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).

中文翻译：

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水危机背景下的社交媒体归因

自然灾害/集体不幸的归因是一个广泛研究的政治学问题。然而，此类研究通常以调查为中心，或者依赖少数专家对此事进行权衡。在本文中，我们探讨了如何使用社交媒体数据和人工智能驱动的方法来补充传统调查并自动提取归因因素。我们关注最近的钦奈水危机，该危机最初是一个区域性问题，但在令人震惊的水危机统计数据之后迅速升级为具有全球重要性的讨论话题。具体来说，我们提出了一种新的归因关系检测预测任务，它确定了造成危机的因素（例如，糟糕的城市规划、人口爆炸等）。在由 YouTube 评论构建的具有挑战性的数据集上 (72,