Text-Convolutional Neural Networks for Fake News Detection in Tweets

Abstract

With the widespread use of online social networking websites, user-generated stories and social network platform have become critical in news propagation. The Web portals are being used to mislead users for political gains. Unreliable information is being shared without any fact-checking. Therefore, there is a dire need for automatic news verification system which can help journalists and the common users from misleading content. In this work, the task is defined as being able to classify a tweet as real or fake. The complexity of natural language constructs along with variegated languages makes this task very challenging. In this work, a deep learning model to learn semantic word embeddings is proposed to handle this complexity. The evaluations on the benchmark dataset (VMU 2015) show that deep learning methods are superior to traditional natural language processing algorithms.