7 papers
FBHM: Functional Benchmarking and Steering of VLMs for Hateful Meme Detection
Paramananda Bhaskar, Naquee Rizwan, Daksh Jogchand +2
Hateful meme detection remains a formidable challenge for vision-language models, as existing benchmarks are structurally observational - confounding rhetorical hate mechanisms wit…
Real-World Challenges in Fake News Detection: Dealing with Posts by Cold Users
Sai Keerthana Karnam, Abhirup Kundu, Jashn Arora +2
Social media serves as a primary source of information in the current digital era. Many people consume a vast range of information in a very short span, yet, amidst the stream of g…
STEMTOX: From Collaborative Tags to Fine-Grained Toxic Meme Detection via Entropy-Guided Multi-Task Learning
Subhankar Swain, Naquee Rizwan, Vishwa Gangadhar S +2
Memes, as a widely used mode of online communication, often serve as vehicles for spreading harmful content. However, limitations in data accessibility and the high costs of datase…
See, Explain, and Intervene: A Few-Shot Multimodal Agent Framework for Hateful Meme Moderation
Naquee Rizwan, Subhankar Swain, Paramananda Bhaskar +3
In this work, we examine hateful memes from three complementary angles - how to detect them, how to explain their content and how to intervene them prior to being posted - by apply…
Toxicity Begets Toxicity: Unraveling Conversational Chains in Political Podcasts
Naquee Rizwan, Nayandeep Deb, Sarthak Roy +3
Tackling toxic behavior in digital communication continues to be a pressing concern for both academics and industry professionals. While significant research has explored toxicity…
HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation
Naquee Rizwan, Seid Muhie Yimam, Daryna Dementieva +11
Despite regulations imposed by nations and social media platforms, e.g. (Government of India, 2021; European Parliament and Council of the European Union, 2022), inter alia, hatefu…