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A Semi-Supervised Approach to Detect Toxic Comments

2021-09-01RANLP 2021Code Available0· sign in to hype

Ghivvago Damas Saraiva, Rafael Anchiêta, Francisco Assis Ricarte Neto, Raimundo Moura

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Abstract

Toxic comments contain forms of non-acceptable language targeted towards groups or individuals. These types of comments become a serious concern for government organizations, online communities, and social media platforms. Although there are some approaches to handle non-acceptable language, most of them focus on supervised learning and the English language. In this paper, we deal with toxic comment detection as a semi-supervised strategy over a heterogeneous graph. We evaluate the approach on a toxic dataset of the Portuguese language, outperforming several graph-based methods and achieving competitive results compared to transformer architectures.

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