Farig Yousuf Sadeque

Work place: BRAC University, Dhaka, 1212, Bangladesh

E-mail: farig.sadeque@bracu.ac.bd

Website: https://orcid.org/0000-0001-6797-7826

Research Interests:

Biography

Farig Yousuf Sadeque was born on January 5, 1990, in Dhaka, Bangladesh. He received his Bachelor’s degree in computer science and engineering from the Bangladesh University of Engineering and Technology, Dhaka, Bangladesh, and his Ph.D. degree in information science with a minor in computer science from The University of Arizona, Tucson, Arizona, USA, under the supervision of Dr. Steven Bethard.
He is currently an Associate Professor with the Department of Computer Science and Engineering at BRAC University, Dhaka, Bangladesh. Before joining BRAC University, he was an Associate Research Scientist at the Educational Testing Service (ETS) and a postdoctoral research fellow at Harvard University, where he was jointly appointed by the Harvard Medical School and Boston Children’s Hospital, working within the Computa- tional Health Informatics Program (CHIP) Laboratory. His research interests include computational linguistics, applied natural language processing, machine learning, computational social science, psycholinguistics, and so- ciolinguistics.

Author Articles
Impact of 2023 Turkey Earthquake Price Hikes: Insightful Socio-Economic Analysis Using Transformer Models and Explainable AI

By Muhammed Yaseen Morshed Adib Md. Tauhid Bin Iqbal Farig Yousuf Sadeque

DOI: https://doi.org/10.5815/ijieeb.2025.05.05, Pub. Date: 8 Oct. 2025

Natural disasters cause economic instability, leading to severe financial hardships for affected communities. The rapid surge in essential goods prices during such events significantly burdens vulnerable populations, highlighting the critical need for timely policy interventions. While understanding public sentiment on economic distress is crucial for effective data-driven policy generation, research specifically analyzing public sentiment on price hikes in such contexts remains limited, often due to a lack of dedicated datasets. To address this, this paper first introduces a novel dataset of social media comments on price hikes related to the 2023 Turkey earthquake. Second, to support data-driven policy-making by quantifying public sentiment, we applied a range of AI models and identified transformer-based models like DistilBERT as particularly effective for sentiment classification. Furthermore, we employ Explainable AI techniques to enhance model trust, enabling policymakers to confidently use these insights to support disaster recovery and economic stabilization in affected regions.

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