Banerjee, Snehasish orcid.org/0000-0001-6355-0470 (2018) A Methodological Template to Construct Ground Truth of Authentic and Fake Online Reviews. In: 2018 IEEE International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics, 01-04 Oct 2018 IEEE , ITA , pp. 641-648.
Abstract
With the emergence of opinion spam, scholars in recent years have been investigating how to distinguish between authentic and fake online reviews. In this research area however, constructing ground truth has been a tricky problem. When labeled datasets of authentic and fake reviews are unavailable, it becomes impossible to systematically investigate differences between the two. In light of this problem, the goal of this paper is three-fold: (1) To review existing approaches of developing ground truth, (2) To present an improved methodological template to construct ground truth, and (3) To conduct a quality-check of the newly constructed ground truth. The existing approaches are dissected to identify several peculiarities. The new approach invests in mitigating pitfalls in the current approaches. In the newly constructed ground truth, authentic reviews were found to be not easily distinguishable from fake reviews. Finally, new research directions are identified with the hope that scholars would be able to stay ahead in their relentless race against spammers.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © IEEE, 2018. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Keywords: | CREDIBILITY,fake review,ground truth,online review,opinion spam,spam 2.0 |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Social Sciences (York) > The York Management School |
Depositing User: | Pure (York) |
Date Deposited: | 01 Aug 2018 10:40 |
Last Modified: | 09 Feb 2025 00:05 |
Status: | Published |
Publisher: | IEEE |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134043 |