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 …

The widespread of offensive content online, such as hate speech and cyber-bullying, is a
global phenomenon. This has sparked interest in the artificial intelligence (AI) and natural …

Social media has effectively become the prime hub of communication and digital marketing.
As these platforms enable the free manifestation of thoughts and facts in text, images and …

Social media content moderation is the standard practice as on today to promote healthy
discussion forums. Toxic span prediction is helpful for explaining the toxic comment …

Recent NLP literature pays little attention to the robustness of toxicity language predictors,
while these systems are most likely to be used in adversarial contexts. This paper presents a …

Detecting toxic comments and rationale for the offensiveness of a social media post
promotes moderation of social media content. For this purpose, we propose a Co-Attentive …

As social media has become a predominant mode of communication globally, the rise of
abusive content threatens to undermine civil discourse. Recognizing the critical nature of …

Corpora that are the fundament for toxicity detection contain such expressions typically
directed against a target individual or group, eg, people of a specific gender or ethnicity …

Content moderation (removing or limiting the distribution of posts based on their contents) is
one tool social networks use to fight problems such as harassment and disinformation …

Work on bias in pretrained language models (PLMs) focuses on bias evaluation and
mitigation and fails to tackle the question of bias attribution and explainability. We propose a …