# Quantifying Influencer Impact on Affective Polarization

arXiv: https://arxiv.org/abs/2405.15893 · HTML: https://mlsanity.com/paper/2405.15893/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2024.

## Built on by (most cited first, 6 of 6)

- [From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Security](https://mlsanity.com/paper/2603.04723/) (2026)
- [Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild](https://mlsanity.com/paper/2603.04727/) (2026)
- [Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software](https://mlsanity.com/paper/2606.20502/) (2026)
- [Diverse LLMs vs. Vulnerabilities: Who Detects and Fixes Them Better?](https://mlsanity.com/paper/2512.12536/) (2025)
- [Measuring Social Media Polarization Using Large Language Models and Heuristic Rules](https://mlsanity.com/paper/2601.00927/) (2026)
- [VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detection](https://mlsanity.com/paper/2608.05069/) (2026)

## Abstract (arXiv)

In today's digital age, social media platforms play a crucial role in shaping public opinion. This study explores how discussions led by influencers on Twitter, now known as 'X', affect public sentiment and contribute to online polarization. We developed a counterfactual framework to analyze the polarization scores of conversations in scenarios both with and without the presence of an influential figure. Two case studies, centered on the polarizing issues of climate change and gun control, were examined. Our research highlights the significant impact these figures have on public discourse, providing valuable insights into how online discussions can influence societal divisions.