The rapid growth of online social media platforms has facilitated a significant increase in content creation and information exchange, which has been highly beneficial. However, these platforms have also become a breeding ground for those who spread malicious rumors, fake news, propaganda, and misinformation. Claims play a vital role in shaping our understanding of the world, but unfortunately, they are often used by purveyors of fake news to deceive people. To address this problem, social media giants employ content moderators to filter out fake news from the actual word, but the sheer volume of information makes it difficult to identify fake news effectively. Therefore, automatically identifying posts containing such claims on social media platforms, verifying their validity, and distinguishing between credible and false claims has become a crucial research problem in NLP. Through this shared task, our objective is to develop systems that can effectively detect and identify claims within social media text.

This involves detecting the presence of a claim within the given text. This can be a challenging task, as claims can take various forms and may be embedded within larger pieces of text. Therefore, the system must be able to recognize patterns and language cues that are characteristic of claims, such as assertive language, specific claims about a topic, and references to evidence or sources.

The shared task on the topic "Uncovering Truth in Social Media through Claim Detection" invites participation in the following sub-task:

The task is binary classification problem, where the objective is to label the given social media post as claim or non-claim. A claim is a assertive statement that may or not have evidences.

For more details, participants can refer to:

Overview of the CLAIMSCAN-2023: Uncovering Truth in Social Media through Claim Detection and Identification of Claim Spans: https://arxiv.org/abs/2310.19267.