A Methodological Template to Construct Ground Truth of Authentic and Fake Online Reviews

Snehasish Banerjee

A Methodological Template to Construct Ground Truth of Authentic and Fake Online Reviews

Management

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

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.

Original language	English
Title of host publication	2018 IEEE International Conference on Data Science and Advanced Analytics
Publisher	IEEE
Pages	641-648
Number of pages	8
Publication status	Published - 2018
Event	IEEE International Conference on Data Science and Advanced Analytics - Turin, Turin, Italy Duration: 1 Oct 2018 → 4 Oct 2018 https://dsaa2018.isi.it/home

Conference

Conference	IEEE International Conference on Data Science and Advanced Analytics
Abbreviated title	DSAA
Country/Territory	Italy
City	Turin
Period	1/10/18 → 4/10/18
Internet address	https://dsaa2018.isi.it/home

Bibliographical note

© 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

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groung truth R1
© 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
Accepted author manuscript, 461 KB

Cite this

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title = "A Methodological Template to Construct Ground Truth of Authentic and Fake Online Reviews",

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.",

keywords = "CREDIBILITY, fake review, ground truth, online review, opinion spam, spam 2.0",

author = "Snehasish Banerjee",

note = "{\textcopyright} IEEE, 2018. This is an author-produced version of the published paper. Uploaded in accordance with the publisher{\textquoteright}s self-archiving policy. Further copying may not be permitted; contact the publisher for details; IEEE International Conference on Data Science and Advanced Analytics, DSAA ; Conference date: 01-10-2018 Through 04-10-2018",

year = "2018",

language = "English",

pages = "641--648",

booktitle = "2018 IEEE International Conference on Data Science and Advanced Analytics",

publisher = "IEEE",

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url = "https://dsaa2018.isi.it/home",

}

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T1 - A Methodological Template to Construct Ground Truth of Authentic and Fake Online Reviews

AU - Banerjee, Snehasish

N1 - © 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

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - CREDIBILITY

KW - fake review

KW - ground truth

KW - online review

KW - opinion spam

KW - spam 2.0

UR - https://ieeexplore.ieee.org/abstract/document/8631446

UR - http://dx.doi.org/10.1109/DSAA.2018.00083

M3 - Conference contribution

SP - 641

EP - 648

BT - 2018 IEEE International Conference on Data Science and Advanced Analytics

PB - IEEE

T2 - IEEE International Conference on Data Science and Advanced Analytics

Y2 - 1 October 2018 through 4 October 2018

ER -

A Methodological Template to Construct Ground Truth of Authentic and Fake Online Reviews

Abstract

Conference

Bibliographical note

Keywords

Access to Document

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Cite this