Eric Gilbert

CREDBANK: A Large-Scale Social Media Corpus With Associated Credibility Annotations

Proceedings of the International AAAI Conference on Web and Social Media · 2021-08-03 · 244 citations

Social media has quickly risen to prominence as a news source, yet lingering doubts remain about its ability to spread rumor and misinformation. Systematically studying this phenomenon, however, has been difficult due to the need to collect large-scale, unbiased data along with in-situ judgements of its accuracy. In this paper we present CREDBANK, a corpus designed to bridge this gap by systematically combining machine and human computation. Specifically, CREDBANK is a corpus of tweets, topics,…

Quarantined! Examining the Effects of a Community-Wide Moderation Intervention on Reddit

ACM Transactions on Computer-Human Interaction · 2022 · 128 citations

Should social media platforms override a community’s self-policing when it repeatedly break rules? What actions can they consider? In light of this debate, platforms have begun experimenting with softer alternatives to outright bans. We examine one such intervention called quarantining, that impedes direct access to and promotion of controversial communities. Specifically, we present two case studies of what happened when Reddit quarantined the influential communities r/TheRedPill (TRP) and r/Th…

Still out there: Modeling and Identifying Russian Troll Accounts on Twitter

2020 · 85 citations

There is evidence that Russia’s Internet Research Agency attempted to interfere with the 2016 U.S. election by running fake accounts on Twitter—often referred to as “Russian trolls”. In this work, we: 1) develop machine learning models that predict whether a Twitter account is a Russian troll within a set of 170K control accounts; and, 2) demonstrate that it is possible to use this model to find active accounts on Twitter still likely acting on behalf of the Russian state. Using both behavioral…

Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory

2022 ACM Conference on Fairness, Accountability, and Transparency · 2022-06-20 · 61 citations

Understanding how ML models work is a prerequisite for responsibly designing, deploying, and using ML-based systems. With interpretability approaches, ML can now offer explanations for its outputs to aid human understanding. Though these approaches rely on guidelines for how humans explain things to each other, they ultimately solve for improving the artifact—an explanation. In this paper, we propose an alternate framework for interpretability grounded in Weick’s sensemaking theory, which focuse…

Women's Perspectives on Harm and Justice after Online Harassment

Proceedings of the ACM on Human-Computer Interaction · 2022-11-07 · 58 citations

Social media platforms aspire to create online experiences where users can participate safely and equitably. However, women around the world experience widespread online harassment, including insults, stalking, aggression, threats, and non-consensual sharing of sexual photos. This article describes women's perceptions of harm associated with online harassment and preferred platform responses to that harm. We conducted a survey in 14 geographic regions around the world (N = 3,993), focusing on re…