Tunazzina Islam

Tunazzina Islam

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

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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

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

Abstract

The landscape of social media is highly dynamic, with users generating and consuming a diverse range of content. Various interest groups, including politicians, advertisers, and stakeholders, utilize these platforms to target potential users to advance their interests by adapting their messaging. This process, known as microtargeting, relies on data-driven techniques that exploit the rich information collected by social networks about their users. Microtargeting is a double-edged sword; while it enhances the relevance and efficiency of targeted content, it also poses challenges. There is the risk of influencing user behavior and perceptions, fostering echo chambers and polarization. Understanding these impacts is crucial for promoting healthy public discourse in the digital age and maintaining a cohesive society. My work focuses on developing computational frameworks for better understanding of microtargeting and activity patterns on social media. To analyze the impacts of microtargeting, understanding messaging from both the sender’s and recipient’s perspectives is essential. For the sender, we need to know what are their motivations. For the recipient, we need to know something about their demographic properties and interests, according to which we hypothesize that messaging would change.

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

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