Using supervised learning to classify authentic and fake online reviews

Snehasish Banerjee; Alton Y.K. Chua; Jung Jae Kim

doi:10.1145/2701126.2701130

Using supervised learning to classify authentic and fake online reviews

Snehasish Banerjee, Alton Y.K. Chua, Jung Jae Kim

Nanyang Technological University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

47 Citations (Scopus)

Abstract

Before making a purchase, users are increasingly inclined to browse online reviews that are posted to share post-purchase experiences of products and services. However, not all reviews are necessarily authentic. Some entries could be fake yet written to appear authentic. Conceivably, authentic and fake reviews are not easy to differentiate. Hence, this paper uses supervised learning algorithms to analyze the extent to which authentic and fake reviews could be distinguished based on four linguistic clues, namely, understandability, level of details, writing style, and cognition indicators. The model performance was compared with two baselines. The results were generally promising.

Original language	English
Title of host publication	ACM IMCOM 2015 - Proceedings
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	9781450333771
DOIs	https://doi.org/10.1145/2701126.2701130
Publication status	Published - Jan 8 2015
Externally published	Yes
Event	9th International Conference on Ubiquitous Information Management and Communication, ACM IMCOM 2015 - Bali, Indonesia Duration: Jan 8 2015 → Jan 10 2015

Publication series

Name	ACM IMCOM 2015 - Proceedings

Conference

Conference	9th International Conference on Ubiquitous Information Management and Communication, ACM IMCOM 2015
Country/Territory	Indonesia
City	Bali
Period	1/8/15 → 1/10/15

ASJC Scopus Subject Areas

General Computer Science
Control and Systems Engineering
Management Information Systems

Keywords

Authentic online reviews
Fake online reviews
Internet shopping
Linguistic clues
Supervised learning

Access to Document

10.1145/2701126.2701130

Cite this

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title = "Using supervised learning to classify authentic and fake online reviews",

abstract = "Before making a purchase, users are increasingly inclined to browse online reviews that are posted to share post-purchase experiences of products and services. However, not all reviews are necessarily authentic. Some entries could be fake yet written to appear authentic. Conceivably, authentic and fake reviews are not easy to differentiate. Hence, this paper uses supervised learning algorithms to analyze the extent to which authentic and fake reviews could be distinguished based on four linguistic clues, namely, understandability, level of details, writing style, and cognition indicators. The model performance was compared with two baselines. The results were generally promising.",

keywords = "Authentic online reviews, Fake online reviews, Internet shopping, Linguistic clues, Supervised learning",

author = "Snehasish Banerjee and Chua, {Alton Y.K.} and Kim, {Jung Jae}",

year = "2015",

month = jan,

day = "8",

doi = "10.1145/2701126.2701130",

language = "English",

series = "ACM IMCOM 2015 - Proceedings",

publisher = "Association for Computing Machinery, Inc",

booktitle = "ACM IMCOM 2015 - Proceedings",

note = "9th International Conference on Ubiquitous Information Management and Communication, ACM IMCOM 2015 ; Conference date: 08-01-2015 Through 10-01-2015",

}

Banerjee, S, Chua, AYK & Kim, JJ 2015, Using supervised learning to classify authentic and fake online reviews. in ACM IMCOM 2015 - Proceedings., a88, ACM IMCOM 2015 - Proceedings, Association for Computing Machinery, Inc, 9th International Conference on Ubiquitous Information Management and Communication, ACM IMCOM 2015, Bali, Indonesia, 1/8/15. https://doi.org/10.1145/2701126.2701130

TY - GEN

T1 - Using supervised learning to classify authentic and fake online reviews

AU - Banerjee, Snehasish

AU - Chua, Alton Y.K.

AU - Kim, Jung Jae

PY - 2015/1/8

Y1 - 2015/1/8

N2 - Before making a purchase, users are increasingly inclined to browse online reviews that are posted to share post-purchase experiences of products and services. However, not all reviews are necessarily authentic. Some entries could be fake yet written to appear authentic. Conceivably, authentic and fake reviews are not easy to differentiate. Hence, this paper uses supervised learning algorithms to analyze the extent to which authentic and fake reviews could be distinguished based on four linguistic clues, namely, understandability, level of details, writing style, and cognition indicators. The model performance was compared with two baselines. The results were generally promising.

AB - Before making a purchase, users are increasingly inclined to browse online reviews that are posted to share post-purchase experiences of products and services. However, not all reviews are necessarily authentic. Some entries could be fake yet written to appear authentic. Conceivably, authentic and fake reviews are not easy to differentiate. Hence, this paper uses supervised learning algorithms to analyze the extent to which authentic and fake reviews could be distinguished based on four linguistic clues, namely, understandability, level of details, writing style, and cognition indicators. The model performance was compared with two baselines. The results were generally promising.

KW - Authentic online reviews

KW - Fake online reviews

KW - Internet shopping

KW - Linguistic clues

KW - Supervised learning

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DO - 10.1145/2701126.2701130

M3 - Conference contribution

AN - SCOPUS:84926137962

T3 - ACM IMCOM 2015 - Proceedings

BT - ACM IMCOM 2015 - Proceedings

PB - Association for Computing Machinery, Inc

T2 - 9th International Conference on Ubiquitous Information Management and Communication, ACM IMCOM 2015

Y2 - 8 January 2015 through 10 January 2015

ER -

Using supervised learning to classify authentic and fake online reviews

Abstract

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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