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Identifying fake Amazon reviews as learning from crowds

Fornaciari, Tommaso and Poesio, Massimo (2014) Identifying fake Amazon reviews as learning from crowds. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, 2014-04 - 2014-04.


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Customers who buy products such as books online often rely on other customers reviews more than on reviews found on specialist magazines. Unfortunately the confidence in such reviews is often misplaced due to the explosion of so-called sock puppetry-Authors writing glowing reviews of their own books. Identifying such deceptive reviews is not easy. The first contribution of our work is the creation of a collection including a number of genuinely deceptive Amazon book reviews in collaboration with crime writer Jeremy Duns, who has devoted a great deal of effort in unmasking sock puppeting among his colleagues. But there can be no certainty concerning the other reviews in the collection: All we have is a number of cues, also developed in collaboration with Duns, suggesting that a review may be genuine or deceptive. Thus this corpus is an example of a collection where it is not possible to acquire the actual label for all instances, and where clues of deception were treated as annotators who assign them heuristic labels. A number of approaches have been proposed for such cases; we adopt here the 'learning from crowds' approach proposed by Raykar et al. (2010). Thanks to Duns' certainly fake reviews, the second contribution of this work consists in the evaluation of the effectiveness of different methods of annotation, according to the performance of models trained to detect deceptive reviews. © 2014 Association for Computational Linguistics.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 14th Conference of the European Chapter of the Association for Computational Linguistics 2014, EACL 2014
Subjects: P Language and Literature > P Philology. Linguistics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 21 Aug 2015 10:52
Last Modified: 15 Jan 2022 00:39

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