根据新闻难易度动态选模型和特征，提升文本假新闻检测准确率
DRES: Fake news detection by dynamic representation and ensemble selection
- 基于实例难度评估动态选择最优文本表示
- 通过动态集成选择使准确率显著优于现有方法
- 适合需要高精度假新闻识别的舆情监控场景
社交媒体信息的快速传播使得基于文本的假新闻检测变得至关重要。本文提出一种名为动态表示与集成选择（DRES）的新方法，仅依靠文本即可识别假新闻。DRES利用实例难度度量，评估每篇新闻在多种文本特征表示下的分类难度，并据此动态选择最合适的文本表示及最擅长该实例的分类器集成。大量实验表明，DRES在多个基准数据集上显著优于当前最佳方法，验证了基于实例难度的表示选择与动态集成选择对性能提升的有效性。代码与数据已公开于：https://github.com/FFarhangian/FakeNewsDetection_DRES
原文摘要 · Abstract (English)
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
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