@inproceedings{fu-ollagnier-2026-theoretically,
title = "A Theoretically Grounded Approach to Summarizing Conversation Dynamics for Forecasting the Derailment of Online Conversations",
author = {Fu, Yingxue and
Ollagnier, Ana{\"i}s},
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.243/",
pages = "5368--5384",
ISBN = "979-8-89176-390-6",
abstract = "Conversation derailment prediction represents a new paradigm of toxicity detection, where a system predicts from the start of a conversation whether it will derail into toxic exchanges, allowing moderators and users to act preemptively before harm is done. This approach requires a deep understanding of conversation dynamics. Previous work relies on linguistic features rooted in linguistic and social theories. While these features provide signals of conversation dynamics, they are exploratory in nature and potentially reflect a fraction of the overall pragmatic devices shaping the conversation trajectory. To capture the pragmatic dimension of conversations systematically, we start with a framework for annotating pragmatic information of conversations systematically and design summary generation methods to capture conversation trajectory dynamically. We achieve about 10{\%} performance increase over a simple baseline, and 6.47{\%} increase over a strong baseline on a dataset, and a slight performance increase on a benchmark dataset for the task of summarizing conversation dynamics."
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<abstract>Conversation derailment prediction represents a new paradigm of toxicity detection, where a system predicts from the start of a conversation whether it will derail into toxic exchanges, allowing moderators and users to act preemptively before harm is done. This approach requires a deep understanding of conversation dynamics. Previous work relies on linguistic features rooted in linguistic and social theories. While these features provide signals of conversation dynamics, they are exploratory in nature and potentially reflect a fraction of the overall pragmatic devices shaping the conversation trajectory. To capture the pragmatic dimension of conversations systematically, we start with a framework for annotating pragmatic information of conversations systematically and design summary generation methods to capture conversation trajectory dynamically. We achieve about 10% performance increase over a simple baseline, and 6.47% increase over a strong baseline on a dataset, and a slight performance increase on a benchmark dataset for the task of summarizing conversation dynamics.</abstract>
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%0 Conference Proceedings
%T A Theoretically Grounded Approach to Summarizing Conversation Dynamics for Forecasting the Derailment of Online Conversations
%A Fu, Yingxue
%A Ollagnier, Anaïs
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F fu-ollagnier-2026-theoretically
%X Conversation derailment prediction represents a new paradigm of toxicity detection, where a system predicts from the start of a conversation whether it will derail into toxic exchanges, allowing moderators and users to act preemptively before harm is done. This approach requires a deep understanding of conversation dynamics. Previous work relies on linguistic features rooted in linguistic and social theories. While these features provide signals of conversation dynamics, they are exploratory in nature and potentially reflect a fraction of the overall pragmatic devices shaping the conversation trajectory. To capture the pragmatic dimension of conversations systematically, we start with a framework for annotating pragmatic information of conversations systematically and design summary generation methods to capture conversation trajectory dynamically. We achieve about 10% performance increase over a simple baseline, and 6.47% increase over a strong baseline on a dataset, and a slight performance increase on a benchmark dataset for the task of summarizing conversation dynamics.
%U https://aclanthology.org/2026.acl-long.243/
%P 5368-5384
Markdown (Informal)
[A Theoretically Grounded Approach to Summarizing Conversation Dynamics for Forecasting the Derailment of Online Conversations](https://aclanthology.org/2026.acl-long.243/) (Fu & Ollagnier, ACL 2026)
ACL