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Paper Type
ERF
Abstract
Despite the abundance and successes of studies on manipulated reviews, there have been notable limitations acknowledged by academia and industry alike. Some of which include the lack of rich datasets for feature extraction and the inability to marry verbal and nonverbal features for model development. Furthermore, prior studies have always relied on existing literature in order to select relevant features for analysis. The limitations mentioned have rendered recent attempts at combating review manipulations quite unreliable and they lack the efficacy to target complex manipulated reviews, for example fake reviewers whose profiles are set to private. We attempt to bridge this gap by proposing a hybrid framework that incorporates econometrics and machine learning in feature extraction, engineering and review detection. Furthermore we utilize these methods in tandem and derive new sets of features for future analysis. This emergent study has important implications for research and practice.
Recommended Citation
King-Kizito, Kelvin and Andoh-Baidoo, Francis Kofi, "A Support Vector Approach to Detecting Manipulated Reviews" (2020). AMCIS 2020 Proceedings. 19.
https://aisel.aisnet.org/amcis2020/virtual_communities/virtual_communities/19
A Support Vector Approach to Detecting Manipulated Reviews
Despite the abundance and successes of studies on manipulated reviews, there have been notable limitations acknowledged by academia and industry alike. Some of which include the lack of rich datasets for feature extraction and the inability to marry verbal and nonverbal features for model development. Furthermore, prior studies have always relied on existing literature in order to select relevant features for analysis. The limitations mentioned have rendered recent attempts at combating review manipulations quite unreliable and they lack the efficacy to target complex manipulated reviews, for example fake reviewers whose profiles are set to private. We attempt to bridge this gap by proposing a hybrid framework that incorporates econometrics and machine learning in feature extraction, engineering and review detection. Furthermore we utilize these methods in tandem and derive new sets of features for future analysis. This emergent study has important implications for research and practice.
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