SBIR Phase I:Software to Prevent the Manipulation of Consumer Reviews of Online Businesses and Websites

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1014075
Agency Tracking Number: 1014075
Amount: $149,600.00
Phase: Phase I
Program: SBIR
Awards Year: 2010
Solicitation Year: 2010
Solicitation Topic Code: IC
Solicitation Number: NSF 09-609
Small Business Information
3150 18th Street, San Francisco, CA, 94110
DUNS: 020107209
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 Michael Lai
 (617) 519-1983
Business Contact
 Michael Lai
Title: PhD
Phone: (617) 519-1983
Research Institution
This Small Business Innovation Research (SBIR) Phase I project will develop software to prevent the manipulation of consumer reviews of websites and online businesses. Consumer reviews are a vital component in educating consumers about the trustworthiness of websites. However, any platform that makes it easy for consumers to review websites also makes itself vulnerable to abuse from actors who write purposefully deceptive, self-promoting, or low-quality reviews. These manipulative reviews can mislead consumers and permanently damage the credibility of the platform on which the reviews are published. Preventing these reviews is difficult because although traditional spam filters are effective in filtering automated spam, they are unable to detect manipulative reviews written by humans. This project will assess the feasibility of building and training a customized content filter with additional heuristic algorithms incorporating community feedback, reviewer attributes, and supplemental third-party data, to effectively detect and remove both automated and human-generated manipulative reviews. The FBI received 275,000 complaints of online fraud in 2008. The Washington Post has estimated $100 billion is lost every year in online fraud. Sites that often present the biggest risk to consumers include health information providers, paid online service providers, small retailers, and sites based outside the US. To address this problem, the company will help consumers identify the best and worst websites quickly and easily via reviews written by members of the community. A typical use case might involve a consumer who is looking to make a purchase on an obscure and unfamiliar website. Using the solution, instead of taking a risk, the consumer could look up the website in question and benefit from the experiences of other consumers to learn important information such as: whether the website is involved in any known scams, if the depictions of goods or services is consistent with what is delivered, and whether there is a better website which provides similar goods or services. If successfully deployed, the solution described in this research effort will address a significant and growing problem related to e-commerce.

* Information listed above is at the time of submission. *

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