Review: Review Spam Detection

Author: Nitin Jindal and Bing Liu
Year: 2007
Published in: The International World Wide Web Conference Committee
Importance: High


It is now a common practice for e-commerce Web sites to enable
their customers to write reviews of products that they have
purchased. Such reviews provide valuable sources of information
on these products. They are used by potential customers to find
opinions of existing users before deciding to purchase a product.
They are also used by product manufacturers to identify problems
of their products and to find competitive intelligence information
about their competitors. Unfortunately, this importance of reviews
also gives good incentive for spam, which contains false positive
or malicious negative opinions. In this paper, we make an attempt
to study review spam and spam detection. To the best of our
knowledge, there is still no reported study on this problem

My Review

This only two pages paper talks about review spams which is new to the word of web spam. According to author claim review spams are different from web and email spam so we need new methods for detecting review spam. Also review spams are hard to detect even manually.

Why hard to detect?

  1. Similarity to real reviews.
  2. Not enough meta-data for analysing.

Mainly, author tries to detect duplicate reviews in this paper and they provide a model based on shingle method. other type of review as author said are hard to detect and the outcome of their work is small.


Personally think that since still it is hard to detect review spam manually we should improve spam prevention methods such as CAPTCHA in order to disallow review spams (Sreview). So for the time being I have no idea on detecting review spam after postage.

Good reference

Web Data mining book by Bing Liu


One Response to Review: Review Spam Detection

  1. […] are currently unique area and they are different from other type of web spams as disscussed in Review: Review Spam Detection by the same authors. they classify review spams into 3 […]

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