Identifying deceptive review comments with rumor and lie theories

Chia Hsun Lin; Ping Yu Hsu; Ming Shien Cheng; Hong Tsuen Lei; Ming Chia Hsu

doi:10.1007/978-3-319-61833-3_44

Identifying deceptive review comments with rumor and lie theories

Chia Hsun Lin, Ping Yu Hsu, Ming Shien Cheng, Hong Tsuen Lei, Ming Chia Hsu

Department of Business Administration

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

5 Scopus citations

Abstract

The survey data showed that consumers trusted online reviews growing year by year, in which deceptive reviews had much more influences on consumer decisions. Unfortunately, due to the rapid transfer and enormous influence were typical of online reviews, many organizations began to deliberately exaggerate their own products or fabricated negative comments to attack competitors in order to derive benefit. Studies had shown that, it had much more influence by online reviews were tourism and hotel industry. This study discussed the negatively truthful review and the deceptive reviews from top twenty famous hotels in Chicago, including the true reviews taking from six famous review sites and the comparison group deceptive reviews on Amazon Mechanical Turk10. On the basis of the rumors and lies theories, the method created six attributes, key words of hotel, vague words personal pronoun negative words pronouns and pleonasm. By using text mining combined classification algorithm to forecast outcome and apply to build models. In this model showed that the mathematical operations not only worked more efficiently but kept the accuracy reasonably, so it could distinguish true or deceptive reviews well.

Original language	English
Title of host publication	Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings
Editors	Ben Niu, Hideyuki Takagi, Yuhui Shi, Ying Tan
Publisher	Springer Verlag
Pages	412-420
Number of pages	9
ISBN (Print)	9783319618326
DOIs	https://doi.org/10.1007/978-3-319-61833-3_44
State	Published - 2017
Event	8th International Conference on Swarm Intelligence, ICSI 2017 - Fukuoka, Japan Duration: 27 Jul 2017 → 1 Aug 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10386 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Swarm Intelligence, ICSI 2017
Country/Territory	Japan
City	Fukuoka
Period	27/07/17 → 1/08/17

Keywords

Deceptive review
Lie
Negative review
Rumor
Text mining

Access to Document

10.1007/978-3-319-61833-3_44

Cite this

Lin, C. H., Hsu, P. Y., Cheng, M. S., Lei, H. T., & Hsu, M. C. (2017). Identifying deceptive review comments with rumor and lie theories. In B. Niu, H. Takagi, Y. Shi, & Y. Tan (Eds.), Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings (pp. 412-420). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10386 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-61833-3_44

Lin, Chia Hsun ; Hsu, Ping Yu ; Cheng, Ming Shien et al. / Identifying deceptive review comments with rumor and lie theories. Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. editor / Ben Niu ; Hideyuki Takagi ; Yuhui Shi ; Ying Tan. Springer Verlag, 2017. pp. 412-420 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The survey data showed that consumers trusted online reviews growing year by year, in which deceptive reviews had much more influences on consumer decisions. Unfortunately, due to the rapid transfer and enormous influence were typical of online reviews, many organizations began to deliberately exaggerate their own products or fabricated negative comments to attack competitors in order to derive benefit. Studies had shown that, it had much more influence by online reviews were tourism and hotel industry. This study discussed the negatively truthful review and the deceptive reviews from top twenty famous hotels in Chicago, including the true reviews taking from six famous review sites and the comparison group deceptive reviews on Amazon Mechanical Turk10. On the basis of the rumors and lies theories, the method created six attributes, key words of hotel, vague words personal pronoun negative words pronouns and pleonasm. By using text mining combined classification algorithm to forecast outcome and apply to build models. In this model showed that the mathematical operations not only worked more efficiently but kept the accuracy reasonably, so it could distinguish true or deceptive reviews well.",

keywords = "Deceptive review, Lie, Negative review, Rumor, Text mining",

author = "Lin, {Chia Hsun} and Hsu, {Ping Yu} and Cheng, {Ming Shien} and Lei, {Hong Tsuen} and Hsu, {Ming Chia}",

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Lin, CH, Hsu, PY, Cheng, MS, Lei, HT & Hsu, MC 2017, Identifying deceptive review comments with rumor and lie theories. in B Niu, H Takagi, Y Shi & Y Tan (eds), Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10386 LNCS, Springer Verlag, pp. 412-420, 8th International Conference on Swarm Intelligence, ICSI 2017, Fukuoka, Japan, 27/07/17. https://doi.org/10.1007/978-3-319-61833-3_44

Identifying deceptive review comments with rumor and lie theories. / Lin, Chia Hsun; Hsu, Ping Yu; Cheng, Ming Shien et al.
Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. ed. / Ben Niu; Hideyuki Takagi; Yuhui Shi; Ying Tan. Springer Verlag, 2017. p. 412-420 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10386 LNCS).

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

TY - GEN

T1 - Identifying deceptive review comments with rumor and lie theories

AU - Lin, Chia Hsun

AU - Hsu, Ping Yu

AU - Cheng, Ming Shien

AU - Lei, Hong Tsuen

AU - Hsu, Ming Chia

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - The survey data showed that consumers trusted online reviews growing year by year, in which deceptive reviews had much more influences on consumer decisions. Unfortunately, due to the rapid transfer and enormous influence were typical of online reviews, many organizations began to deliberately exaggerate their own products or fabricated negative comments to attack competitors in order to derive benefit. Studies had shown that, it had much more influence by online reviews were tourism and hotel industry. This study discussed the negatively truthful review and the deceptive reviews from top twenty famous hotels in Chicago, including the true reviews taking from six famous review sites and the comparison group deceptive reviews on Amazon Mechanical Turk10. On the basis of the rumors and lies theories, the method created six attributes, key words of hotel, vague words personal pronoun negative words pronouns and pleonasm. By using text mining combined classification algorithm to forecast outcome and apply to build models. In this model showed that the mathematical operations not only worked more efficiently but kept the accuracy reasonably, so it could distinguish true or deceptive reviews well.

AB - The survey data showed that consumers trusted online reviews growing year by year, in which deceptive reviews had much more influences on consumer decisions. Unfortunately, due to the rapid transfer and enormous influence were typical of online reviews, many organizations began to deliberately exaggerate their own products or fabricated negative comments to attack competitors in order to derive benefit. Studies had shown that, it had much more influence by online reviews were tourism and hotel industry. This study discussed the negatively truthful review and the deceptive reviews from top twenty famous hotels in Chicago, including the true reviews taking from six famous review sites and the comparison group deceptive reviews on Amazon Mechanical Turk10. On the basis of the rumors and lies theories, the method created six attributes, key words of hotel, vague words personal pronoun negative words pronouns and pleonasm. By using text mining combined classification algorithm to forecast outcome and apply to build models. In this model showed that the mathematical operations not only worked more efficiently but kept the accuracy reasonably, so it could distinguish true or deceptive reviews well.

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A2 - Tan, Ying

PB - Springer Verlag

T2 - 8th International Conference on Swarm Intelligence, ICSI 2017

Y2 - 27 July 2017 through 1 August 2017

ER -

Lin CH, Hsu PY, Cheng MS, Lei HT, Hsu MC. Identifying deceptive review comments with rumor and lie theories. In Niu B, Takagi H, Shi Y, Tan Y, editors, Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. Springer Verlag. 2017. p. 412-420. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-61833-3_44

Identifying deceptive review comments with rumor and lie theories

Abstract

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Keywords

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