Social Media Attributions in the Context of Water Crisis
- URL: http://arxiv.org/abs/2001.01697v1
- Date: Mon, 6 Jan 2020 18:20:09 GMT
- Title: Social Media Attributions in the Context of Water Crisis
- Authors: Rupak Sarkar, Hirak Sarkar, Sayantan Mahinder, Ashiqur R. KhudaBukhsh
- Abstract summary: We explore how can we use social media data and an AI-driven approach to complement traditional surveys and automatically extract attribution factors.
We focus on the most-recent Chennai water crisis which started off as a regional issue but rapidly escalated into a discussion topic.
We present a novel prediction task of attribution tie detection which identifies the factors held responsible for the crisis.
- Score: 13.5346836945515
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Attribution of natural disasters/collective misfortune is a widely-studied
political science problem. However, such studies are typically survey-centric
or rely on a handful of experts to weigh in on the matter. In this paper, we
explore how can we use social media data and an AI-driven approach to
complement traditional surveys and automatically extract attribution factors.
We focus on the most-recent Chennai water crisis which started off as a
regional issue but rapidly escalated into a discussion topic with global
importance following alarming water-crisis statistics. Specifically, we present
a novel prediction task of attribution tie detection which identifies the
factors held responsible for the crisis (e.g., poor city planning, exploding
population etc.). On a challenging data set constructed from YouTube comments
(72,098 comments posted by 43,859 users on 623 relevant videos to the crisis),
we present a neural classifier to extract attribution ties that achieved a
reasonable performance (Accuracy: 81.34\% on attribution detection and 71.19\%
on attribution resolution).
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