隨著網路平台的興起，人們習慣在消費前瀏覽網路平台上的評論，透過其他消費者的評論來幫助自己做選擇，然而研究估計所有網路評論中約有 15%至30%是假評論，部分商家為了增加自己的正面評論，並利用負面評論來攻擊競爭對手，便會購買寫手來撰寫假評論，這些假評論不僅會危害商家的名譽，也會降低第三方平台的可信度，使消費者無法獲得正確的資訊，因此假評論的偵測已成為網路平台中重要的議題，故本研究致力於建立一個假評論的分類模型，期望可以為假評論偵測領域做出貢獻。
本研究利用評論文章之語言風格與評論者之習性特徵作為模型特徵值，語言風格之特徵值包含詞性分析與語文探索與字詞計算之數量(Linguistic Inquiry and Word Count, LIWC)、易讀性、可信性、實據性語詞性分析，習性特徵包含評論者之評分亂度、評分偏差、間隔時間與帳號生命值，並搭配階層羅吉斯迴歸分析作為模型。由於評論文章與評論者之間為巢套關係，不符合一般迴歸分析中資料須具獨立性的前題，而過往研究均未探討資料的巢套關係，故本研究採用階層式的分析，考慮各個評論者之間的組間差異，並分析語言風格與習性特徵如何影響假評論的偵測，實驗結果顯示對於具有階層特性資料集，階層羅吉斯迴歸分析能夠有效的分類出假評論與假評論者，預測假評論者之準確率甚至高達 94%，且其成效優於其他機器學習演算法。

In this study, we propose a model to predict fake reviews and fake reviewers. Using linguistic features from each review and behavioral features from each reviewer as model features. There have been many studies exploring the detection of fake reviews. However, only few studies have considered both features from review and reviewer. And most of the studies use Logistic Model (LM) to predict fake reviews. Yet the relationship between reviews and reviewers is nested. One reviewer can write more than one review. With the data being hierarchical, we should use hierarchical analysis instead of using LM for prediction. Therefore, Hierarchical Logistic Model (HLM) is used as model in this study. The dataset is divided into sub-data sets according to different review amount of each reviewer and review time range of the review. The experimental results show that HLM can effectively classify fake reviews and fake reviewers due to the hierarchical characteristic of the data. In the analysis of fake reviews, the use of each reviewer’s reviews within one month can effectively predict fake reviews with an accuracy rate of 86%. However, it is more effective to predict fake reviewers when we consider more past reviews of each reviewer. The best reviewer prediction result is when we consider the reviews within the past 6 years of each reviewer. And the accuracy rate is 94%. All of the prediction results of HLM are better than other machine learning algorithms, such as Support Vector Machine (SVM), Random Forest (RF), Naive Bayes classifier (NB), KNearest Neighbor (KNN).

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