Identifying Offensive Expressions of Opinion in Context

Vargas, Francielle Alves; Carvalho, Isabelle; Góes, Fabiana Rodrigues de

doi:10.48550/arxiv.2104.12227

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“…This base lexicon was then expanded through automatic classification employing information from both corpora and lexical resources. Vargas et al (2021) propose a contextual and cross-lingual offensive lexicon (MOL-Multilingual Offensive Lexicon) composed of 1,000 explicit and implicit offensive and swearing expressions. Offensive terms are defined as term or expression that intends to undermine or disparage any of the following social aspects: moral, appearance, physical, psychological health, sexual behaviour and orientation, intellectual, economic, religious and political aspects.…”

Section: Lexical Resources For Offensive and Hate Speech Detectionmentioning

confidence: 99%

An Italian lexical resource for incivility detection in online discourses

et al. 2022

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The exponential growth of social media has brought an increasing propagation of online hostile communication and vitriolic discourses, and social media have become a fertile ground for heated discussions that frequently result in the use of insulting and offensive language. Lexical resources containing specific negative words have been widely employed to detect uncivil communication. This paper describes the development and implementation of an innovative resource, namely the Revised HurtLex Lexicon, in which every headword is annotated with an offensiveness level score. The starting point is HurtLex, a multilingual lexicon of hate words. Concentrating on the Italian entries, we revised the terms in HurtLex and derived an offensive score for each lexical item by applying an Item Response Theory model to the ratings provided by a large number of annotators. This resource can be used as part of a lexicon-based approach to track offensive and hateful content. Our work comprises an evaluation of the Revised HurtLex lexicon.

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Section: Lexical Resources For Offensive and Hate Speech Detectionmentioning

confidence: 99%