The exponential growth in fake news and its role in deteriorating general public trust and democratic standards certainly calls for some counter combat approaches. The prediction of chances of news to be fake is deemed to be hard task since most of the deceptive news has its roots in true news. With a minor fabrication in legitimate news, influential fake news can be created that can be used for political, entertainment, or business-related gains. This work provides a novel intuitive approach to exploit data from multiple sources to segregate news into real and fake. To efficiently capture the contextual information present in the data, Bidirectional Encoder Representations from Transformer (BERT) have been deployed. It attempts to further enhance the performance of the deceptive news detection model by incorporating information about the speaker profile and the credibility associated with him/her. A hybrid sequence encoding model has been proposed to harvest the speaker profile and speaker credibility data which makes it useful for prediction. On evaluation over benchmark fake news dataset LIAR, our model outperformed the previous state-of-the-art works. This attests to the fact that the speaker’s profile and credibility play a crucial role in predicting the validity of news.