Issue 3

A significant body of research is dedicated to developing language models that can detect various types of online abuse, for example, hate speech, cyberbullying. However, there is a disconnect between platform policies, which often consider the author’s intention as a criterion for content moderation, and the current capabilities of detection models, which typically lack efforts to capture intent.

How effective are user corrections in combatting misinformation on social media, and does adding a link to a fact check improve their effectiveness? We conducted a pre-registered online experiment on representative samples of the online population in Brazil, India, and the United Kingdom (N participants = 3,000, N observations = 24,000).

Misinformation research often draws optimistic conclusions, with fact-checking, for example, being established as an effective means of reducing false beliefs. However, it rarely considers the details of socioeconomic disparities that often shape who is most vulnerable to science misinformation. Historical and systemic inequalities have fostered mistrust in institutions, limiting access to credible information, for example, when Black patients distrust public health guidance due to past medical racism.

Following the leadership transition on October 27, 2022, Twitter/X underwent a notable change in platform governance. This study investigates how these changes influenced information quality for registered U.S. voters and the platform more broadly. We address this question by analyzing two complementary datasets—a Twitter panel and a Decahose sample.

The potential for harm entrenched in mis- and disinformation content, regardless of intentionality, opens space for a new analytical agenda to investigate the weaponization of identity-based features like gender, race, and ethnicity through the lens of violence. Therefore, we lay out the triangle of violence to support new studies aiming to investigate multimedia content, victims, and audiences of false claims.