VocalTweets: Investigating Social Media Offensive Language Among Nigerian Musicians

TL;DR

This paper introduces VocalTweets, a multilingual dataset of Nigerian musicians' tweets labeled for offensive language, and evaluates a model achieving 74.5 F1 score, highlighting challenges in cross-corpus generalization.

Contribution

It presents VocalTweets, a novel dataset for offensive language detection among Nigerian musicians, and assesses model performance and generalizability across datasets.

Findings

Achieved 74.5 F1 score with Twitter-RoBERTa model.

Demonstrated challenges in cross-corpus generalization.

Provided insights into offensive language use by Nigerian musicians.

Abstract

Musicians frequently use social media to express their opinions, but they often convey different messages in their music compared to their posts online. Some utilize these platforms to abuse their colleagues, while others use it to show support for political candidates or engage in activism, as seen during the #EndSars protest. There are extensive research done on offensive language detection on social media, the usage of offensive language by musicians has received limited attention. In this study, we introduce VocalTweets, a code-switched and multilingual dataset comprising tweets from 12 prominent Nigerian musicians, labeled with a binary classification method as Normal or Offensive. We trained a model using HuggingFace's base-Twitter-RoBERTa, achieving an F1 score of 74.5. Additionally, we conducted cross-corpus experiments with the OLID dataset to evaluate the generalizability of our dataset.