Claim Detection in Persian Twitter Posts

The proliferation of false information on social media has profound negative impacts across various aspects of people's lives. To mitigate these effects, numerous studies have focused on developing automated factchecking systems aimed at enhancing the accuracy and reliability of news and information. Claim detection, recognized as the initial stage in constructing such systems, has been explored in several languages. In our paper, we introduce a corpus of Persian tweets annotated with ۱۱ labels derived from linguistic analysis, representing different types of claims. Additionally, we establish a baseline claim detection model to assess the dataset. This study frames claim detection as a classification task and employs a transformer-based approach to train a multi-label classifier capable of identifying various types of claims in Persian texts.

کلیدواژه ها:

Automatic Claim Detection ، Persian Language Processing ، Low-resource language ، Multi Label Classification