@conference {1548867,
	title = {Social Media Attributions in the Context of Water Crisis},
	booktitle = {IJCAI 2021 Workshop on AI for Social Good},
	year = {2021},
	abstract = {Attribution of natural disasters/collective misfortunes is a widely studied social science problem. At present, most such studies rely on surveys or external signals such as voting outcomes. Typically, these surveys are costly to conduct and often have considerable turnaround time. In contrast, procuring social media data is vastly cheaper and can be obtained at varying spatiotemporal granularity. In this paper, we describe our recent work1 that looked into the viability of estimating attributions through social media discussions. To this end, (1) we fo- cus on the 2019 Chennai water crisis, a major in- stance of recent environmental resource crisis; (2) construct a substantial corpus of 72,098 YouTube comments posted by 43,859 users on 623 videos relevant to the crisis; (3) define a novel natural language processing task of attribution tie detection; and (4) design a neural classifier that achieves a reasonable performance. We also release the first data set on this novel task and important domain.},
	author = {Rupak Sarkar and Sayantan Mahinder and Hirak Sarkar and Ashiqur R. KhudaBukhsh}
}
