教師資料查詢 | 類別: 會議論文 | 教師: 陳建彰 Chen, Chien-chang (瀏覽個人網頁)

標題:Temporal and Sentimental Analysis of A Real Case of Fake Reviews in Taiwan
學年105
學期2
發表日期2017/07/31
作品名稱Temporal and Sentimental Analysis of A Real Case of Fake Reviews in Taiwan
作品名稱(其他語言)
著者Chih-Chien Wang, Min-Yuh Day, Chien-Chang Chen, Jai-Wei Liou
作品所屬單位
出版者
會議名稱IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2017)
會議地點Sydney, Australia
摘要Product reviews are important information sources for consumers as they make their purchasing decisions. However, some unethical firms hire fake reviewers to generate biased positive reviews to promote their product and to damage the product reputations of their competitors. From the point of view of online product review platform providers, it is essential to keep the platform neutral and unbiased by detecting fake reviews and preventing fake reviewers from spreading biased reviews. In the current study, we attempt to use temporal and sentiment analyses as cues to separate fake reviews from authentic product reviews. Real case data of fake reviews in Taiwan was used for this temporal and sentiment analysis. Based on the analysis results, we find that fake reviewers usually generated and replied to fake reviewers during normal work hours. In contrast, ordinary users only generated and replied to a small proportion of normal product reviews during work hours. They generated and replied to normal product reviews the most during off-work hours and weekends. Additionally, the current study also revealed that more than half of fake reviewers replied others’ responses to their own fake reviews no later than within one day. The research results revealed that temporal and sentiment analyses have the potential to serve as cues to detect fake reviews and fake reviewers.
關鍵字Spammers; Fake Reviewers; Fake Review; Temporal Analysis; Sentiment Analysi
語言英文
收錄於
會議性質國際
校內研討會地點
研討會時間20170731~20170803
通訊作者
國別澳洲
公開徵稿
出版型式
出處IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2017)
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