# In-Group Love, Out-Group Hate: A Framework to Measure Affective Polarization via Contentious Online Discussions

arXiv: https://arxiv.org/abs/2412.14414 · HTML: https://mlsanity.com/paper/2412.14414/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 18 Dec 2024.

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

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Discovering Polarized Communities in Signed Networks](https://mlsanity.com/paper/1910.02438/) (2019)
- [Modeling echo chambers and polarization dynamics in social networks](https://mlsanity.com/paper/1906.12325/) (2019)
- [Affective Polarization and Dynamics of Information Spread in Online Networks](https://mlsanity.com/paper/2310.18553/) (2023)
- [Spreading and Structural Balance on Signed Networks](https://mlsanity.com/paper/2212.10158/) (2022)
- [Pandemic Culture Wars: Partisan Differences in the Moral Language of COVID-19 Discussions](https://mlsanity.com/paper/2305.18533/) (2023)
- [Unpacking polarization: Antagonism and Alignment in Signed Networks of Online Interaction](https://mlsanity.com/paper/2307.06571/) (2023)
- [What Do Your Friends Think? Efficient Polling Methods for Networks Using Friendship Paradox](https://mlsanity.com/paper/1802.06505/) (2018)
- [Friendship Paradox Biases Perceptions in Directed Networks](https://mlsanity.com/paper/1905.05286/) (2019)
- [The Enmity Paradox](https://mlsanity.com/paper/2304.10076/) (2023)

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

- [A Natural Language Agentic Approach to Study Affective Polarization](https://mlsanity.com/paper/2603.02711/) (2026)
- [Gendered Divides in Online Discussions about Reproductive Rights](https://mlsanity.com/paper/2507.05443/) (2025)
- [Dynamics of Human-AI Collective Knowledge on the Web: A Scalable Model and Insights for Sustainable Growth](https://mlsanity.com/paper/2601.20099/) (2026)

## Abstract (arXiv)

Affective polarization, the emotional divide between ideological groups marked by in-group love and out-group hate, has intensified in the United States, driving contentious issues like masking and lockdowns during the COVID-19 pandemic. Despite its societal impact, existing models of opinion change fail to account for emotional dynamics nor offer methods to quantify affective polarization robustly and in real-time. In this paper, we introduce a discrete choice model that captures decision-making within affectively polarized social networks and propose a statistical inference method estimate key parameters -- in-group love and out-group hate -- from social media data. Through empirical validation from online discussions about the COVID-19 pandemic, we demonstrate that our approach accurately captures real-world polarization dynamics and explains the rapid emergence of a partisan gap in attitudes towards masking and lockdowns. This framework allows for tracking affective polarization across contentious issues has broad implications for fostering constructive online dialogues in digital spaces.