On the world wide web, toxic content detectors are a crucial line of defense against
potentially hateful and offensive messages. As such, building highly effective classifiers that …

Recent advances in the capacity of large language models to generate human-like text have
resulted in their increased adoption in user-facing settings. In parallel, these improvements …

Background: The existence of toxic conversations in open-source platforms can degrade
relationships among software developers and may negatively impact software product …

Large Language Model (LLMs) such as ChatGPT that exhibit generative AI capabilities are
facing accelerated adoption and innovation. The increased presence of Generative AI (GAI) …

Toxic conversations during software development interactions may have serious
repercussions on a Free and Open Source Software (FOSS) development project. For …

Toxicity is an increasingly common and severe issue in online spaces. Consequently, a rich
line of machine learning research over the past decade has focused on computationally …

Automated filtering of toxic conversations may help an Open-source software (OSS)
community to maintain healthy interactions among the project participants. Although, several …

Abstract Machine learning and deep learning models are increasingly susceptible to
adversarial attacks, particularly in critical areas like cybersecurity and Information Disorder …

The amount of images with embedded text shared on Online Social Networks (OSNs), such
as Twitter or Facebook has been growing in recent years. It is becoming important to …

This paper presents a novel application of Natural Language Processing techniques to
classify unstructured text into toxic and non-toxic categories. In the current century, social …