Exploring the Emotional Dimension of French Online Toxic Content
Explorer la dimension émotionnelle des contenus toxiques en Français
Résumé
One of the biggest hurdles for the effective analysis of data collected on social platforms is the need for deeper insights on the content of this data. Emotion annotation can bring new perspectives on this issue and can enable the identification of content-specific features. This study aims at investigating the ways in which variation in online toxic content can be explored through emotions detection . The paper describes the emotion annotation of three different corpora in French which all belong to toxic content (extremist content, sexist content and hateful content respectively). To this end, first a fine-grained annotation parser of emotions was used to automatically annotate the data sets. Then, several empirical studies were carried out to characterize the content in the light of obtained emotional categories. Results suggest that emotion annotations can provide new insights for online content analysis and stronger empirical background for automatic toxic content detection.
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