新興領域的資料具有其資料相關特性，諸如資料特徵破碎、資料大幅缺失、資料極度不平衡等情況，像是COVID-19與氣候變遷這2個研究領域，這兩個領域近年來備受關注，而除了對於像是確診人數、氣候溫度變化等量化的資料進行分析，在自然語言處理方面，以COVID-19來說，大流行期間與 COVID-19 相關的多語言版本的假新聞的迅速傳播給公共衛生當局帶來了重大挑戰。而在氣候變遷領域，TCFD最近被引入作為投資者投資企業時的準則，使得企業須逐步揭露自身對於氣候變遷的貢獻，但是由於是自主揭露，企業普遍只揭露對自身有利的部分。從上述可知，這兩個新興領域的研究資料集存在了截然不同的資料特性。
為了因應不同的資料集特性，本研究設計了3種實驗來應對這些挑戰：第一個實驗為了驗證多語言性對於文字分類的影響，本研究使用深度學習模型對於不同語言的COVID-19 假新聞進行分類，第二個實驗為了驗證特徵重要性對於文字分類的影響，本研究設計了一種模糊特徵聚類方法應用於COVID-19 假新聞檢測。第三個實驗為了驗證資料不平衡性，在TCFD文本中使用文字增生模型強化資料特性並使用深度學習分類，為了解決深度學習的黑盒子問題，在實驗3使用可解釋性AI技術對TCFD文本進行解釋，最後為了驗證大型語言模型的效能，在3個實驗中都使用了最新的大型語言模型進行分析。
在結果上，實驗一顯示使用 BiLSTM深度學習模型，可以實現很高的分類準確率（英語為 99%，中文為 86%）。實驗2結果顯示整合關鍵特徵可以保持準確性，同時顯著減少檢測時間（英語為 10%，中文為 15%）。實驗3結果顯示使用生成對抗網路進行資料增強來解決資料不平衡提高了分類性能指標。可解釋的人工智慧技術提供了影響模型預測的關鍵特徵及其與分類結果的關係，而大型語言模型雖然未超過深度學習所達到的效果，但透過其內部大量的參數與訓練內容也提供了良好的分類效果。

The data in emerging fields exhibit unique characteristics, such as fragmented data, significant data loss, and extreme data imbalance. For instance, the research fields of COVID-19 and climate change have garnered significant attention in recent years. Beyond analyzing quantitative data such as the number of confirmed cases and climate temperature variations, natural language processing also plays a crucial role. In the case of COVID-19, the rapid spread of multilingual versions of fake news related to the pandemic posed significant challenges to public health authorities. In the climate change domain, the Task Force on Climate-related Financial Disclosures (TCFD) was recently introduced as a guideline for investors when evaluating companies, requiring businesses to gradually disclose their contributions to climate change. However, since these disclosures are voluntary, companies often reveal only information that is favorable to them. From the above, it is evident that research datasets in these two emerging fields exhibit vastly different data characteristics.
To address the varying characteristics of different datasets, the study designed three experiments to tackle these challenges. The first experiment aimed to verify the impact of multilingualism on text classification by using deep learning models to classify COVID-19 fake news in different languages. The second experiment examined the effect of feature importance on text classification by developing a fuzzy feature clustering method for COVID-19 fake news detection. The third experiment focused on verifying data imbalance by enhancing data characteristics in TCFD texts using a text augmentation model and applying deep learning classification. To address the black-box problem of deep learning, explainable AI techniques were employed in Experiment 3 to interpret TCFD texts. Lastly, to assess the performance of large language models, the latest models were used for analysis across all three experiments.
In the results, experiment 1 demonstrated that using the BiLSTM deep learning model achieved high classification accuracy (99% for English and 86% for Chinese). The results of Experiment 2 showed that integrating key features maintained accuracy while significantly reducing detection time (by 10% for English and 15% for Chinese). Experiment 3 indicated that using Generative Adversarial Networks (GANs) for data augmentation to address data imbalance improved classification performance metrics. Explainable AI techniques identified key features influencing model predictions and their relationship with classification outcomes. While large language models did not surpass the performance achieved by deep learning, they still provided strong classification results due to their extensive internal parameters and training data.

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