Degree based Classification of Harmful Speech using Twitter Data

By Sanjana Sharma, Saksham Agrawal, Manish Shrivastava. 2018

58 citations recorded by the source corpus.

Harmful speech has various forms and it has been plaguing the social media in different ways. If we need to crackdown different degrees of hate speech and abusive behavior amongst it, the classification needs to be based on complex ramifications which needs to be defined and hold accountable for, other than racist, sexist or against some particular group and community. This paper primarily describes how we created an ontological classification of harmful speech based on degree of hateful intent and used it to annotate twitter data accordingly. The key contribution of this paper is the new dataset of tweets we created based on ontological classes and degrees of harmful speech found in the text. We also propose supervised classification system for recognizing these respective harmful speech classes in the texts hence. This serves as a preliminary work to lay down foundation on defining different classes of harmful speech and subsequent work will be done in making it’s automatic detection more robust and efficient.

Subfields: Artificial Intelligence, Information Systems, Social Psychology

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Citations in the corpus

Challenges for Toxic Comment Classification: An In-Depth Error Analysis (2018, background): “…classiﬁcation tasks, related work considers different aspects of toxic language, such as “racism” (Greevy and Smeaton, 2004; Waseem, 2016; Kwok and Wang, 2013) and “sexism” (Waseem and Hovy, 2016; Jha and Mamidi, 2017), or the severity of toxicity (David-son et al., 2017; Sharma et al., 2018).”
Hate Speech Dataset from a White Supremacy Forum (2018, background): “Moreover, Sharma et al. (2018) claim that context is essential to understand the speaker’s intention.”
Directions in Abusive Language Training Data: Garbage In, Garbage Out (2020, background): “f abuse such as bullying, maliciousness and hostility [50, 87]. Most of the guidelines for the datasets we have studied do not contain an explicit discussion of intent, although there are exceptions. [97] include intent as a core part of their annotation standard, noting that understanding context (”