Fake News Detection Using Deep Learning

FAKE news has proliferated to a big crowd than before in this digital era, the main factor derives from the rise of social media and direct messaging platform. Techniques of fake news stories detection ingenious, varied, and exciting. This study aims to apply natural language processing (NLP) techniques for text analytics and train deep learning models for detecting fake news based on news title or news content. Solution proposed in this study aims to be applied in real-world social media and eliminate the bad experience for user to receive misleading stories that come from non-reputable source. For NLP techniques, text preprocessing such as regular expression, tokenization, lemmatization and stop words removal are used before vectorizing them into N-gram vectors or sequence vectors using terms frequency inverse document frequency (TF-IDF) or one-hot encoding respectively. Then, TensorFlow is chosen as the framework to be used with built in Keras deep learning libraries that is having a large community and number of commits on Tensorflow GitHub repository that can be enough to build deep learning neural network models. Results from the models are showing that models trained with news content can achieve better performance with computation time being sacrificed while models trained with news title require less computation time to achieve good performance. Also, overall performance of models fed with N-gram vectors are slightly better than models fed with sequence vectors.