Online Library

VOX-Pol Blog Post.

This paper offers a set of specific proposals for better describing harmful content online and for reducing the damage it causes, while protecting freedom of expression. The ideas are mainly meant for OSPs since they regulate the vast majority of online content; taken together they operate the largest system of censorship the world has ever known, controlling more human communication than any government. Governments, for their part, have tried to berate or force the companies into changing their policies, with limited and often repressive results. For these reasons, this paper focuses on what OSPs should do to diminish harmful content online. The proposals focus on the rules that form the basis of each regulation system, as well as on other crucial steps in the regulatory process, such as communicating rules to platform users, giving multiple stakeholders a role in regulation, and enforcement of the rules.

Online social networks have become a powerful venue for political activism. In many cases large, insular online communities form that have been shown to be powerful diffusion mechanisms of both misinformation and propaganda. In some cases, these groups users advocate actions or policies that could be construed as extreme along nearly any distribution of opinion and are thus called Online Extremist Communities (OECs). Although these communities appear increasingly common, little is known about how these groups form or the methods used to influence them. The work in this thesis provides researchers a methodological framework to study these groups by answering three critical research questions:
• How can we detect large dynamic online activist or extremist communities?
• What automated tools are used to build, isolate, and influence these communities?
• What methods can be used to gain novel insight into large online activist or extremist communities?
This group members social ties can be inferred based on the various affordances offered by OSNs for group curation. By developing heterogeneous, annotated graph representations of user behavior I can efficiently extract online activist discussion cores using an ensemble of unsupervised machine learning methods. I call this technique Ensemble Agreement Clustering. Through manual inspection, these discussion cores can then often be used as training data to detect the larger community. I present a novel supervised learning algorithm called Multiplex Vertex Classification for network bipartition on heterogeneous, annotated graphs. This methodological pipeline has also proven useful for social botnet detection, and a study of large, complex social botnets used for propaganda dissemination is provided as well. Throughout this thesis, I provide Twitter case studies including communities focused on the Islamic State of Iraq and al-Sham (ISIS), the ongoing Syrian Revolution, the Euromaidan Movement in Ukraine, as well as the alt-Right.

The Islamic State of Iraq and ash-Sham (ISIS) continues to use social media as an essential element of its campaign to motivate support. On Twitter, ISIS’ unique ability to leverage unaffiliated sympathizers that simply retweet propaganda has been identified as a primary mechanism in their success in motivating both recruitment and “lone wolf” attacks. The present work explores a large community of Twitter users whose activity supports ISIS propaganda diffusion in varying degrees. Within this ISIS supporting community, we observe a diverse range of actor types, including fighters, propagandists, recruiters, religious scholars, and unaffiliated sympathizers. The interaction between these users offers unique insight into the people and narratives critical to ISIS’ sustainment. In their entirety, we refer to this diverse set of users as an online extremist community or OEC. We present Iterative Vertex Clustering and Classification (IVCC), a scalable analytic approach for OEC detection in annotated heterogeneous networks, and provide an illustrative case study of an online community of over 22,000 Twitter users whose online behavior directly advocates support for ISIS or contibutes to the group’s propaganda dissemination through retweets.

Policymakers and scholars fear that the Internet has increased the ability of transnational terrorists, like al Qaeda, to attack targets in the West, even in the face of increased policing and military efforts. Although access to the Internet has increased across the globe, there has been no corresponding increase in completed transnational terrorist attacks. This analysis examines the causal logics—which have led to the conventional wisdom—and demonstrates both theoretically and empirically that the Internet is not a force multiplier for transnational terrorist organizations. Far from being at a disadvantage on the Internet, state security organs actually gain at least as much utility from the Internet as terrorist groups do, meaning that at worst the Internet leaves the state in the same position vis-a`-vis terrorist campaigns as it was prior to the Internet.

The so-called ‘alt-right’ is an amorphous but synchronized collection of far-right people and movements, an umbrella label for a number of loosely affiliated social movements around the world, although its centre of gravity is in the United States. Many factors have contributed to the alt-right’s rise to prominence, but one of the most visible is its online presence. Alt-right views have been promoted online by a small army of trolls and activists staging harassment campaigns, pushing hashtags and posting links to extremist content and conspiracy theories on social media. Since 2016, the alt-right and its allies have held an increasingly prominent place in American and European politics, rallying support behind a variety of causes and candidates. This study seeks to evaluate the alt-right’s online presence with robust metrics and an analysis of content shared by adherents. The alt-right has many components online; this report will primarily examine its presence on Twitter, in part because the movement is particularly active on that platform, and in part because Twitter’s data access policies allow for more robust evaluation than is possible on other platforms.

Erratum: Due to a collection error discovered after publication, some tweets in this dataset were truncated. This mainly affected the precise counts of top hashtags and may also have had a minor impact on the order in which hashtags were ranked. We do not believe the impact is material to the study’s overall findings.