Angry or Sad ? Emotion Annotation for Extremist Content Characterisation
Valentina Dragos, Delphine Battistelli, Aline Etienne, Yolène Constable
Abstract
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 set-up 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.- Anthology ID:
- 2022.lrec-1.21
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 193–201
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.21
- DOI:
- Cite (ACL):
- Valentina Dragos, Delphine Battistelli, Aline Etienne, and Yolène Constable. 2022. Angry or Sad ? Emotion Annotation for Extremist Content Characterisation. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 193–201, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Angry or Sad ? Emotion Annotation for Extremist Content Characterisation (Dragos et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.lrec-1.21.pdf
- Data
- EmoBank