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)

Abstract

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.

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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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