用SVM和文本向量化检测假新闻，效果接近BERT但更省算力。
Strengthening False Information Propagation Detection: Leveraging SVM and Sophisticated Text Vectorization Techniques in comparison to BERT
- 对比TF-IDF、Word2Vec、BoW三种向量化方法，选BoW搭配SVM表现最佳。
- SVM+BoW达99.81%准确率，接近BERT的99.98%。
- 适合关注轻量级、低资源假新闻检测的开发者与研究者。
虚假信息在在线平台上的快速传播凸显了可靠检测系统的重要性。本研究探讨了支持向量机（SVM）与预训练语言模型BERT在假新闻检测中的应用。采用三种文本向量化方法：词频逆文档频率（TF-IDF）、Word2Vec和词袋模型（BoW），评估其区分真实与虚假新闻的效果。同时将这些方法与基于Transformer的大规模语言模型BERT进行对比。研究包含详尽的预处理、严谨的模型实现及全面的评估。结果显示，尽管BERT以99.98%的准确率和0.9998的F1分数领先，但使用线性核的SVM配合BoW向量化也达到99.81%准确率和0.9980的F1分数。这表明，即便在性能上稍逊，基于BoW和TF-IDF的SVM仍具极强竞争力，且计算成本更低。
原文摘要 · Abstract (English)
The rapid spread of misinformation, particularly through online platforms, underscores the urgent need for reliable detection systems. This study explores the utilization of machine learning and natural language processing, specifically Support Vector Machines (SVM) and BERT, to detect fake news. We employ three distinct text vectorization methods for SVM: Term Frequency Inverse Document Frequency (TF-IDF), Word2Vec, and Bag of Words (BoW), evaluating their effectiveness in distinguishing between genuine and fake news. Additionally, we compare these methods against the transformer large language model, BERT. Our comprehensive approach includes detailed preprocessing steps, rigorous model implementation, and thorough evaluation to determine the most effective techniques. The results demonstrate that while BERT achieves superior accuracy with 99.98% and an F1-score of 0.9998, the SVM model with a linear kernel and BoW vectorization also performs exceptionally well, achieving 99.81% accuracy and an F1-score of 0.9980. These findings highlight that, despite BERT's superior performance, SVM models with BoW and TF-IDF vectorization methods come remarkably close, offering highly competitive performance with the advantage of lower computational requirements.
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