Fake news detection on social media documents

Abstract

The objective of this Ph.D. research is to suggest automated intelligent approaches for detecting fake news sources,especially social bots.Social bots are autonomous entities that generate significant social media content.In our thesis,we present two main contributions: the first one presents “Sentiment Analysis-based Model for Bot Detectionon Social Media” (Deep Bi-LSTM) that incorporates different sentiment and semantic features to perform the bots detection.Experiment on the cresci-2017 dataset shows that our approach can achieve competitive performance with 97.36% of accuracy. The second contribution captures the linguistic-based features by developing a novel framework that we have called “Hybrid Mixing Engineered Linguistic framework Features Based on Autoencoder”.This framework is split into two segments:the features learner and a deep neural networks classifier.The feature learner aims at performing the feature extraction task due to a deep autoencoder based on dense layers and a BiLSTMautoencoder. We enhance the feature extractor:(i)by feeding the lexical and syntactic features to the first autoencoder to represent the high-order features in latent space;(ii)by building the semantic and the context features using the BiLSTMautoencoder;(iii)the merging of the two previous trained encoder blocks would generate a compacted data based on elite features.This architecture help us to discover human writing style patterns accurately.Experiments conducted on real datasets show that a significant improvement can be achieved for fine-grained bots detection with 92.22% of accuracy.