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ISSN:
1798-2340 (Online)
Frequency:
Monthly
DOI:
10.12720/jait
Indexing:
ESCI (Web of Science)
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Scopus
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CNKI
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etc
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Acceptance Rate:
19%
APC:
500 USD
Average Days to Accept:
135 days
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Impact Factor 2022: 1.0
3.1
2022
CiteScore
49th percentile
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Editor-in-Chief
Prof. Kin C. Yow
University of Regina, Saskatchewan, Canada
I'm delighted to serve as the Editor-in-Chief of
Journal of Advances in Information Technology
.
JAIT
is intended to reflect new directions of research and report latest advances in information technology. I will do my best to increase the prestige of the journal.
What's New
2024-03-28
Vol. 15, No. 3 has been published online!
2024-02-26
The papers published in Vol. 15, Nos. 1&2 have been registered with Crossref.
2024-02-26
Vol. 15, No. 2 has been published online!
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2022
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Volume 13, No. 5, October 2022
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JAIT 2022 Vol.13(5): 518-523
doi: 10.12720/jait.13.5.518-523
Social Media Fake Profile Detection Using Data Mining Technique
Nitika Kadam and Sanjeev Kumar Sharma
Computer Science Engineering, Oriental University, Indore, India
Abstract
—In social media, a significant amount of data has been distributed in the entire world with thousands of new users joining social media each day. Social media is a virtual life where malicious users can impact someone’s reputation. Mostly such kind of activity is performed by fake accounts. Thus, identification of fake profiles is necessary and can be done in the early stage of profile building is an essential task for ML. In this paper, the aim is to design a ML model which identifies fake profiles in the early stage and ML based survey on social media has been carried out. Further, the collected literature is categorized according to the used social media datasets and popular areas of employing ML in social media platforms. In this investigation, we have used the Twitter dataset fake profile detection to demonstrate the proposed idea of ML-based fake news detection. The proposed model includes preprocessing to refine the contents and attributes to improve the quality of the dataset and reduce dimensions of the data. The next five popular ML algorithms namely C4.5, Bayes classifier, SVM, ANN, and KNN algorithms are implemented to predict the fake profiles. The evaluation of the system is performed under two scenarios based on training and testing sample ratio of 70-30% and 80-20% and using 4-fold cross-validation. Findings show 80-20% based samples reduce the resource consumption and 70-30% of ratio improves the classification accuracy. Finally, the future extension of the presented work has been discussed.
Index Terms
—social media analysis, security and privacy, fake profile detection, data mining and techniques, survey
Cite: Nitika Kadam and Sanjeev Kumar Sharma, "Social Media Fake Profile Detection Using Data Mining Technique," Journal of Advances in Information Technology, Vol. 13, No. 5, pp. 518-523, October 2022.
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
14-JAIT-3301-Final-India
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