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        <title>A Novel Methodology for Developing Automatic Harassment Classifiers for Twitter</title>
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            <title>Proceedings of the Fourth Workshop on Online Abuse and Harms</title>
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    <abstract>Most efforts at identifying abusive speech online rely on public corpora that have been scraped from websites using keyword-based queries or released by site or platform owners for research purposes. These are typically labeled by crowd-sourced annotators – not the targets of the abuse themselves. While this method of data collection supports fast development of machine learning classifiers, the models built on them often fail in the context of real-world harassment and abuse, which contain nuances less easily identified by non-targets. Here, we present a mixed-methods approach to create classifiers for abuse and harassment which leverages direct engagement with the target group in order to achieve high quality and ecological validity of data sets and labels, and to generate deeper insights into the key tactics of bad actors. We use women journalists’ experience on Twitter as an initial community of focus. We identify several structural mechanisms of abuse that we believe will generalize to other target communities.</abstract>
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%0 Conference Proceedings
%T A Novel Methodology for Developing Automatic Harassment Classifiers for Twitter
%A Arora, Ishaan
%A Guo, Julia
%A Levitan, Sarah Ita
%A McGregor, Susan
%A Hirschberg, Julia
%Y Akiwowo, Seyi
%Y Vidgen, Bertie
%Y Prabhakaran, Vinodkumar
%Y Waseem, Zeerak
%S Proceedings of the Fourth Workshop on Online Abuse and Harms
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F arora-etal-2020-novel
%X Most efforts at identifying abusive speech online rely on public corpora that have been scraped from websites using keyword-based queries or released by site or platform owners for research purposes. These are typically labeled by crowd-sourced annotators – not the targets of the abuse themselves. While this method of data collection supports fast development of machine learning classifiers, the models built on them often fail in the context of real-world harassment and abuse, which contain nuances less easily identified by non-targets. Here, we present a mixed-methods approach to create classifiers for abuse and harassment which leverages direct engagement with the target group in order to achieve high quality and ecological validity of data sets and labels, and to generate deeper insights into the key tactics of bad actors. We use women journalists’ experience on Twitter as an initial community of focus. We identify several structural mechanisms of abuse that we believe will generalize to other target communities.
%R 10.18653/v1/2020.alw-1.2
%U https://aclanthology.org/2020.alw-1.2
%U https://doi.org/10.18653/v1/2020.alw-1.2
%P 7-15

Markdown (Informal)

[A Novel Methodology for Developing Automatic Harassment Classifiers for Twitter](https://aclanthology.org/2020.alw-1.2) (Arora et al., ALW 2020)

• A Novel Methodology for Developing Automatic Harassment Classifiers for Twitter (Arora et al., ALW 2020)

ACL

• Ishaan Arora, Julia Guo, Sarah Ita Levitan, Susan McGregor, and Julia Hirschberg. 2020. A Novel Methodology for Developing Automatic Harassment Classifiers for Twitter. In Proceedings of the Fourth Workshop on Online Abuse and Harms, pages 7–15, Online. Association for Computational Linguistics.