在現今資訊爆炸的時代，透過社交平台傳遞資訊與知識，往往會有民眾誤信錯誤訊息或惡意製造的假訊息，並且將其資訊互相傳遞，造成社會不同程度的影響，而這個傳遞過程被稱為「資訊流行病（Infodemic）」，「資訊流行病」一詞，最早出現於2003年SARS疫情期間，當時大量傳播著不實資訊、謠言，不僅讓公衛危機難以控制，同時透過各種傳播管道，快速大量於全球流竄，造成國安、經濟、政治等受到影響，因此問題必須從源頭進行遏止。
本研究使用主題模型中的隱含狄利克雷分配（Latent Dirichlet Allocation, LDA）方法，結合TF與TF-IDF進行實驗比較，而LDA結合TF-IDF特徵於SVM、隨機森林兩種模型皆能提升F1-score分數，其中SVM模型提升效果最顯著，經十折交叉驗證結果，平均F1-score成長幅度提升1.13%，Accuracy為98.04%，Accuracy的標準差為0.95%，F1-score達到98.10%。而使用基於BERT預訓練模型之T-BERT，Accuracy是所有模型當中最高，達到98.36%。

In the present information-saturated era, the transmission of information and knowledge through social media platforms often results in the public believing in incorrect or maliciously fabricated fake news, which is then disseminated, leading to various degrees of impact on society. This process is referred to as an "Infodemic." The term "Infodemic" first appeared during the 2003 SARS epidemic, when a large amount of false information and rumors were disseminated, not only making it difficult to control public health crises, but also rapidly spreading globally through various channels, resulting in impacts on national security, economy, politics, etc. Therefore, addressing this issue at its source is necessary.
In this study, we conducted an experimental comparison using Latent Dirichlet Allocation (LDA) in combination with the TF and TF-IDF in the topic model. Our results showed that LDA combined with TF-IDF features in both SVM and Random Forest models improved the F1-score, with the SVM model showing the most significant improvement. Through ten-fold cross-validation, the average F1-score growth rate increased by 1.13%, with an Accuracy of 98.04% and a standard deviation of 0.95% for Accuracy, and an F1-score of 98.10%. Additionally, we found that using the T-BERT, which is based on the BERT pre-trained model, resulted in the highest Accuracy among all models, at 98.36%.

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