18 citations · 54 across the 31 of their papers we have counts for
33 papers
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…
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…
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…
Community Needs and Assets: A Computational Analysis of Community Conversations
Md Towhidul Absar Chowdhury, Naveen Sharma, Ashiqur R. KhudaBukhsh
A community needs assessment is a tool used by non-profits and government agencies to quantify the strengths and issues of a community, allowing them to allocate their resources be…
Infrastructure Ombudsman: Mining Future Failure Concerns from Structural Disaster Response
Md Towhidul Absar Chowdhury, Soumyajit Datta, Naveen Sharma +1
Current research concentrates on studying discussions on social media related to structural failures to improve disaster response strategies. However, detecting social web posts di…
Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling
Clay H. Yoo, Ashiqur R. KhudaBukhsh
This paper makes two key contributions. First, it argues that highly specialized rare content classifiers trained on small data typically have limited exposure to the richness and…
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…
Disentangling Societal Inequality from Model Biases: Gender Inequality in Divorce Court Proceedings
Sujan Dutta, Parth Srivastava, Vaishnavi Solunke +2
Divorce is the legal dissolution of a marriage by a court. Since this is usually an unpleasant outcome of a marital union, each party may have reasons to call the decision to quit…
Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning
Tharindu Cyril Weerasooriya, Sarah Luger, Saloni Poddar +2
Human-annotated data plays a critical role in the fairness of AI systems, including those that deal with life-altering decisions or moderating human-created web/social media conten…
For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis of a Watershed Moment in Iran's Gender Struggles
Adel Khorramrouz, Sujan Dutta, Ashiqur R. KhudaBukhsh
In this paper, we present a computational analysis of the Persian language Twitter discourse with the aim to estimate the shift in stance toward gender equality following the death…
Quantifying the Academic Quality of Children's Videos using Machine Comprehension
Sumeet Kumar, Mallikarjuna T., Ashiqur Khudabukhsh
YouTube Kids (YTK) is one of the most popular kids' applications used by millions of kids daily. However, various studies have highlighted concerns about the videos on the platform…
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…
How is Vaping Framed on Online Knowledge Dissemination Platforms?
Keyu Chen, Yiwen Shi, Jun Luo +7
We analyze 1,888 articles and 1,119,453 vaping posts to study how vaping is framed across multiple knowledge dissemination platforms (Wikipedia, Quora, Medium, Reddit, Stack Exchan…
US News and Social Media Framing around Vaping
Keyu Chen, Marzieh Babaeianjelodar, Yiwen Shi +9
In this paper, we investigate how vaping is framed differently (2008-2021) between US news and social media. We analyze 15,711 news articles and 1,231,379 Facebook posts about vapi…
'Beach' to 'Bitch': Inadvertent Unsafe Transcription of Kids' Content on YouTube
Krithika Ramesh, Ashiqur R. KhudaBukhsh, Sumeet Kumar
Over the last few years, YouTube Kids has emerged as one of the highly competitive alternatives to television for children's entertainment. Consequently, YouTube Kids' content shou…
Empathy and Hope: Resource Transfer to Model Inter-country Social Media Dynamics
Clay H. Yoo, Shriphani Palakodety, Rupak Sarkar +1
The ongoing COVID-19 pandemic resulted in significant ramifications for international relations ranging from travel restrictions, global ceasefires, and international vaccine produ…
Exploring Polarization of Users Behavior on Twitter During the 2019 South American Protests
Ramon Villa-Cox, Helen, Zeng +2
Research across different disciplines has documented the expanding polarization in social media. However, much of it focused on the US political system or its culturally controvers…