ANN: Adversarial News Net

With the ease of access to social media platforms, the spread of fake news has become a growing concern in today’s society. Classifying fake news is an important task, as it can help prevent its negative impact on individuals and society. In this work, an end-to-end framework for fake news detection is developed by utilizing the power of adversarial training to make the model more robust. This framework is named “ANN: Adversarial News Net”. The performance of ANN is evaluated using four publicly available datasets, and it is found to outperform previous studies after adversarial training. Furthermore, emoticons have been extracted from the dataset to understand their meanings in relation to fake news. This information is then fed into the model, which helped to improve its performance in classifying fake news. The proposed framework has the potential to be used as a tool for detecting fake news in real-time, thereby mitigating its harmful effects on society.