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Amphan - Analyzing Experiences of Extreme Weather Events using Online Data
The DSSG Solve team, in collaboration with the International Water Management Institute (IWMI), aims to develop research methodologies that leverage online social media data to better understand the experiences and needs of vulnerable communities affected by natural disasters. This research tool would enable users to tap into the online discourse and social, political, and economic effects of extreme weather events. Through our goal, we hope to derive actionable insights to supplement on-the-ground efforts and help inform critical policy-making processes.
Summarization of Microblogs for Disaster and Emergency Preparedness
Our goal was to analyze existing extractive summarization algorithms and devise novel methods for summarizing the large volumes of microblogs (tweets) posted during disaster events with implications for relief efforts.
Advisor: Dr. Saptarshi Ghosh, IIT Kharagpur, India.
SMERP Data Challenge Track - ECIR 2017
The motivation of the data challenge is to promote development of IR methodologies that can be used to extract important information from social media during emergency events, and to arrange for comparative evaluation of the methodologies.
publications
Summarizing Microblogs for Emergency Relief and Preparedness
In this paper, we propose a semi-automatic method which exploits a combination of SumBasic Summarizer and different classifiers to summarize the topic wise relevant microblogs (tweets), extracted through manually identified query term matching.
Mehra, K. and V. Chandra. (2017). "Summarizing Microblogs for Emergency Relief and Preparedness." SMERP@ECIR.
Ensemble Algorithms for Microblog Summarization
In this paper, we propose two ensemble schemes that can combine the outputs of multiple base summarization algorithms, to produce summaries that are better than what is obtained from any of the individual base algorithms.
S. Dutta, V. Chandra, K. Mehra, A. K. Das, T. Chakraborty and S. Ghosh. (2018). "Ensemble Algorithms for Microblog Summarization." IEEE Intelligent Systems, vol. 33, no. 3, pp. 4–14. https://ieeexplore.ieee.org/document/8423533
Summarizing Microblogs During Emergency Events: A Comparison of Extractive Summarization Algorithms
In this paper, we evaluate and compare the performance of eight extractive summarization algorithms in the application of summarizing microblogs posted during emergency events.
Dutta, S., Chandra, V., Mehra, K., Ghatak, S., Das, A. & Ghosh, S. (2019). "Summarizing Microblogs during Emergency Events: A Comparison of Extractive Summarization Algorithms." International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS), pp. 859-872. https://link.springer.com/chapter/10.1007%2F978-981-13-1498-8_76
Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse
In this paper, we contribute two novel methodologies that leverage Twitter discourse to characterize narratives and identify unmet needs in response to Cyclone Amphan, which affected 18 million people in May 2020.
Crayton, A., Fonseca, J., Mehra, K., Ng, M., Ross, J., Sandoval-Castañeda, M., & von Gnechten, R. (2020). "Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse." Tackling Climate Change with Machine Learning Workshop at NeurIPS 2020. AI for Social Good Workshop at IJCAI 2020. https://www.climatechange.ai/papers/neurips2020/42
Deliberation and Voting in Approval-Based Multi-Winner Elections
Abstract: Citizen-focused democratic processes where participants deliberate on alternatives and then vote to make the final decision are increasingly popular today. While the computational social choice literature has extensively investigated voting rules, there is limited work that explicitly looks at the interplay of the deliberative process and voting. In this paper, we build a deliberation model using established models from the opinion-dynamics literature and study the effect of different deliberation mechanisms on voting outcomes achieved when using well-studied voting rules. Our results show that deliberation generally improves welfare and representation guarantees, but the results are sensitive to how the deliberation process is organized. We also show, experimentally, that simple voting rules, such as approval voting, perform as well as more sophisticated rules such as proportional approval voting or method of equal shares if deliberation is properly supported. This has ramifications on the practical use of such voting rules in citizen-focused democratic processes.
Mehra, K., Sreenivas, N., Larson, K. (2023). Deliberation and Voting in Approval-Based Multi-Winner Elections. International Joint Conference on Artificial Intelligence (IJCAI) 2023 Main Track https://arxiv.org/pdf/2305.08970.pdf