DRES: Fake news detection by dynamic representation and ensemble selection
This addresses the societal problem of fake news spread on social media, but it is incremental as it builds on existing detection techniques.
The paper tackles fake news detection from text by proposing DRES, a method that dynamically selects textual representations and classifier ensembles based on instance hardness, achieving significant accuracy improvements over state-of-the-art methods.
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