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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林永松 | |
| dc.contributor.author | Chen Chen | en |
| dc.contributor.author | 陳晨 | zh_TW |
| dc.date.accessioned | 2021-06-17T03:32:45Z | - |
| dc.date.available | 2021-03-01 | |
| dc.date.copyright | 2018-03-01 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-02-13 | |
| dc.identifier.citation | [1] Basuroy, S., Chatterjee, S., & Ravid, S. A. (2003). How critical are critical reviews? The box office effects of film critics, star power, and budgets. Journal of marketing, 67(4), 103-117.
[2] Jindal, N., & Liu, B. (2008, February). Opinion spam and analysis. In Proceedings of the 2008 International Conference on Web Search and Data Mining (pp. 219-230). ACM. [3] Lim, E. P., Nguyen, V. A., Jindal, N., Liu, B., & Lauw, H. W. (2010, October). Detecting product review spammers using rating behaviors. In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 939-948). ACM. [4] Benevenuto, F., Magno, G., Rodrigues, T., & Almeida, V. (2010, July). Detecting spammers on twitter. In Collaboration, electronic messaging, anti-abuse and spam conference (CEAS) (Vol. 6, p. 12). [5] Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J., & Gonçalves, M. (2009, July). Detecting spammers and content promoters in online video social networks. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (pp. 620-627). ACM. [6] Amleshwaram, A. A., Reddy, N., Yadav, S., Gu, G., & Yang, C. (2013, January). Cats: characterizing automation of twitter spammers. In 2013 Fifth International Conference on Communication Systems and Networks (COMSNETS) (pp. 1-10). IEEE. [7] Stringhini, G., Kruegel, C., & Vigna, G. (2010, December). Detecting spammers on social networks. In Proceedings of the 26th Annual Computer Security Applications Conference (pp. 1-9). ACM. [8] Mukherjee, A., Liu, B., & Glance, N. (2012, April). Spotting fake reviewer groups in consumer reviews. In Proceedings of the 21st international conference on World Wide Web (pp. 191-200). ACM. [9] Xu, C. (2013, November). Detecting collusive spammers in online review communities. In Proceedings of the sixth workshop on Ph. D. students in information and knowledge management (pp. 33-40). ACM. [10] Lin, C., He, J., Zhou, Y., Yang, X., Chen, K., & Song, L. (2013, August). Analysis and identification of spamming behaviors in sina weibo microblog. In Proceedings of the 7th Workshop on Social Network Mining and Analysis (p. 5). ACM. [11] Wang, K., Xiao, Y., & Xiao, Z. (2014, January). Detection of Internet water army in social network. In Proc. of the 2014 Int'l Conf. on Computer, Communications and Information Technology (CCIT 2014). Amsterdam: Atlantis Press (pp. 189-192). [12] Li, X., & Yan, X. (2016, June). A Novel Chinese Text Mining Method for E-Commerce Review Spam Detection. In International Conference on Web-Age Information Management(pp. 95-106). Springer, Cham. [13] Li, F., Huang, M., Yang, Y., & Zhu, X. (2011, July). Learning to identify review spam. In IJCAI Proceedings-International Joint Conference on Artificial Intelligence (Vol. 22, No. 3, p. 2488). [14] Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011, June). Finding deceptive opinion spam by any stretch of the imagination. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1 (pp. 309-319). Association for Computational Linguistics. [15] Wang, G., Xie, S., Liu, B., & Philip, S. Y. (2011, December). Review graph based online store review spammer detection. In 2011 IEEE 11th International Conference on Data Mining (pp. 1242-1247). IEEE. [16] Zhu, Y. X., Zhang, X. G., Sun, G. Q., Tang, M., Zhou, T., & Zhang, Z. K. (2014). Influence of reciprocal links in social networks. PloS one, 9(7), e103007. [17] Gilbert, E., & Karahalios, K. (2010, February). Understanding deja reviewers. In Proceedings of the 2010 ACM conference on Computer supported cooperative work (pp. 225-228). ACM. [18] Vrij, A. (2008). Detecting lies and deceit: Pitfalls and opportunities. John Wiley & Sons. [19] Harris, C. (2012, July). Detecting deceptive opinion spam using human computation. In Workshops at AAAI on Artificial Intelligence. [20] Sun, J. (2012). 'Jieba' Chinese word segmentation tool. [21] Ramos, J. (2003, December). Using tf-idf to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning (Vol. 242, pp. 133-142). [22] Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18. [23] G. Piatetsky-Shapiro. KD nuggets news on SIGKDD service award. http://www.kdnuggets.com/news/2005/n13/2i.html, 2005. [24] Tsang, A. S., & Prendergast, G. (2009). Is a 'star' worth a thousand words? The interplay between product-review texts and rating valences. European Journal of Marketing, 43(11/12), 1269-1280. [25] Fleiss, J. L. (1971). Measuring nominal scale agreement among many raters. Psychological bulletin, 76(5), 378. [26] Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, 159-174. [27] Biber, D., & Conrad, S. (1999). Lexical bundles in conversation and academic prose. Language and Computers, 26, 181-190. [28] Rayson, P., Wilson, A., & Leech, G. (2002). Grammatical word class variation within the British National Corpus sampler. Language and Computers, 36, 295-306. [29] Hancock, J. T., Curry, L. E., Goorha, S., & Woodworth, M. (2007). On lying and being lied to: A linguistic analysis of deception in computer-mediated communication. Discourse Processes, 45(1), 1-23. [30] Newman, M. L., Pennebaker, J. W., Berry, D. S., & Richards, J. M. (2003). Lying words: Predicting deception from linguistic styles. Personality and social psychology bulletin, 29(5), 665-675. [31] Heydari, A., ali Tavakoli, M., Salim, N., & Heydari, Z. (2015). Detection of review spam: A survey. Expert Systems with Applications, 42(7), 3634-3642. 中文參考文獻： [32] 黄娅琴,&李娟.(2016). '网络水军'的社会危害及其治理.传媒(14),81-83. [33] 陈样平.(2013). '网络水军'围剿中国电影.时代人物(1),100-101. [34] 黄晓婷.(2014).'水军':电影恶性网络营销中的潜伏者.传媒观察(4), 20-21. [35] 王烁,徐健,&刘颖.(2014).网络'水军'探测方法研究.现代图书情报技术,30(z1),92-100. [36] 状态. (2012). 大片上映水军互黑 电影营销亟待规范. 大众电影(24), 3-3. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69892 | - |
| dc.description.abstract | 近年來中國電影產業蓬勃發展，電影評論網站的評分和評論越來越成為人們決定是否去影院觀看電影的重要依據。電影發行公司為了獲利，常會僱傭網路水軍（Review Spammer）對自己電影評分進行美化或是對競爭對手的電影進行抹黑。為了消除水軍的失真影響，還原電影的真實分數，故進行此項研究。
前人水軍識別的研究大多聚焦于社交網路和電子商務網站，但幾乎沒人涉及電影評論網站的水軍識別。我們提出了一個水軍識別分類的方法，對中國某著名的電影評論網站進行水軍探測。首先，我們結合前人的研究和該電影評論網站的特點，提出了23個分類特徵。接著我們爬取了29部被水軍污染嫌疑較高的電影的評論資料及評論者資料。利用2個分類特征進行排序，得到分層樣本，又另外隨機抽取了一個樣本進行人工標記。用這些樣本訓練的分類器對四部被水軍污染嫌疑最大的電影進行分類預測，去除被我們的模型標記為水軍的評分記錄之後，這四部電影的評分更加接近於真實分數。我們希望我們模型產生的結果可以給網站使用者以參考。 | zh_TW |
| dc.description.abstract | With the rapid development of Chinese film industry, movie rating website has become a significant factor when audience decide whether to see a movie in the cinema. To gain profit, some film distribution companies may hire review spammers to fame their own movies or defame the competitors' movies. To fight against review spammers and restore the authentic grade of movies, we conduct this study.
Prior studies have focus on the review spam detection in social network and e-commerce. Nearly no researcher study on detection of review spam in movie rating websites. Taking a famous Chinese movie rating website as an example, we study the detection of review spammers for movie rating websites. Firstly, combining features used in prior researches and original features raised by ourselves, three types of features are proposed by us. Then, we crawl down reviews data of 29 movies, which are high possibly polluted by spammers. We also crawl down the profiles of these reviewers. Two samples gained stratifiedly and randomly are labeled by three evaluators. Classification models trained by these samples show a good performance. We sincerely hope our model can be used for reference by users. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T03:32:45Z (GMT). No. of bitstreams: 1 ntu-107-R04725052-1.pdf: 1704113 bytes, checksum: 3e5d5c427ecbfc825da425d638002962 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 中文摘要 ii
Abstract iii List of Figures vi List of Tables vii Chapter 1: Introduction 1 1.1 Background 1 1.2 Research Motivation and Expected Contributions 5 Chapter 2: Related Work 7 2.1 Development History of Review Spam 7 2.2 Approaches on Detecting Review Spam 8 2.2.1 Review Centric Approaches 9 2.2.2 Reviewer Centric Approaches 10 2.2.3 Reviewer Group Centric Approaches 11 2.3 Research Status of Review Spam Detection on Movie Rating Websites 12 Chapter 3: Model 13 3.1 Introduction to Research Object 13 3.2 Classification Features 13 3.2.1 Social Network Features 14 3.2.2 Non-content Based Features 14 3.2.3 Content Based Features 18 3.3 Classifier 19 Chapter 4: Experiment 20 4.1 Data Collection 20 4.2 Human Judgement 22 4.3 Feature Analysis 23 4.4 Evaluation Results 28 4.4.1 Classification Results 28 4.4.2 Effects of Features 29 4.4.3 Effects of Classifiers 30 4.4.4 Model Evaluation 31 Chapter 5: Conclusions and Future Work 36 5.1 Conclusions 36 5.2 Future Work 37 References 39 Appendix A 43 Appendix B 46 Appendix C 48 | |
| dc.language.iso | en | |
| dc.subject | 水軍探測 | zh_TW |
| dc.subject | 電影評分 | zh_TW |
| dc.subject | 監督分類 | zh_TW |
| dc.subject | Spammer Detection | en |
| dc.subject | Movie Rating | en |
| dc.subject | Supervised Classification | en |
| dc.title | 電影評論網站的水軍探測 | zh_TW |
| dc.title | Detection of Review Spammers for Movie Rating Websites | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 莊東穎,林宜隆,溫演福 | |
| dc.subject.keyword | 水軍探測,電影評分,監督分類, | zh_TW |
| dc.subject.keyword | Spammer Detection,Movie Rating,Supervised Classification, | en |
| dc.relation.page | 48 | |
| dc.identifier.doi | 10.6342/NTU201800570 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-02-14 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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