Misinformation such as fake news and rumors is a serious threat on information ecosystems
and public trust. The emergence of Large Language Models (LLMs) has great potential to …

Spurred by the recent rapid increase in the development and distribution of large language
models (LLMs) across industry and academia, much recent work has drawn attention to …

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 …

Large language models (LLMs) have strong capabilities in solving diverse natural language
processing tasks. However, the safety and security issues of LLM systems have become the …

We introduce a generic, language-independent method to collect a large percentage of
offensive and hate tweets regardless of their topics or genres. We harness the extralinguistic …

Social media moderation policies are often at the center of public debate, and their
implementation and enactment are sometimes surrounded by a veil of mystery …

The detection of toxic language in the Arabic language has emerged as an active area of
research in recent years, and reviewing the existing datasets employed for training the …

We present an overview of CheckThat! Lab's 2023 Task 1, which is part of CLEF-2023. Task
1 asks to determine whether a text item, or a text coupled with an image, is check-worthy …

We present an overview of the CheckThat! Lab 2024 Task 1, part of CLEF 2024. Task 1
involves determining whether a text item is check-worthy, with a special emphasis on COVID …

Counterfactually Augmented Data (CAD) aims to improve out-of-domain generalizability, an
indicator of model robustness. The improvement is credited with promoting core features of …