Selected Work

Selected Publications

• A cross-jurisdiction abstraction of data-protection laws beyond GDPR.
  Sharma, S., Myers, E., De Carli, Lorenzo, Banerjee, R., & Ray, I. (2026). Local Privacy Laws in a Globalized World. In 16th ACM Conference on Data and Application Security and Privacy. To appear.

• A corpus (spanning 2.5 years, 4 countries, and 3 languages) for cross-national analysis of conflict narratives.
  Mohanty, D., Sabadyn, T., Rodrigues, J., Wang, C., Kalugade, A., & Banerjee, R. (2026). A Longitudinal, Multinational, and Multilingual Corpus of News Coverage of the Russo-Ukrainian War. In Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026) (pp. 6452–6471). European Language Resources Association (ELRA).

• A novel efficient training paradigm for pragmatic language tasks.
  Wang, C., Lyu, W., & Banerjee, R. (2025). Class distillation with mahalanobis contrast: An efficient training paradigm for pragmatic language understanding tasks. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 29428-29442). Association for Computational Linguistics.

• Personalized modeling for inclusive recognition of atypical speech.
  Raja, V., Ganesan, A., V., Syamkumar, A., Banerjee, R., & Schwartz, H. (2025). Idiosyncratic Versus Normative Modeling of Atypical Speech Recognition: Dysarthric Case Studies. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 33514-33525). Association of Computational Linguistics.

• A novel distance measure based on attention weights to distinguish "whataboutism" from other non-deflective pragmatic functions with similar semantics.
  Phi, K., Salek Faramarzi, N., Wang, C., & Banerjee, R. (2024). Paying attention to deflections: Mining pragmatic nuances for whataboutism detection in online discourse. Findings of the Association for Computational Linguistics ACL (pp. 12628-12643). Association for Computational Linguistics.

• Zero-shot stance detection towards claims in social media posts.
  Salek Faramarzi, N., Hashemi Chaleshtori, F., Shirazi, H., Ray, I., & Banerjee, R. (2023). Claim extraction and dynamic stance detection in COVID-19 tweets. Companion Proceedings of the ACM Web Conference (pp. 1059-1068). Association for Computing Machinery.

• Catching plausible-but-deceptive citations before the spread of misinformation.
  Zuo, C., Banerjee, R., Chaleshtori, F. H., Shirazi, H., & Ray, I. (2023). Seeing should probably not be believing: The role of deceptive support in COVID-19 misinformation on Twitter. ACM Journal of Data and Information Quality, 15(1), 1-26. Association for Computing Machinery.

• How readers perceive medical claims, as those claims from move from research to news.
  Zuo, C., Mathur, K., Kela, D., Salek Faramarzi, N., & Banerjee, R. (2022). Beyond belief: a cross-genre study on perception and validation of health information online. International Journal of Data Science and Analytics, 13(4), 299-314. Springer.

• Matching health news claims to the research behind them.
  Zuo, C., Acharya, N., & Banerjee, R. (2020). Querying Across Genres for Medical Claims in News. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1783-1789). Association for Computational Linguistics.

• Deep syntactic structure is a much stronger signal for deception, beyond surface lexical and syntactic cues.
  Feng, S., Banerjee, R., & Choi, Y. (2012). Syntactic Stylometry for Deception Detection. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 171-175). Association for Computational Linguistics.

Dataset & Resources

• Mohanty, D., Sabadyn, T., Rodrigues, J., Wang, C., Kalugade, A., & Banerjee, R. (2026). Diverse Narratives and International Perspectives on the Russo-Ukrainian Offensive (DNIPRO). Zenodo. doi : 10.5281/zenodo.18470676

Full Publication List

• Google Scholar

• CV (PDF)