Credible Review Detection with Limited Information Using Consistency Features

  • Subhabrata Mukherjee
  • Sourav Dutta
  • Gerhard Weikum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9852)


Online reviews provide viewpoints on the strengths and shortcomings of products/services, influencing potential customers’ purchasing decisions. However, the proliferation of non-credible reviews — either fake (promoting/ demoting an item), incompetent (involving irrelevant aspects), or biased — entails the problem of identifying credible reviews. Prior works involve classifiers harnessing rich information about items/users — which might not be readily available in several domains — that provide only limited interpretability as to why a review is deemed non-credible.

This paper presents a novel approach to address the above issues. We utilize latent topic models leveraging review texts, item ratings, and timestamps to derive consistency features without relying on item/user histories, unavailable for “long-tail” items/users. We develop models, for computing review credibility scores to provide interpretable evidence for non-credible reviews, that are also transferable to other domains — addressing the scarcity of labeled data. Experiments on real-world datasets demonstrate improvements over state-of-the-art baselines.


  1. 1.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  2. 2.
    Chen, D.R., Wu, Q., Ying, Y., Zhou, D.X.: Support vector machine soft margin classifiers: error analysis. J. Mach. Learn. Res. 5, 1143–1175 (2004)MathSciNetMATHGoogle Scholar
  3. 3.
    Chen, Y.R., Chen, H.H.: Opinion spam detection in web forum:a real case study. In: WWW (2015)Google Scholar
  4. 4.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)MATHGoogle Scholar
  5. 5.
    Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)MATHGoogle Scholar
  6. 6.
    Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., Ghosh, R.: Exploiting burstiness in reviews for review spammer detection. In: ICWSM (2013)Google Scholar
  7. 7.
    Feng, S., Banerjee, R., Choi, Y.: Syntactic stylometry for deception detection. In: ACL (2012)Google Scholar
  8. 8.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: KDD (2004)Google Scholar
  9. 9.
    Hu, N., Bose, I., Koh, N.S., Liu, L.: Manipulation of online reviews: an analysis of ratings, readability, and sentiments. Decis. Support Syst. 52(3), 674–684 (2012)CrossRefGoogle Scholar
  10. 10.
    Jindal, N., Liu, B.: Analyzing and detecting review spam. In: ICDM, pp. 547–552 (2007)Google Scholar
  11. 11.
    Jindal, N., Liu, B.: Opinion spam and analysis. In: WSDM, pp. 219–230 (2008)Google Scholar
  12. 12.
    Joachims, T.: Optimizing search engines using clickthrough data. In: KDD (2002)Google Scholar
  13. 13.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: ICML (2014)Google Scholar
  14. 14.
    Li, H., Chen, Z., Liu, B., Wei, X., Shao, J.: Spotting fake reviews via collective positive-unlabeled learning. In: ICDM, pp. 899–904 (2014)Google Scholar
  15. 15.
    Li, H., Chen, Z., Mukherjee, A., Liu, B., Shao, J.: Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns. In: ICWSM (2015)Google Scholar
  16. 16.
    Li, J., Ott, M., Cardie, C.: Identifying manipulated offerings on review portals. In: EMNLP (2013)Google Scholar
  17. 17.
    Lim, E., Nguyen, V., Jindal, N., Liu, B., Lauw, H.W.: Detecting product review spammers using rating behaviors. In: CIKM, pp. 939–948 (2010)Google Scholar
  18. 18.
    Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: CIKM (2009)Google Scholar
  19. 19.
    Liu, T.Y.: Learning to rank for information retrieval. Found. Trends Inf. Retrieval 3(3), 225–331 (2009)CrossRefGoogle Scholar
  20. 20.
    Luca, M., Zervas, G.: Fake it till you make it: Reputation, competition, and yelp review fraud. Technical report, Harvard Business School (2015)Google Scholar
  21. 21.
    Mihalcea, R., Strapparava, C.: The lie detector: explorations in the automatic recognition of deceptive language. In: ACL/IJCNLP (Short Papers), pp. 309–312 (2009)Google Scholar
  22. 22.
    Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., Ghosh, R.: Spotting opinion spammers using behavioral footprints. In: KDD, pp. 632–640 (2013)Google Scholar
  23. 23.
    Mukherjee, A., Liu, B., Glance, N.S.: Spotting fake reviewer groups in consumer reviews. In: WWW, pp. 191–200 (2012)Google Scholar
  24. 24.
    Mukherjee, A., Venkataraman, V., Liu, B., Glance, N.S.: What yelp fake review filter might be doing? In: ICWSM (2013)Google Scholar
  25. 25.
    Mukherjee, S., Weikum, G., Danescu-Niculescu-Mizil, C.: People on drugs: credibility of user statements in health communities. In: KDD, pp. 65–74 (2014)Google Scholar
  26. 26.
    Ott, M., Cardie, C., Hancock, J.T.: Negative deceptive opinion spam. In: NAACL (2013)Google Scholar
  27. 27.
    Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: ACL-HLT, vol. 1. pp. 309–319 (2011)Google Scholar
  28. 28.
    Pennebaker, J., Francis, M., Booth, R.: Linguistic Inquiry and Word Count: A Computerized Text Analysis Program. Psychology Press, Mahwah (2001)Google Scholar
  29. 29.
    Rahman, M., Carbunar, B., Ballesteros, J., Chau, D.H.P.: To catch a fake: curbing deceptive yelp ratings and venues. Stat. Anal. Data Min. 8(3), 147–161 (2015)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Strapparava, C., Valitutti, A.:WordNet-Affect: an affective extension of wordnet. In: LREC (2004)Google Scholar
  31. 31.
    Sun, H., Morales, A., Yan, X.: Synthetic review spamming and defense. In: KDD (2013)Google Scholar
  32. 32.
    Wang, G., Xie, S., Liu, B., Yu, P.S.: Review graph based online store review spammer detection. In: ICDM, pp. 1242–1247 (2011)Google Scholar
  33. 33.
    Yoo, K.H., Gretzel, U.: Comparison of deceptive and truthful travel reviews. In: ENTER (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Subhabrata Mukherjee
    • 1
  • Sourav Dutta
    • 1
  • Gerhard Weikum
    • 1
  1. 1.Max Planck Institute for InformaticsSaarbrückenGermany

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