VocalTweets: Investigating Social Media Offensive Language Among Nigerian Musicians
This work addresses a gap in research on offensive language usage by musicians, though it is incremental as it applies existing methods to a new dataset.
The study tackled the problem of offensive language detection in social media posts by Nigerian musicians by introducing the VocalTweets dataset, achieving an F1 score of 74.5 with a RoBERTa-based model.
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.