Conversations Gone Awry: Detecting Early Signs of Conversational Failure

Justine Zhang, Jonathan Chang, Cristian Danescu-Niculescu-Mizil, Lucas Dixon, Yiqing Hua, Dario Taraborelli, Nithum Thain


Abstract
One of the main challenges online social systems face is the prevalence of antisocial behavior, such as harassment and personal attacks. In this work, we introduce the task of predicting from the very start of a conversation whether it will get out of hand. As opposed to detecting undesirable behavior after the fact, this task aims to enable early, actionable prediction at a time when the conversation might still be salvaged. To this end, we develop a framework for capturing pragmatic devices—such as politeness strategies and rhetorical prompts—used to start a conversation, and analyze their relation to its future trajectory. Applying this framework in a controlled setting, we demonstrate the feasibility of detecting early warning signs of antisocial behavior in online discussions.
Anthology ID:
P18-1125
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1350–1361
Language:
URL:
https://aclanthology.org/P18-1125
DOI:
10.18653/v1/P18-1125
Bibkey:
Cite (ACL):
Justine Zhang, Jonathan Chang, Cristian Danescu-Niculescu-Mizil, Lucas Dixon, Yiqing Hua, Dario Taraborelli, and Nithum Thain. 2018. Conversations Gone Awry: Detecting Early Signs of Conversational Failure. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1350–1361, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Conversations Gone Awry: Detecting Early Signs of Conversational Failure (Zhang et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1125.pdf
Notes:
 P18-1125.Notes.pdf
Video:
 https://vimeo.com/285803564