Publications

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

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

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

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 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.

Kanav Mehra