Details

Autor(en) / Beteiligte

• Malmasi, Shervin
• Zampieri, Marcos

Titel

Challenges in discriminating profanity from hate speech

Ist Teil von

• Journal of experimental & theoretical artificial intelligence, 2018-03, Vol.30 (2), p.187-202

Ort / Verlag

Abingdon: Taylor & Francis

Erscheinungsjahr

2018

Link zum Volltext

Quelle

Taylor & Francis Journals Auto-Holdings Collection

Beschreibungen/Notizen

• In this study, we approach the problem of distinguishing general profanity from hate speech in social media, something which has not been widely considered. Using a new dataset annotated specifically for this task, we employ supervised classification along with a set of features that includes -grams, skip-grams and clustering-based word representations. We apply approaches based on single classifiers as well as more advanced ensemble classifiers and stacked generalisation, achieving the best result of accuracy for this 3-class classification task. Analysis of the results reveals that discriminating hate speech and profanity is not a simple task, which may require features that capture a deeper understanding of the text not always possible with surface -grams. The variability of gold labels in the annotated data, due to differences in the subjective adjudications of the annotators, is also an issue. Other directions for future work are discussed.