"It's Not Just Hate'': A Multi-Dimensional Perspective on Detecting Harmful Speech Online

• URL: http://arxiv.org/abs/2210.15870v1
• Date: Fri, 28 Oct 2022 03:34:50 GMT
• Title: "It's Not Just Hate'': A Multi-Dimensional Perspective on Detecting Harmful Speech Online
• Authors: Federico Bianchi, Stefanie Anja Hills, Patricia Rossini, Dirk Hovy, Rebekah Tromble, Nava Tintarev
• Abstract summary: We show that a more fine-grained multi-label approach to predicting incivility and hateful or intolerant content addresses both conceptual and performance issues. We release a novel dataset of over 40,000 tweets about immigration from the US and UK, annotated with six labels for different aspects of incivility and intolerance.
• Score: 26.10949184015077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Well-annotated data is a prerequisite for good Natural Language Processing models. Too often, though, annotation decisions are governed by optimizing time or annotator agreement. We make a case for nuanced efforts in an interdisciplinary setting for annotating offensive online speech. Detecting offensive content is rapidly becoming one of the most important real-world NLP tasks. However, most datasets use a single binary label, e.g., for hate or incivility, even though each concept is multi-faceted. This modeling choice severely limits nuanced insights, but also performance. We show that a more fine-grained multi-label approach to predicting incivility and hateful or intolerant content addresses both conceptual and performance issues. We release a novel dataset of over 40,000 tweets about immigration from the US and UK, annotated with six labels for different aspects of incivility and intolerance. Our dataset not only allows for a more nuanced understanding of harmful speech online, models trained on it also outperform or match performance on benchmark datasets.

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