@inproceedings{chandra-etal-2020-abuseanalyzer,
title = "{A}buse{A}nalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts",
author = "Chandra, Mohit and
Pathak, Ashwin and
Dutta, Eesha and
Jain, Paryul and
Gupta, Manish and
Shrivastava, Manish and
Kumaraguru, Ponnurangam",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.552",
doi = "10.18653/v1/2020.coling-main.552",
pages = "6277--6283",
abstract = "While extensive popularity of online social media platforms has made information dissemination faster, it has also resulted in widespread online abuse of different types like hate speech, offensive language, sexist and racist opinions, etc. Detection and curtailment of such abusive content is critical for avoiding its psychological impact on victim communities, and thereby preventing hate crimes. Previous works have focused on classifying user posts into various forms of abusive behavior. But there has hardly been any focus on estimating the severity of abuse and the target. In this paper, we present a first of the kind dataset with 7,601 posts from Gab which looks at online abuse from the perspective of presence of abuse, severity and target of abusive behavior. We also propose a system to address these tasks, obtaining an accuracy of ∼80{\%} for abuse presence, ∼82{\%} for abuse target prediction, and ∼65{\%} for abuse severity prediction.",
}
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<abstract>While extensive popularity of online social media platforms has made information dissemination faster, it has also resulted in widespread online abuse of different types like hate speech, offensive language, sexist and racist opinions, etc. Detection and curtailment of such abusive content is critical for avoiding its psychological impact on victim communities, and thereby preventing hate crimes. Previous works have focused on classifying user posts into various forms of abusive behavior. But there has hardly been any focus on estimating the severity of abuse and the target. In this paper, we present a first of the kind dataset with 7,601 posts from Gab which looks at online abuse from the perspective of presence of abuse, severity and target of abusive behavior. We also propose a system to address these tasks, obtaining an accuracy of ∼80% for abuse presence, ∼82% for abuse target prediction, and ∼65% for abuse severity prediction.</abstract>
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%0 Conference Proceedings
%T AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts
%A Chandra, Mohit
%A Pathak, Ashwin
%A Dutta, Eesha
%A Jain, Paryul
%A Gupta, Manish
%A Shrivastava, Manish
%A Kumaraguru, Ponnurangam
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F chandra-etal-2020-abuseanalyzer
%X While extensive popularity of online social media platforms has made information dissemination faster, it has also resulted in widespread online abuse of different types like hate speech, offensive language, sexist and racist opinions, etc. Detection and curtailment of such abusive content is critical for avoiding its psychological impact on victim communities, and thereby preventing hate crimes. Previous works have focused on classifying user posts into various forms of abusive behavior. But there has hardly been any focus on estimating the severity of abuse and the target. In this paper, we present a first of the kind dataset with 7,601 posts from Gab which looks at online abuse from the perspective of presence of abuse, severity and target of abusive behavior. We also propose a system to address these tasks, obtaining an accuracy of ∼80% for abuse presence, ∼82% for abuse target prediction, and ∼65% for abuse severity prediction.
%R 10.18653/v1/2020.coling-main.552
%U https://aclanthology.org/2020.coling-main.552
%U https://doi.org/10.18653/v1/2020.coling-main.552
%P 6277-6283
Markdown (Informal)
[AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts](https://aclanthology.org/2020.coling-main.552) (Chandra et al., COLING 2020)
ACL
- Mohit Chandra, Ashwin Pathak, Eesha Dutta, Paryul Jain, Manish Gupta, Manish Shrivastava, and Ponnurangam Kumaraguru. 2020. AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6277–6283, Barcelona, Spain (Online). International Committee on Computational Linguistics.