18 citations · 54 across the 31 of their papers we have counts for
15 papers · 1 filter
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…
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…
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…
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,…
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…
On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook
Ana-Maria Bucur, Andreea-Codrina Moldovan, Krutika Parvatikar +3
Computational approaches to predicting mental health conditions in social media have been substantially explored in the past years. Multiple reviews have been published on this top…
ARTICLE: Annotator Reliability Through In-Context Learning
Sujan Dutta, Deepak Pandita, Tharindu Cyril Weerasooriya +3
Ensuring annotator quality in training and evaluation data is a key piece of machine learning in NLP. Tasks such as sentiment analysis and offensive speech detection are intrinsica…
Gender Representation and Bias in Indian Civil Service Mock Interviews
Somonnoy Banerjee, Sujan Dutta, Soumyajit Datta +1
This paper makes three key contributions. First, via a substantial corpus of 51,278 interview questions sourced from 888 YouTube videos of mock interviews of Indian civil service c…
Rater Cohesion and Quality from a Vicarious Perspective
Deepak Pandita, Tharindu Cyril Weerasooriya, Sujan Dutta +5
Human feedback is essential for building human-centered AI systems across domains where disagreement is prevalent, such as AI safety, content moderation, or sentiment analysis. Man…
Down the Toxicity Rabbit Hole: A Novel Framework to Bias Audit Large Language Models
Arka Dutta, Adel Khorramrouz, Sujan Dutta +1
This paper makes three contributions. First, it presents a generalizable, novel framework dubbed \textit{toxicity rabbit hole} that iteratively elicits toxic content from a wide su…
Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive
Tharindu Cyril Weerasooriya, Sujan Dutta, Tharindu Ranasinghe +3
Offensive speech detection is a key component of content moderation. However, what is offensive can be highly subjective. This paper investigates how machine and human moderators d…
Are Chess Discussions Racist? An Adversarial Hate Speech Data Set
Rupak Sarkar, Ashiqur R. KhudaBukhsh
On June 28, 2020, while presenting a chess podcast on Grandmaster Hikaru Nakamura, Antonio Radić's YouTube handle got blocked because it contained "harmful and dangerous" content.…
We Don't Speak the Same Language: Interpreting Polarization through Machine Translation
Ashiqur R. KhudaBukhsh, Rupak Sarkar, Mark S. Kamlet +1
Polarization among US political parties, media and elites is a widely studied topic. Prominent lines of prior research across multiple disciplines have observed and analyzed growin…
Discovering Bilingual Lexicons in Polyglot Word Embeddings
Ashiqur R. KhudaBukhsh, Shriphani Palakodety, Tom M. Mitchell
Bilingual lexicons and phrase tables are critical resources for modern Machine Translation systems. Although recent results show that without any seed lexicon or parallel data, hig…
Harnessing Code Switching to Transcend the Linguistic Barrier
Ashiqur R. KhudaBukhsh, Shriphani Palakodety, Jaime G. Carbonell
Code mixing (or code switching) is a common phenomenon observed in social-media content generated by a linguistically diverse user-base. Studies show that in the Indian sub-contine…