Angry or Sad ? Emotion Annotation for Extremist Content Characterization

V. Dragos (1) , Delphine Battistelli (2) , A Etienne (2) , Y. Constable (1)

1 DTIS, ONERA, Université Paris Saclay [Palaiseau]
2 MoDyCo - Modèles, Dynamiques, Corpus

This paper examines the role of emotion annotations to characterize extremist content released on social platforms. The analysis of extremist content is important to identify user emotions towards some extremist ideas and to highlight the root cause of where emotions and extremist attitudes merge together. To address these issues our methodology combines knowledge from sociological and linguistic annotations to explore French extremist content collected online. For emotion linguistic analysis, the solution presented in this paper relies on a complex linguistic annotation scheme. The scheme was used to annotate extremist text corpora in French. Data sets were collected online by following semi-automatic procedures for content selection and validation. The paper describes the integrated annotation scheme, the annotation protocol that was setup for French corpora annotation and the results, e.g. agreement measures and remarks on annotation disagreements. The aim of this work is twofold: first, to provide a characterization of extremist contents; second, to validate the annotation scheme and to test its capacity to capture and describe various aspects of emotions.

Mots clés: Emotion annotation; social data analysis; extremist content

Domaines: Linguistique; Traitement du texte et du document

V. Dragos, Delphine Battistelli, A Etienne, Y. Constable. Angry or Sad ? Emotion Annotation for Extremist Content Characterization. 13th Language Resources and Evaluation Conference, Jun 2022, Marseille, France. pp.193-201. ⟨hal-03712950⟩