| Field |
Value |
Language |
|
dc.contributor.author |
He, L |
|
|
dc.contributor.author |
Wang, X
https://orcid.org/0000-0001-9582-3445
|
|
|
dc.contributor.author |
Chen, H |
|
|
dc.contributor.author |
Xu, G
https://orcid.org/0000-0003-4493-6663
|
|
|
dc.date.accessioned |
2022-08-25T10:25:10Z |
|
|
dc.date.available |
2022-08-25T10:25:10Z |
|
|
dc.date.issued |
2022-05-05 |
|
|
dc.identifier.citation |
Human-Centric Intelligent Systems, 2022, 2, (1-2), pp. 14-30 |
|
|
dc.identifier.issn |
2667-1336 |
|
|
dc.identifier.issn |
2667-1336 |
|
|
dc.identifier.uri |
http://hdl.handle.net/10453/160857
|
|
|
dc.description.abstract |
<jats:title>Abstract</jats:title><jats:p>The increasingly developed online platform generates a large amount of online reviews every moment, e.g., Yelp and Amazon. Consumers gradually develop the habit of reading previous reviews before making a decision of buying or choosing various products. Online reviews play an vital part in determining consumers’ purchase choices in e-commerce, yet many online reviews are intentionally created to confuse or mislead potential consumers. Moreover, driven by product reputations and merchants’ profits, more and more spam reviews were inserted into online platform. This kind of reviews can be positive, negative or neutral, but they had common features: misleading consumers or damaging reputations. In the past decade, many people conducted research on detecting spam reviews using statistical or deep learning method with various datasets. In view of that, this article first introduces the task of spam online reviews detection and makes a common definition of spam reviews. Then, we comprehensively conclude the existing method and available datasets. Third, we summarize the existing network-based approaches in dealing with this task and propose some direction for future research.</jats:p> |
|
|
dc.language |
en |
|
|
dc.publisher |
Springer Science and Business Media LLC |
|
|
dc.relation |
http://purl.org/au-research/grants/arc/DP200101374
|
|
|
dc.relation |
eBay Inc |
|
|
dc.relation |
http://purl.org/au-research/grants/arc/DP220103717
|
|
|
dc.relation |
http://purl.org/au-research/grants/arc/DE180100251
|
|
|
dc.relation.ispartof |
Human-Centric Intelligent Systems |
|
|
dc.relation.isbasedon |
10.1007/s44230-022-00001-3 |
|
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
|
dc.title |
Online Spam Review Detection: A Survey of Literature |
|
|
dc.type |
Journal Article |
|
|
utslib.citation.volume |
2 |
|
|
pubs.organisational-group |
/University of Technology Sydney |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology |
|
|
pubs.organisational-group |
/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science |
|
|
utslib.copyright.status |
open_access |
* |
|
dc.date.updated |
2022-08-25T10:25:09Z |
|
|
pubs.issue |
1-2 |
|
|
pubs.publication-status |
Published |
|
|
pubs.volume |
2 |
|
|
utslib.citation.issue |
1-2 |
|
