Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company’s Reputation

Nikolay Babakov, Varvara Logacheva, Olga Kozlova, Nikita Semenov, Alexander Panchenko


Abstract
Not all topics are equally “flammable” in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.
Anthology ID:
2021.bsnlp-1.4
Volume:
Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing
Month:
April
Year:
2021
Address:
Kiyv, Ukraine
Venue:
BSNLP
SIG:
SIGSLAV
Publisher:
Association for Computational Linguistics
Note:
Pages:
26–36
Language:
URL:
https://aclanthology.org/2021.bsnlp-1.4
DOI:
Bibkey:
Cite (ACL):
Nikolay Babakov, Varvara Logacheva, Olga Kozlova, Nikita Semenov, and Alexander Panchenko. 2021. Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company’s Reputation. In Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing, pages 26–36, Kiyv, Ukraine. Association for Computational Linguistics.
Cite (Informal):
Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company’s Reputation (Babakov et al., BSNLP 2021)
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PDF:
https://aclanthology.org/2021.bsnlp-1.4.pdf