Role of ELMo Embedding in Detecting Fake News on Social Media

The advancement of technology makes it difficult to distinguish between genuine and false information. Social media allow fake news to spread faster in comparison to conventional media. The propagation of such fake news creates a harmful effect on the public and society. It can also generate biased opinions and disturb the stability of the news ecosystem. To combat the rapid proliferation of fake news on online media, a holistic approach is required. In this paper, we used Elmo word embedding for counterfeit detection. For implementation, the Reuter dataset is used. In particular, This paper used different machine-learning models to evaluate their effectiveness.