Continuous Monitoring of Body Shaming Actions in Social Networks

Social media platforms provide significant communication opportunities, yet they are sometimes utilized inappropriately. Among the prevalent misuses is cyberbullying, particularly body shaming. Twitter (now known as X) facilitates daily interactions for millions of users, allowing them to post and read succinct messages on the Internet. This study explores the development of a text mining pipeline, employing machine learning models, designed to identify instances of body shaming within the Italian Twitter community. Additionally, it addresses the challenge of concept drift, a phenomenon where the characteristics of the dataset evolve over time, potentially leading to a decline in classification accuracy. Through an online monitoring phase, the presence of concept drift is confirmed, and an effective solution is sought by evaluating several strategies to mitigate its impact.