A Multi-Perspective Methodology for Detecting Low-Quality Contents in Social Medi

DOI

10.2991/icaicte-14.2014.9How to use a DOI?

Keywords

Opinion Credibility; Opinion Mining; Spam Analysis; Machine Learning; E-Business

Abstract

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the credibility of online consumer reviews. One of the reasons is the lack of an effective computational method to deal with the huge number of online reviews which are not embedded with explicit features for a spam detection system to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham). To improve the hygiene and the usefulness of online comments, there is a pressing need to develop a robust methodology for an objective and systematic assessment of the quality of online comments. The main contribution of this paper is the design, development, and evaluation of a novel information theory based methodology for the assessment of the quality of online comments. Our preliminary experiments show that the proposed quality assessment methodology is more effective than other baseline methods such as a peer-review based quality assessment approach.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Otto K.M. Cheng
AU  - Raymond Y.K. Lau
PY  - 2014/08
DA  - 2014/08
TI  - A Multi-Perspective Methodology for Detecting Low-Quality Contents in Social Medi
BT  - Proceedings of the 2014 International Conference on Advanced ICT (ICAICTE 2014)
PB  - Atlantis Press
SP  - 39
EP  - 42
SN  - 2352-538X
UR  - https://doi.org/10.2991/icaicte-14.2014.9
DO  - 10.2991/icaicte-14.2014.9
ID  - Cheng2014/08
ER  -