activity
20192026
most citedVicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive

13 citations · 36 across the 30 of their papers we have counts for

collaborators

33 papers

cs.CL2026

Two Centuries of Sexism in British Parliament: A Computational Analysis of Women's Representation in the Hansard Corpus

Mohammad Omar Khursheed, Mandira Sawkar, Ashiqur R. KhudaBukhsh

The language a legislature uses to debate women's rights, even in favour of them, encodes systematic patterns of sexism that persist across two centuries. In this work, we analyse…

cs.CY2026

Investigating Vaccine Buyer's Remorse: Post-Vaccination Decision Regret in COVID-19 Social Media Using Politically Diverse Human Annotation

Miles Stanley, Soumyajit Datta, Ashutosh Kumar +1

A significant gap exists in datasets regarding post-COVID-19 vaccination experiences, particularly ``vaccine buyer's remorse''. Understanding the prevalence and nature of vaccine r…

cs.CL2025★ 1 cited

What About the Scene with the Hitler Reference? HAUNT: A Framework to Probe LLMs' Self-consistency Via Adversarial Nudge

Arka Dutta, Sujan Dutta, Rijul Magu +3

Hallucinations pose a critical challenge to the real-world deployment of large language models (LLMs) in high-stakes domains. In this paper, we present a framework for stress testi…

cs.CL2025

Navigating the Rabbit Hole: Emergent Biases in LLM-Generated Attack Narratives Targeting Mental Health Groups

Rijul Magu, Arka Dutta, Sean Kim +2

Large Language Models (LLMs) have been shown to demonstrate imbalanced biases against certain groups. However, the study of unprovoked targeted attacks by LLMs towards at-risk popu…

cs.CL2025

Datasets for Depression Modeling in Social Media: An Overview

Ana-Maria Bucur, Andreea-Codrina Moldovan, Krutika Parvatikar +3

Depression is the most common mental health disorder, and its prevalence increased during the COVID-19 pandemic. As one of the most extensively researched psychological conditions,…

cs.CL2025

Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speech

Jonathan Pofcher, Christopher M. Homan, Randall Sell +1

This paper makes three contributions. First, via a substantial corpus of 1,419,047 comments posted on 3,161 YouTube news videos of major US cable news outlets, we analyze how users…