TY - JOUR
T1 - A Novel Approach for Fake Comments and Reviews Detection on the Online Social Networks
AU - Gaurav, Akshat
AU - Gupta, B. B.
AU - Chui, Kwok Tai
AU - Peraković, Dragan
AU - Chaurasia, Priyanka
AU - Hsu, Ching Hsien
N1 - Publisher Copyright:
© 2021 Copyright for this paper by its authors.
PY - 2021
Y1 - 2021
N2 - As the primary source of information dissemination, social media networks have surpassed traditional news organisations for the first time. Nonetheless, as the number of people who use social media websites grows, they become more susceptible to the spread of misinformation, making it increasingly difficult to distinguish between real news and false news in real time. In this paper, we proposed a machine learning technique for the detection of fake comments in social networks. According to the results of the experiment, it is clear that the machine learning technique efficiently detects the fake comments.
AB - As the primary source of information dissemination, social media networks have surpassed traditional news organisations for the first time. Nonetheless, as the number of people who use social media websites grows, they become more susceptible to the spread of misinformation, making it increasingly difficult to distinguish between real news and false news in real time. In this paper, we proposed a machine learning technique for the detection of fake comments in social networks. According to the results of the experiment, it is clear that the machine learning technique efficiently detects the fake comments.
KW - Deep learning
KW - Fake comments
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85124397475&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85124397475
SN - 1613-0073
VL - 3080
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2021 International Conference on Smart Systems and Advanced Computing, SysCom 2021
Y2 - 26 December 2021 through 27 December 2021
ER -