# A Computational Analysis of Polarization on Indian and Pakistani Social Media

arXiv: https://arxiv.org/abs/2005.09803 · HTML: https://mlsanity.com/paper/2005.09803/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2020.

## Built on by (most cited first, 4 of 4)

- [A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets](https://mlsanity.com/paper/2211.03533/) (2022)
- [ENTRUST: Argument Reframing with Language Models and Entailment](https://mlsanity.com/paper/2103.06758/) (2021)
- [Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity](https://mlsanity.com/paper/2104.08829/) (2021)
- [Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology](https://mlsanity.com/paper/2212.07547/) (2022)

## Abstract (arXiv)

Between February 14, 2019 and March 4, 2019, a terrorist attack in Pulwama, Kashmir followed by retaliatory airstrikes led to rising tensions between India and Pakistan, two nuclear-armed countries. In this work, we examine polarizing messaging on Twitter during these events, particularly focusing on the positions of Indian and Pakistani politicians. We use a label propagation technique focused on hashtag co-occurrences to find polarizing tweets and users. Our analysis reveals that politicians in the ruling political party in India (BJP) used polarized hashtags and called for escalation of conflict more so than politicians from other parties. Our work offers the first analysis of how escalating tensions between India and Pakistan manifest on Twitter and provides a framework for studying polarizing messages.