TI-CNN: Convolutional Neural Networks for Fake News Detection

Abstract

With the development of social networks, fake news for various commercial and political purposes has been appearing in large numbers and gotten widespread in the online world. With deceptive words, people can get infected by the fake news very easily and will share them without any fact-checking. For instance, during the 2016 US president election, various kinds of fake news about the candidates widely spread through both official news media and the online social networks. These fake news is usually released to either smear the opponents or support the candidate on their side. The erroneous information in the fake news is usually written to motivate the voters' irrational emotion and enthusiasm. Such kinds of fake news sometimes can bring about devastating effects, and an important goal in improving the credibility of online social networks is to identify the fake news timely. In this paper, we propose to study the fake news detection problem. Automatic fake news identification is extremely hard, since pure model based fact-checking for news is still an open problem, and few existing models can be applied to solve the problem. With a thorough investigation of a fake news data, lots of useful explicit features are identified from both the text words and images used in the fake news. Besides the explicit features, there also exist some hidden patterns in the words and images used in fake news, which can be captured with a set of latent features extracted via the multiple convolutional layers in our model. A model named as TI-CNN (Text and Image information based Convolutinal Neural Network) is proposed in this paper. By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously. Extensive experiments carried on the real-world fake news datasets have demonstrate the effectiveness of TI-CNN.

References in corpus (3)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Natural Language Processing (almost) from Scratch
• Convolutional Neural Networks for Sentence Classification

Cited by in corpus (22)

• SAFE: Similarity-Aware Multi-Modal Fake News Detection
• Graph Neural Networks with Continual Learning for Fake News Detection from Social Media
• An ensemble deep learning technique for detecting suicidal ideation from posts in social media platforms
• SEMI-FND: Stacked Ensemble Based Multimodal Inference For Faster Fake News Detection
• Research Status of Deep Learning Methods for Rumor Detection
• The Future of Misinformation Detection: New Perspectives and Trends
• Network-based Fake News Detection: A Pattern-driven Approach
• A Survey on Multimodal Disinformation Detection
• Modelling election dynamics and the impact of disinformation
• Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models
• GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media
• FR-Detect: A Multi-Modal Framework for Early Fake News Detection on Social Media Using Publishers Features
• RELIANCE: Reliable Ensemble Learning for Information and News Credibility Evaluation
• Science Checker: Extractive-Boolean Question Answering For Scientific Fact Checking
• Knowledge Enhanced Multi-modal Fake News Detection
• Combating Disinformation in a Social Media Age
• Learning to Detect Few-Shot-Few-Clue Misinformation
• Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection
• Rumour Detection via News Propagation Dynamics and User Representation Learning
• ReINTEL Challenge 2020: A Multimodal Ensemble Model for Detecting Unreliable Information on Vietnamese SNS
• A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks
• Claim Verification using a Multi-GAN based Model