Tunazzina Islam

Tunazzina Islam

Ph.D. in Computer Science, Purdue University, West Lafayette.

Posts by Collection

portfolio

publications

Ex-Twit: Explainable Twitter Mining on Health Data

Published in The 7th International Workshop on Natural Language Processing for Social Media (SocialNLP 2019) In conjunction with 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), 1900

[arXiv] [PDF] [Slide]

Recommended citation: Tunazzina Islam. 7th International Workshop on Natural Language Processing for Social Media (SocialNLP 2019) @ IJCAI-2019

Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA

Published in Preprint 2026, 1900

Abstract

Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characterized by dense legal language and evolving, overlapping regulatory frameworks. We study the application of RAG to AI governance and policy analysis using the AI Governance and Regulatory Archive (AGORA) corpus, a curated collection of 947 AI policy documents. Our system combines a ColBERT-based retriever fine-tuned with contrastive learning and a generator aligned to human preferences using Direct Preference Optimization (DPO). We construct synthetic queries and collect pairwise preferences to adapt the system to the policy domain. Through experiments evaluating retrieval quality, answer relevance, and faithfulness, we find that domain-specific fine-tuning improves retrieval metrics but does not consistently improve end-to-end question answering performance. In some cases, stronger retrieval counterintuitively leads to more confident hallucinations when relevant documents are absent from the corpus. These results highlight a key concern for those building policy-focused RAG systems: improvements to individual components do not necessarily translate to more reliable answers. Our findings provide practical insights for designing grounded question-answering systems over dynamic regulatory corpora.

Recommended citation: Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur, Daniel Stuart Schiff, Tunazzina Islam. Under Review.

Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging

Published in 6th AAAI/ACM Conference on AI, Ethics, and Society 2023 ([AIES-2023](https://www.aies-conference.com/2023/)), 1900

[Paper link] [arXiv] [Slide]

Abstract

Climate change is the defining issue of our time, and we are at a defining moment. Various interest groups, social movement organizations, and individuals engage in collective action on this issue on social media. In addition, issue advocacy campaigns on social media often arise in response to ongoing societal concerns, especially those faced by energy industries. Our goal in this paper is to analyze how those industries, their advocacy group, and climate advocacy group use social media to influence the narrative on climate change. In this work, we propose a minimally supervised model soup [57] approach combined with messaging themes to identify the stances of climate ads on Facebook. Finally, we release our stance dataset, model, and set of themes related to climate campaigns for future work on opinion mining and the automatic detection of climate change stances.

Recommended citation: Tunazzina Islam, Ruqi Zhang, Dan Goldwasser. 6th AAAI/ACM Conference on AI, Ethics, and Society 2023 (AIES-2023).

A Holistic Framework for Analyzing the COVID-19 Vaccine Debate

Published in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ([NAACL 2022](https://2022.naacl.org/)), 1900

[Paper link] [arXiv] [Slide]

Recommended citation: Maria Leonor Pacheco*, Tunazzina Islam*, Monal Mahajan, Andrey Shor, Ming Yin, Lyle Ungar, Dan Goldwasser.Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2022), 5821–583.

Understanding Microtargeting Pattern on Social Media

Published in 30th AAAI/SIGAI Doctoral Consortium ([AAAI-25 Doctoral Consortium](https://aaai.org/conference/aaai/aaai-25/doctoral-consortium-call/)), 1900

🏆 Won the best poster award in 2025 AAAI/SIGAI Doctoral Consortium

Recommended citation: Tunazzina Islam. AAAI-25 Doctoral Consortium.

talks

teaching