A Framework for Review Spam Detection Research
Description
With the increasing number of people reviewing
products online in recent years, opinion sharing websites has become
the most important source of customers’ opinions. Unfortunately,
spammers generate and post fake reviews in order to promote or
demote brands and mislead potential customers. These are notably
destructive not only for potential customers, but also for business
holders and manufacturers. However, research in this area is not
adequate, and many critical problems related to spam detection have
not been solved to date. To provide green researchers in the domain
with a great aid, in this paper, we have attempted to create a highquality
framework to make a clear vision on review spam-detection
methods. In addition, this report contains a comprehensive collection
of detection metrics used in proposed spam-detection approaches.
These metrics are extremely applicable for developing novel
detection methods.
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Additional details
References
- Liu, Bing. Web data mining: exploring hyperlinks, contents, and usage data. Springer Science & Business Media, 2007. Liu (2011). Opinion mining and sentiment analysis. Web Data Mining, Springer: 459-526.
- Castillo, Carlos, and Brian D. Davison. "Adversarial web search." Foundations and trends in Information Retrieval 4, no. 5 (2011): 377-486.
- Heydari, Atefeh, Mohammad Ali Tavakoli, Naomie Salim, and Zahra Heydari. "Detection of review spam: A survey." Expert Systems with Applications 42, no. 7 (2015): 3634-3642.
- Jindal and Liu (2007a). Analyzing and Detecting Review Spam. Seventh IEEE International Conference on Data Mining.
- Newman et al. (2003). "Lying Words: Predicting Deception from Linguistic Styles." Personality and Social Psychology Bulletin 29: 5.
- Hancock et al. (2007). "On Lying and Being Lied To: A Linguistic Analysis of Deception in Computer- Mediated Communication." Discourse Processes 45: 23.
- Pennebaker et al. (2007). "The Development and Psychometric Properties of LIWC." www.LIWC.Net.
- Zhou et al. (2008). "A Statistical Language Modelling Approach to Online Deception Detection." IEEE Transactions on Knowledge and Data Engineering - TKDE, 20: 8.
- Mihalcea and Strapparava (2009). "The Lie Detector: Explorations in the Automatic Recognition of Deceptive Language." Conference: Meeting of the Association for Computational Linguistics - ACL: 4. [10] Jindal and Liu (2008). "Opinion Spam and Analysis." Conference of web search and web data mining: 11. [11] Jindal and Liu (2007b). "Review Spam Detection." World Wide Web Conference Series: 1189-1190. [12] Mukherjee et al. (2011). Detecting Group Review Spam. in Proceedings of International Conference on World Wide Web (WWW-2011, poster paper). [13] Mukherjee et al. (2012). Spotting Fake Reviewer Groups in Consumer Reviews. in Proceedings of International World Web Conference (WWW-2012). [14] Liu, Bing. "Sentiment analysis and opinion mining." Synthesis Lectures on Human Language Technologies 5, no. 1 (2012): 1-167. [15] Xie et al. (2012). Review Spam Detection via Temporal Pattern Discovery. international conference on Knowledge discovery and data mining [16] Zuriati Ismail, Atefeh Heydari, Mohammadali Tavakoli, Naomie Salim. "Incorporating Author's Activeness in Online Discussion in Thread Retrieval Model" ARPN Journal of Engineering and Applied Sciences 10 (2), 473-479 [17] Li et al. (2010). Learning to Identify Review Spam. Joint conference on AI [18] Ott et al. (2011). Finding Deceptive Opinion Spam by Any Stretch of the Imagination. 49th annual meeting of the association for the computational linguistics. [19] Wang et al. (2011). Review Graph based Online Store Review Spammer Detection. IEEE International Conference on Data Mining - ICDM 6.