# Dimensions of Online Conflict: Towards Modeling Agonism

arXiv: https://arxiv.org/abs/2311.03584 · HTML: https://mlsanity.com/paper/2311.03584/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 69 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://mlsanity.com/paper/2111.07997/) (2021)
- [Directions in Abusive Language Training Data: Garbage In, Garbage Out](https://mlsanity.com/paper/2004.01670/) (2020)
- [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016)
- [Conversations Gone Awry: Detecting Early Signs of Conversational Failure](https://mlsanity.com/paper/1805.05345/) (2018)
- [Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreement](https://mlsanity.com/paper/2109.13563/) (2021)
- [Sarcasm Analysis using Conversation Context](https://mlsanity.com/paper/1808.07531/) (2018)
- [A Web of Hate: Tackling Hateful Speech in Online Social Spaces](https://mlsanity.com/paper/1709.10159/) (2017)
- [The language of opinion change on social media under the lens of communicative action](https://mlsanity.com/paper/2210.17234/) (2022)
- [Content Removal as a Moderation Strategy: Compliance and Other Outcomes in the ChangeMyView Community](https://mlsanity.com/paper/1910.09563/) (2019)
- [From Toxicity in Online Comments to Incivility in American News: Proceed with Caution](https://mlsanity.com/paper/2102.03671/) (2021)
- [Modeling the Severity of Complaints in Social Media](https://mlsanity.com/paper/2103.12428/) (2021)
- [Hate Raids on Twitch: Echoes of the Past, New Modalities, and Implications for Platform Governance](https://mlsanity.com/paper/2301.03946/) (2023)
- [The Effect of Sociocultural Variables on Sarcasm Communication Online](https://mlsanity.com/paper/2004.04945/) (2020)
- [Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Model](https://mlsanity.com/paper/2109.10033/) (2021)

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

- [The geometry of online conversations and the causal antecedents of conflictual discourse](https://mlsanity.com/paper/2602.15600/) (2026)
- [Power Echoes: Investigating Moderation Biases in Online Power-Asymmetric Conflicts](https://mlsanity.com/paper/2603.01457/) (2026)

## Abstract (arXiv)

Agonism plays a vital role in democratic dialogue by fostering diverse perspectives and robust discussions. Within the realm of online conflict there is another type: hateful antagonism, which undermines constructive dialogue. Detecting conflict online is central to platform moderation and monetization. It is also vital for democratic dialogue, but only when it takes the form of agonism. To model these two types of conflict, we collected Twitter conversations related to trending controversial topics. We introduce a comprehensive annotation schema for labelling different dimensions of conflict in the conversations, such as the source of conflict, the target, and the rhetorical strategies deployed. Using this schema, we annotated approximately 4,000 conversations with multiple labels. We then trained both logistic regression and transformer-based models on the dataset, incorporating context from the conversation, including the number of participants and the structure of the interactions. Results show that contextual labels are helpful in identifying conflict and make the models robust to variations in topic. Our research contributes a conceptualization of different dimensions of conflict, a richly annotated dataset, and promising results that can contribute to content moderation.