# Out of the Echo Chamber: Detecting Countering Debate Speeches

arXiv: https://arxiv.org/abs/2005.01157 · HTML: https://mlsanity.com/paper/2005.01157/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2020.

## Built on by (most cited first, 15 of 15)

- [Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks](https://mlsanity.com/paper/2409.19338/) (2024)
- [Argument Undermining: Counter-Argument Generation by Attacking Weak Premises](https://mlsanity.com/paper/2105.11752/) (2021)
- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation](https://mlsanity.com/paper/2406.03151/) (2024)
- [A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation](https://mlsanity.com/paper/2112.05084/) (2021)
- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [Large Language Models Often Say One Thing and Do Another](https://mlsanity.com/paper/2503.07003/) (2025)
- [Knowledge-Enhanced Evidence Retrieval for Counterargument Generation](https://mlsanity.com/paper/2109.09057/) (2021)
- [Topic Ontologies for Arguments](https://mlsanity.com/paper/2301.09759/) (2023)
- [Debatable Intelligence: Benchmarking LLM Judges via Debate Speech Evaluation](https://mlsanity.com/paper/2506.05062/) (2025)
- [SparseCL: Sparse Contrastive Learning for Contradiction Retrieval](https://mlsanity.com/paper/2406.10746/) (2024)
- [Conversational Prompt Engineering](https://mlsanity.com/paper/2408.04560/) (2024)
- [Conclusion-based Counter-Argument Generation](https://mlsanity.com/paper/2301.09911/) (2023)
- [Revisiting the Role of Similarity and Dissimilarity in Best Counter Argument Retrieval](https://mlsanity.com/paper/2304.08807/) (2023)
- [Multimodal Propaganda Processing](https://mlsanity.com/paper/2302.08709/) (2023)

## Abstract (arXiv)

An educated and informed consumption of media content has become a challenge in modern times. With the shift from traditional news outlets to social media and similar venues, a major concern is that readers are becoming encapsulated in "echo chambers" and may fall prey to fake news and disinformation, lacking easy access to dissenting views. We suggest a novel task aiming to alleviate some of these concerns -- that of detecting articles that most effectively counter the arguments -- and not just the stance -- made in a given text. We study this problem in the context of debate speeches. Given such a speech, we aim to identify, from among a set of speeches on the same topic and with an opposing stance, the ones that directly counter it. We provide a large dataset of 3,685 such speeches (in English), annotated for this relation, which hopefully would be of general interest to the NLP community. We explore several algorithms addressing this task, and while some are successful, all fall short of expert human performance, suggesting room for further research. All data collected during this work is freely available for research.