# Divided We Rule: Influencer Polarization on Twitter During Political Crises in India

arXiv: https://arxiv.org/abs/2105.08361 · HTML: https://mlsanity.com/paper/2105.08361/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 10 of 10)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Accelerated Hierarchical Density Clustering](https://mlsanity.com/paper/1705.07321/) (2017)
- [Quantifying Controversy in Social Media](https://mlsanity.com/paper/1507.05224/) (2015)
- [Embeddings-Based Clustering for Target Specific Stances: The Case of a Polarized Turkey](https://mlsanity.com/paper/2005.09649/) (2020)
- [Who Let The Trolls Out? Towards Understanding State-Sponsored Trolls](https://mlsanity.com/paper/1811.03130/) (2018)
- [Automatic Detection and Categorization of Election-Related Tweets](https://mlsanity.com/paper/1605.05150/) (2016)
- [Rihanna versus Bollywood: Twitter Influencers and the Indian Farmers' Protest](https://mlsanity.com/paper/2102.04031/) (2021)

## Built on by (most cited first, 2 of 2)

- [Same Words, Different Meanings: Semantic Polarization in Broadcast Media Language Forecasts Polarization on Social Media Discourse](https://mlsanity.com/paper/2301.08832/) (2023)
- [Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization](https://mlsanity.com/paper/2402.11895/) (2024)

## Abstract (arXiv)

Influencers are key to the nature and networks of information propagation on social media. Influencers are particularly important in political discourse through their engagement with issues, and may derive their legitimacy either solely or in large part through online operation, or have an offline sphere of expertise such as entertainers, journalists etc. To quantify influencers' political engagement and polarity, we use Google's Universal Sentence Encoder (USE) to encode the tweets of 6k influencers and 26k Indian politicians during political crises in India. We then obtain aggregate vector representations of the influencers based on their tweet embeddings, which alongside retweet graphs help compute their stance and polarity with respect to these political issues. We find that influencers engage with the topics in a partisan manner, with polarized influencers being rewarded with increased retweeting and following. Moreover, we observe that specific groups of influencers are consistently polarized across all events. We conclude by discussing how our study provides insights into the political schisms of present-day India, but also offers a means to study the role of influencers in exacerbating political polarization in other contexts.