Social Media Attributions in the Context of Water Crisis

Rupak Sarkar, Sayantan Mahinder, Hirak Sarkar, Ashiqur KhudaBukhsh


Abstract
Attribution of natural disasters/collective misfortune is a widely-studied political science problem. However, such studies typically rely on surveys, or expert opinions, or external signals such as voting outcomes. In this paper, we explore the viability of using unstructured, noisy social media data to complement traditional surveys through automatically extracting attribution factors. We present a novel prediction task of attribution tie detection of identifying the factors (e.g., poor city planning, exploding population etc.) held responsible for the crisis in a social media document. We focus on the 2019 Chennai water crisis that rapidly escalated into a discussion topic with global importance following alarming water-crisis statistics. On a challenging data set constructed from YouTube comments (72,098 comments posted by 43,859 users on 623 videos relevant to the crisis), we present a neural baseline to identify attribution ties that achieves a reasonable performance (accuracy: 87.34% on attribution detection and 81.37% on attribution resolution). We release the first annotated data set of 2,500 comments in this important domain.
Anthology ID:
2020.emnlp-main.109
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1402–1412
Language:
URL:
https://aclanthology.org/2020.emnlp-main.109
DOI:
10.18653/v1/2020.emnlp-main.109
Bibkey:
Cite (ACL):
Rupak Sarkar, Sayantan Mahinder, Hirak Sarkar, and Ashiqur KhudaBukhsh. 2020. Social Media Attributions in the Context of Water Crisis. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1402–1412, Online. Association for Computational Linguistics.
Cite (Informal):
Social Media Attributions in the Context of Water Crisis (Sarkar et al., EMNLP 2020)
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PDF:
https://aclanthology.org/2020.emnlp-main.109.pdf
Video:
 https://slideslive.com/38939332