It's All in the Embedding! Fake News Detection Using Document Embeddings

Abstract

With the current shift in the mass media landscape from journalistic rigor to social media, personalized social media is becoming the new norm. Although the digitalization progress of the media brings many advantages, it also increases the risk of spreading disinformation, misinformation, and malformation through the use of fake news. The emergence of this harmful phenomenon has managed to polarize society and manipulate public opinion on particular topics, e.g., elections, vaccinations, etc. Such information propagated on social media can distort public perceptions and generate social unrest while lacking the rigor of traditional journalism. Natural Language Processing and Machine Learning techniques are essential for developing efficient tools that can detect fake news. Models that use the context of textual data are essential for resolving the fake news detection problem, as they manage to encode linguistic features within the vector representation of words. In this paper, we propose a new approach that uses document embeddings to build multiple models that accurately label news articles as reliable or fake. We also present a benchmark on different architectures that detect fake news using binary or multi-labeled classification. We evaluated the models on five large news corpora using accuracy, precision, and recall. We obtained better results than more complex state-of-the-art Deep Neural Network models. We observe that the most important factor for obtaining high accuracy is the document encoding, not the classification model's complexity.

References in corpus (11)

• Scikit-learn: Machine Learning in Python
• XGBoost: A Scalable Tree Boosting System
• Efficient Estimation of Word Representations in Vector Space
• Distributed Representations of Words and Phrases and their Compositionality
• Distributed Representations of Sentences and Documents
• Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
• Influence of fake news in Twitter during the 2016 US presidential election
• MisRoBÆRTa: Transformers versus Misinformation
• Fake News Detection: a comparison between available Deep Learning techniques in vector space
• TextBenDS: a generic Textual data Benchmark for Distributed Systems
• Meta-Path-based Fake News Detection Leveraging Multi-level Social Context Information

Cited by in corpus (3)

• MCWDST: a Minimum-Cost Weighted Directed Spanning Tree Algorithm for Real-Time Fake News Mitigation in Social Media
• CONTAIN: A Community-based Algorithm for Network Immunization
• DocNet: Semantic Structure in Inductive Bias Detection Models