A System for Supporting the Detection of Deceptive Reviews Using Argument Mining
File(s)FAIA287-0469.pdf (183.12 KB)
Published version
Author(s)
Cocarascu, O
Toni, F
Type
Conference Paper
Abstract
The unstoppable rise of social networks and the web is facing a serious challenge: identifying the truthfulness of online opinions and reviews. We propose a system to identify two new argumentative features that a trained classifier can use to help determine whether a review is deceptive.
Editor(s)
Baroni, P
Gordon, TF
Scheffler, T
Stede, M
Date Issued
2016-09-16
Date Acceptance
2016-09-12
ISBN
978-1-61499-686-6
ISSN
0922-6389
Publisher
IOS PRESS
Start Page
469
End Page
470
Journal / Book Title
Computational Models of Argument
Volume
287
Copyright Statement
© 2016 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000383377900050&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Source
6th International Conference on Computational Models of Argument (COMMA)
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Logic
Computer Science
Science & Technology - Other Topics
Deception
Argument mining
Abstract argumentation
Publication Status
Published
Start Date
2016-09-12
Finish Date
2016-09-16
Coverage Spatial
Univ Potsdam, Potsdam, GERMANY