  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).
