Fake news detection using deep learning models: A novel approach

With the ever increase in social media usage, it has become necessary to combat the spread of false information and decrease the reliance of information retrieval from such sources. Social platforms are under constant pressure to come up with efficient methods to solve this problem because users' interaction with fake and unreliable news leads to its spread at an individual level. This spreading of misinformation adversely affects the perception about an important activity, and as such, it needs to be dealt with using a modern approach. In this paper, we collect 1356 news instances from various users via Twitter and media sources such as PolitiFact and create several datasets for the real and the fake news stories. Our study compares multiple state‐of‐the‐art approaches such as convolutional neural networks (CNNs), long short‐term memories (LSTMs), ensemble methods, and attention mechanisms. We conclude that CNN + bidirectional LSTM ensembled network with attention mechanism achieved the highest accuracy of 88.78%, whereas Ko et al tackled the fake news identification problem and achieved a detection rate of 85%. With the ever increase in social media usage, it has become necessary to combat the spread of false information and decrease the reliance of information retrieval from such sources. Social platforms are under constant pressure to come up with efficient methods to solve this problem. We tackled the fake news identification problem and achieved a detection rate of 88.78%.