Detecting online toxicity has always been a challenge due to its inherent subjectivity. Factors
such as the context, geography, socio-political climate, and background of the producers …

Recent years have witnessed the proliferation of offensive content online such as fake news,
propaganda, misinformation, and disinformation. While initially this was mostly about textual …

Understanding attitudes expressed in texts, also known as stance detection, plays an
important role in systems for detecting false information online, be it misinformation …

Internet memes have become powerful means to transmit political, psychological, and socio-
cultural ideas. Although memes are typically humorous, recent days have witnessed an …

The proliferation of harmful content on online platforms is a major societal problem, which
comes in many different forms, including hate speech, offensive language, bullying and …

The automatic identification of harmful content online is of major concern for social media
platforms, policymakers, and society. Researchers have studied textual, visual, and audio …

Detecting cyberbullying from memes is highly challenging, because of the presence of the
implicit affective content which is also often sarcastic, and multi-modality (image+ text). The …

Multimodal learning is an emerging yet challenging research area. In this paper, we deal
with multimodal sarcasm and humor detection from conversational videos and image-text …

Hateful meme detection is a challenging multimodal task that requires comprehension of
both vision and language, as well as cross-modal interactions. Recent studies have tried to …

The age of social media is rife with memes. Understanding and detecting harmful memes
pose a significant challenge due to their implicit meaning that is not explicitly conveyed …