# Social Media Attributions in the Context of Water Crisis

arXiv: https://arxiv.org/abs/2001.01697 · HTML: https://mlsanity.com/paper/2001.01697/

## Key facts

- Cited by 3 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 6 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [Hope Speech detection in under-resourced Kannada language](https://mlsanity.com/paper/2108.04616/) (2021)
- [Infrastructure Ombudsman: Mining Future Failure Concerns from Structural Disaster Response](https://mlsanity.com/paper/2402.13528/) (2024)
- [Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs](https://mlsanity.com/paper/2401.16638/) (2024)

## Abstract (arXiv)

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).