會議論文
| 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 | en |
| 收錄於 | |
| 會議性質 | 國際 |
| 校內研討會地點 | 無 |
| 研討會時間 | 20170731~20170803 |
| 通訊作者 | |
| 國別 | AUS |
| 公開徵稿 | |
| 出版型式 | |
| 出處 | IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2017) |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/112056 ) |