@inproceedings{orbach-etal-2020-echo,
title = "Out of the Echo Chamber: {D}etecting Countering Debate Speeches",
author = "Orbach, Matan and
Bilu, Yonatan and
Toledo, Assaf and
Lahav, Dan and
Jacovi, Michal and
Aharonov, Ranit and
Slonim, Noam",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.633",
doi = "10.18653/v1/2020.acl-main.633",
pages = "7073--7086",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Out of the Echo Chamber: Detecting Countering Debate Speeches
%A Orbach, Matan
%A Bilu, Yonatan
%A Toledo, Assaf
%A Lahav, Dan
%A Jacovi, Michal
%A Aharonov, Ranit
%A Slonim, Noam
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F orbach-etal-2020-echo
%X 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.
%R 10.18653/v1/2020.acl-main.633
%U https://aclanthology.org/2020.acl-main.633
%U https://doi.org/10.18653/v1/2020.acl-main.633
%P 7073-7086
Markdown (Informal)
[Out of the Echo Chamber: Detecting Countering Debate Speeches](https://aclanthology.org/2020.acl-main.633) (Orbach et al., ACL 2020)
ACL
- Matan Orbach, Yonatan Bilu, Assaf Toledo, Dan Lahav, Michal Jacovi, Ranit Aharonov, and Noam Slonim. 2020. Out of the Echo Chamber: Detecting Countering Debate Speeches. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7073–7086, Online. Association for Computational Linguistics.