Online “helpful” Lies: An Empirical Study of Helpfulness in Fake and Authentic Online Reviews

Alton Y.K. Chua; Xiaoyu Chen

doi:10.1007/978-3-030-96957-8_10

Online “helpful” Lies: An Empirical Study of Helpfulness in Fake and Authentic Online Reviews

Alton Y.K. Chua^*, Xiaoyu Chen

^*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

Some fake online reviews may have overlapping textual features with authentic reviews. This paper explores the nuanced differences between helpful and unhelpful reviews, as well as fake and authentic reviews. Four textual features—polarity, subjectivity, readability, and depth—are used for the investigation. Results suggest that subjectivity, readability and depth help to distinguish between helpful and unhelpful reviews but not between fake and authentic ones. However, polarity offers a clue to differentiate between helpful fake and helpful authentic reviews. Specifically, for positive entries, helpful fake reviews contain more contents indicative of surprise while helpful authentic had more expectation-confirmed words like satisfaction. For negative entries, helpful fake reviews contained more contents indicative of anger while authentic helpful ones carried more anxiety.

Original language	English
Title of host publication	Information for a Better World
Subtitle of host publication	Shaping the Global Future - 17th International Conference, iConference 2022, Proceedings
Editors	Malte Smits
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	91-99
Number of pages	9
ISBN (Print)	9783030969561
DOIs	https://doi.org/10.1007/978-3-030-96957-8_10
Publication status	Published - 2022
Externally published	Yes
Event	17th International Conference on Information for a Better World: Shaping the Global Future, iConference 2022 - Virtual, Online Duration: Feb 28 2022 → Mar 4 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13192 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Information for a Better World: Shaping the Global Future, iConference 2022
City	Virtual, Online
Period	2/28/22 → 3/4/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Authentic online reviews
Fake online reviews
Review helpfulness

Access to Document

10.1007/978-3-030-96957-8_10

Cite this

Chua, A. Y. K., & Chen, X. (2022). Online “helpful” Lies: An Empirical Study of Helpfulness in Fake and Authentic Online Reviews. In M. Smits (Ed.), Information for a Better World: Shaping the Global Future - 17th International Conference, iConference 2022, Proceedings (pp. 91-99). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13192 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-96957-8_10

Chua, Alton Y.K. ; Chen, Xiaoyu. / Online “helpful” Lies : An Empirical Study of Helpfulness in Fake and Authentic Online Reviews. Information for a Better World: Shaping the Global Future - 17th International Conference, iConference 2022, Proceedings. editor / Malte Smits. Springer Science and Business Media Deutschland GmbH, 2022. pp. 91-99 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ad4ace14b44f4cb0b593a19db0bada7b,

title = "Online “helpful” Lies: An Empirical Study of Helpfulness in Fake and Authentic Online Reviews",

abstract = "Some fake online reviews may have overlapping textual features with authentic reviews. This paper explores the nuanced differences between helpful and unhelpful reviews, as well as fake and authentic reviews. Four textual features—polarity, subjectivity, readability, and depth—are used for the investigation. Results suggest that subjectivity, readability and depth help to distinguish between helpful and unhelpful reviews but not between fake and authentic ones. However, polarity offers a clue to differentiate between helpful fake and helpful authentic reviews. Specifically, for positive entries, helpful fake reviews contain more contents indicative of surprise while helpful authentic had more expectation-confirmed words like satisfaction. For negative entries, helpful fake reviews contained more contents indicative of anger while authentic helpful ones carried more anxiety.",

keywords = "Authentic online reviews, Fake online reviews, Review helpfulness",

author = "Chua, {Alton Y.K.} and Xiaoyu Chen",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 17th International Conference on Information for a Better World: Shaping the Global Future, iConference 2022 ; Conference date: 28-02-2022 Through 04-03-2022",

year = "2022",

doi = "10.1007/978-3-030-96957-8_10",

language = "English",

isbn = "9783030969561",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "91--99",

editor = "Malte Smits",

booktitle = "Information for a Better World",

address = "Germany",

}

Chua, AYK & Chen, X 2022, Online “helpful” Lies: An Empirical Study of Helpfulness in Fake and Authentic Online Reviews. in M Smits (ed.), Information for a Better World: Shaping the Global Future - 17th International Conference, iConference 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13192 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 91-99, 17th International Conference on Information for a Better World: Shaping the Global Future, iConference 2022, Virtual, Online, 2/28/22. https://doi.org/10.1007/978-3-030-96957-8_10

Online “helpful” Lies: An Empirical Study of Helpfulness in Fake and Authentic Online Reviews. / Chua, Alton Y.K.; Chen, Xiaoyu.
Information for a Better World: Shaping the Global Future - 17th International Conference, iConference 2022, Proceedings. ed. / Malte Smits. Springer Science and Business Media Deutschland GmbH, 2022. p. 91-99 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13192 LNCS).

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

TY - GEN

T1 - Online “helpful” Lies

T2 - 17th International Conference on Information for a Better World: Shaping the Global Future, iConference 2022

AU - Chua, Alton Y.K.

AU - Chen, Xiaoyu

PY - 2022

Y1 - 2022

N2 - Some fake online reviews may have overlapping textual features with authentic reviews. This paper explores the nuanced differences between helpful and unhelpful reviews, as well as fake and authentic reviews. Four textual features—polarity, subjectivity, readability, and depth—are used for the investigation. Results suggest that subjectivity, readability and depth help to distinguish between helpful and unhelpful reviews but not between fake and authentic ones. However, polarity offers a clue to differentiate between helpful fake and helpful authentic reviews. Specifically, for positive entries, helpful fake reviews contain more contents indicative of surprise while helpful authentic had more expectation-confirmed words like satisfaction. For negative entries, helpful fake reviews contained more contents indicative of anger while authentic helpful ones carried more anxiety.

AB - Some fake online reviews may have overlapping textual features with authentic reviews. This paper explores the nuanced differences between helpful and unhelpful reviews, as well as fake and authentic reviews. Four textual features—polarity, subjectivity, readability, and depth—are used for the investigation. Results suggest that subjectivity, readability and depth help to distinguish between helpful and unhelpful reviews but not between fake and authentic ones. However, polarity offers a clue to differentiate between helpful fake and helpful authentic reviews. Specifically, for positive entries, helpful fake reviews contain more contents indicative of surprise while helpful authentic had more expectation-confirmed words like satisfaction. For negative entries, helpful fake reviews contained more contents indicative of anger while authentic helpful ones carried more anxiety.

KW - Authentic online reviews

KW - Fake online reviews

KW - Review helpfulness

UR - http://www.scopus.com/inward/record.url?scp=85126242466&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85126242466&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-96957-8_10

DO - 10.1007/978-3-030-96957-8_10

M3 - Conference contribution

AN - SCOPUS:85126242466

SN - 9783030969561

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 91

EP - 99

BT - Information for a Better World

A2 - Smits, Malte

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 28 February 2022 through 4 March 2022

ER -

Chua AYK, Chen X. Online “helpful” Lies: An Empirical Study of Helpfulness in Fake and Authentic Online Reviews. In Smits M, editor, Information for a Better World: Shaping the Global Future - 17th International Conference, iConference 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 91-99. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-96957-8_10

Online “helpful” Lies: An Empirical Study of Helpfulness in Fake and Authentic Online Reviews

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

Other files and links

Fingerprint

Cite this