Detecting Stance in Urdu Content on Social Media and Websites for Fake News and Propaganda Identification

The extensive spread of fake information has rendered various news types questionable, leading to a significant decline in trust in news. Social media is the primary channel by which fake news is disseminatedwidely. Worldwide, several deep learning methods have been created to identify fake news, achieving significant success with content in the English language. However, to our knowledge, there is no deep learning method available for detecting fake news or stance detection in content written in Urdu. Therefore, it is crucial to create a method that can detect fake news within Urdu language content. This study seeks to identify a method for detecting fake news in the Urdu language by proposing a framework that employs advanced Bidirectional Encoder Representations from Transformers (BERT), Embeddings from Language Models (ELMO), and various deep learning models (CNN, LSTM, Bi-LSTM) to evaluate performance accuracy on Urdu datasets (Liar-ProSOUL and Bend the Truth-Benchmark). We utilized Embeddings from Language Models (ELMO) for feature extraction and a convolutional neural network (CNN) for the classification task. The findings from the suggested framework indicate that ELMO excels with extensive datasets.