An Efficient LSTM Model for Fake News Detection

Abstract

Information spread through online social media or sites has increased drastically with the swift growth of the Internet. Unverified or fake news reaches numerous users without concern about the trustworthiness of the info. Such fake news is created for political or commercial interests to mislead the users. In current society, the spread of misinformation is a big challenge. Hence, we propose a deep learning-based Long Short Term Memory (LSTM) classifier for fake news classification. Textual content is the primary unit in the fake news scenario. Therefore, natural language processing-based feature extraction is used to generate language-driven features. Experimental results show that NLP-based featured extraction with LSTM model achieves a higher accuracy rate in discernible less time.