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Deep Learning for Fake News Detection in a Pairwise Textual Input Schema
In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexibledoi:10.3390/computation9020020 fatcat:p7ciykp6kzbp3dw45snrgehaje