# Linguistic Characterization of Divisive Topics Online: Case Studies on Contentiousness in Abortion, Climate Change, and Gun Control

arXiv: https://arxiv.org/abs/2108.13556 · HTML: https://mlsanity.com/paper/2108.13556/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Conversations Gone Awry: Detecting Early Signs of Conversational Failure](https://mlsanity.com/paper/1805.05345/) (2018)
- [Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings](https://mlsanity.com/paper/1904.01596/) (2019)
- [Something's Brewing! Early Prediction of Controversy-causing Posts from Discussion Features](https://mlsanity.com/paper/1904.07372/) (2019)
- [(Re)construing Meaning in NLP](https://mlsanity.com/paper/2005.09099/) (2020)
- [Explaining Controversy on Social Media via Stance Summarization](https://mlsanity.com/paper/1806.07942/) (2018)
- [Controversy and Sentiment in Online News](https://mlsanity.com/paper/1409.8152/) (2014)
- [Breaking the News: First Impressions Matter on Online News](https://mlsanity.com/paper/1503.07921/) (2015)
- [The Ebb and Flow of Controversial Debates on Social Media](https://mlsanity.com/paper/1703.05994/) (2017)
- [The Effect of Collective Attention on Controversial Debates on Social Media](https://mlsanity.com/paper/1705.05908/) (2017)

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

- [Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation](https://mlsanity.com/paper/2402.15988/) (2024)
- [Causal Effects of Trigger Words in Social Media Discussions: A Large-Scale Case Study about UK Politics on Reddit](https://mlsanity.com/paper/2405.10213/) (2024)
- [TWeddit : A Dataset of Triggering Stories Predominantly Shared by Women on Reddit](https://mlsanity.com/paper/2601.11819/) (2026)
- [A Decade of Climate Polarization on Brazilian YouTube using Language Models](https://mlsanity.com/paper/2609.27811/) (2026)

## Abstract (arXiv)

As public discourse continues to move and grow online, conversations about divisive topics on social media platforms have also increased. These divisive topics prompt both contentious and non-contentious conversations. Although what distinguishes these conversations, often framed as what makes these conversations contentious, is known in broad strokes, much less is known about the linguistic signature of these conversations. Prior work has shown that contentious content and structure can be a predictor for this task, however, most of them have been focused on conversation in general, very specific events, or complex structural analysis. Additionally, many models used in prior work have lacked interpret-ability, a key factor in online moderation. Our work fills these gaps by focusing on conversations from highly divisive topics (abortion, climate change, and gun control), operationalizing a set of novel linguistic and conversational characteristics and user factors, and incorporating them to build interpretable models. We demonstrate that such characteristics can largely improve the performance of prediction on this task, and also enable nuanced interpretability. Our case studies on these three contentious topics suggest that certain generic linguistic characteristics are highly correlated with contentiousness in conversations while others demonstrate significant contextual influences on specific divisive topics.