics, aspects of model development, and reporting of results. Of 53 unique articles identified by the search, 18 were included in the review. Most articles were published between 2016 and 2022 (n = 16, 88.8%). Studies focused on violent extremism worldwide, with the majority of studies not specifically focused on a distinct region (n = 11, 61.1%). The most frequently used machine learning algorithms were support vector machines (n = 9, 50%), followed by random forests (n = 5, 27.7%), natural language processing (n = 4, 22.2%), and deep learning (n = 4, 22.2%). The number of features used varied greatly, ranging from 17 to 7556. Many studies did not report an epistemological or theoretical framework which guided their machine learning approaches or interpretation of findings (n = 8, 44.4%). Many studies did not incorporate the TRIPOD or any other recommended guidelines for the reporting of predictive models. Future research in this field should prioritize evaluating the impact of prediction models on decisions for addressing and preventing violent extremism.

Hate speech, harassment, and an increasing prevalence of right-wing extremism in online game spaces are of growing concern in the United States. Understanding trends in how and to what extent extremist groups utilize online gaming spaces is vital in taking action to protect players. To synthesize the current state of extant research and suggest future directions, we conduct a systematic review of the literature on right-wing extremism in videogames. We detail our search protocol, selection criteria, and analysis of the collected work, and then summarize the findings. Important themes include how and why extremists’ targeting of online game communities began, the role of Gamergate in this process, and the industry and market context in which such activities emerged. We describe the current nature of the problem, with extremist language and ideology providing a kind of on-ramp for radicalizing disenfranchised gamers. We conclude with a summary of responses from industry and legislators.

The reception to 2020’s The Last of Us Part II was a maelstrom of misleading marketing, unprecedented leaks, and a vicious fallout characterized by prejudiced online harassment and sprawling conspiracies. Through an in-depth analysis of Part II‘s reception, this article seeks to apprehend the increasing frequency of such controversies in popular culture as a distinct transformation of online fandom, which is defined by the agendas of the alt-right. The “anti-woke” campaigns emblematic of these communities are best understood through what this article defines as alt-fandom, where conspiracy theories are fabricated in order to defy the supposed ideological and narrative transgressions of a new text. In the case of The Last of Us franchise, the challenges posed by its corrosive alt-fandom are endemic to a new reception climate confronting the production of media texts.