DRES: Fake news detection by dynamic representation and ensemble selection

Abstract

The rapid spread of information via social media has made text-based fake news detection critically important due to its societal impact. This paper presents a novel detection method called Dynamic Representation and Ensemble Selection (DRES) for identifying fake news based solely on text. DRES leverages instance hardness measures to estimate the classification difficulty for each news article across multiple textual feature representations. By dynamically selecting the textual representation and the most competent ensemble of classifiers for each instance, DRES significantly enhances prediction accuracy. Extensive experiments show that DRES achieves notable improvements over state-of-the-art methods, confirming the effectiveness of representation selection based on instance hardness and dynamic ensemble selection in boosting performance. Codes and data are available at: https://github.com/FFarhangian/FakeNewsDetection_DRES.

Authors

Faramarz Farhangian, LIVIA laboratory, École de technologie supérieure, Montreal, Canada

Leandro A. Ensina, Universidade Tecnológica Federal do Paraná, Curitiba, Brazil

George D. C. Cavalcanti, Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil

Rafael M. O. Cruz, Ecole de Technologie Supérieure, Montreal, Canada