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The widespread use of social networking platforms has significantly increased the risk of malicious activities, including the spread of spam, misinformation, and the creation of fake user profiles. This paper presents an analytical study on the detection of spammers and fake users on social networks using data mining and machine learning techniques. The proposed system analyzes user behavior patterns, content characteristics, and network interactions to distinguish between genuine and suspicious accounts. Key features such as posting frequency, friend-to-follower ratio, message similarity, and account activity are extracted and evaluated. Machine learning algorithms, including Decision Tree, Random Forest, and Support Vector Machine (SVM), are applied to classify users based on these behavioral attributes. The study highlights the importance of feature selection and model optimization to enhance detection accuracy. Experimental results using real-world social network datasets show that the proposed approach effectively identifies fake and spam accounts, improving platform security and user trust.
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Copyright © 2020, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: MOLLI SRINIVASA RAO, drmollisrinivasarao@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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