Work

Annotated tweet data of mixed Wolof-French for detecting obnoxious messages

Year: 2025

Type: data-paper

Abstract: Automatic detection of obnoxious (abusive) messages on social networks is complex, especially for low-resource languages and in the case of mixed code, such as Wolof-French. This phenomenon is common in Senegalese tweets, but there is a lack of annotated data to facilitate this task. To fill this gap, we created AWOFRO, the first annotated corpus of 3510 tweets in mixed code. We analysed this corpus and validated the annotations using measures such as Cohen's Kappa.

Source: Data in Brief

DOI: 10.1016/j.dib.2025.111500

Authors: Ibrahima Ndao, Khadim Dramé, Gorgoumack Sambe, Gayo Diallo

Institutions: Ziguinchor University, Université numérique Cheikh Hamidou KANE, Université de Bordeaux, Inserm, Bordeaux Population Health

Language: English

Topic: Hate Speech and Cyberbullying Detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,220

OpenAlex ID: W4408747584

Cites: 21

Cited by: 5

Related to: 10