Fake News Detection Using Machine Learning Ensemble Methods

TLDR

The rapid growth of the Web and social media has led to unprecedented information sharing, including widespread misinformation that is difficult to classify automatically. The study proposes a machine‑learning ensemble approach to automatically classify news articles as fake or real. The authors extract textual features, train multiple classifiers with ensemble methods, and evaluate the models on four real‑world datasets. The ensemble outperforms individual learners in classification accuracy.

Abstract

The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners.