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Stream/Area:
-
Subject:
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Paper ID:
10.55041/IJSREM.RACCES037
Volume:
10 |
4 |
April 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science and Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Department of Computer Science and Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Department of Computer Science and Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Department of Computer Science and Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Department of Computer Science and Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Online social networks have become an important platform for communication and information sharing. However, the rapid growth of these platforms has also led to the creation of fake profiles that spread spam, misinformation, and malicious content. Detecting such fake accounts is essential to ensure user safety and maintain the integrity of social networks. This study focuses on identifying fake profiles in online social networks using Machine Learning (ML) and Natural Language Processing (NLP) techniques. The proposed system analyzes user profile information, activity patterns, and textual content posted by users. Machine learning algorithms are used to classify accounts as real or fake based on features such as friend connections, posting frequency, and account behavior. NLP techniques are applied to analyze the text content of posts, comments, and messages to detect suspicious patterns or spam-like language. By combining behavioral analysis with text analysis, the system improves the accuracy of fake profile detection. The results show that integrating ML and NLP methods can effectively detect fake profiles and reduce harmful activities in social networking platforms. This approach helps social media platforms enhance security, protect genuine users, and maintain a trustworthy online environment.
KEYWORDS
In conclusion, fake profile identification in online social networking is important to maintain security and trust among users. By using machine learning and natural language processing techniques, it is possible to analyze user profile information, behavior patterns, and textual content to detect suspicious accounts. The proposed system helps in accurately are to network classifying profiles as real or fake based on to that extracted features and trained models. This approach improves the reliability of social media .
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Publication Date
5/2/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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