• MR.V.LAXSHMI NARAYANA
• MS.G.SIVAGAMI
• DR.T.GEETHA

The proliferation of fake accounts on social media platforms poses significant challenges, including misinformation spread, manipulation of public opinion, and security risks. This study explores various techniques for detecting fake accounts by analyzing behavioural patterns, content authenticity, network interactions, and account metadata. Machine learning algorithms, including supervised and unsupervised models, are applied to identify distinguishing features of fraudulent profiles. The research demonstrates that combining multiple data sources and analytical methods improves detection accuracy, thereby enhancing platform integrity and user trust. The findings contribute to the development of more robust systems for real-time fake account identification and prevention. Machine learning algorithms, including supervised and unsupervised models, are applied to identify distinguishing features of fraudulent profiles. Features such as account age, follower-to-following ratio, posting frequency, and sentiment analysis are utilized to differentiate between genuine and fake users. The research demonstrates that combining multiple data sources and analytical methods significantly improves detection accuracy. The system is implemented using Python and integrated with tools like Pandas, Scikit-learn, Flask, and Matplotlib. It includes a web-based interface for user interaction, allowing real-time predictions on uploaded datasets.

DATA REPRESENTATION,DATA PRE PROCESSING,DATA SPLIT,COLLECTION OF TWEETS,FILE DESIGN,INPUT DESIGN

"DETECTING FAKE ACCOUNTS ON SOCIAL MEDIA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.j295-j300, June-2025, Available :http://www.jetir.org/papers/JETIR2506934.pdf

"DETECTING FAKE ACCOUNTS ON SOCIAL MEDIA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. ppj295-j300, June-2025, Available at : http://www.jetir.org/papers/JETIR2506934.pdf