Detection of Fake Profile on Social Media
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
NHCE
Abstract
In Python, fake profile detection entails detecting and flagging profiles on social media or
other platforms that display questionable or fraudulent behavior. These personas may
impersonate others, send spam, distribute misinformation, or participate in fraud.
Machine learning methods, natural language processing (NLP) for text analysis, image
recognition for profile photos, network analysis, and anomaly detection are techniques used to
detect bogus profiles.
Python offers a range of libraries and tools to implement fake profile detection, including:
Scikit-learn: Implementing machine learning models like decision trees, random forests, or SVMs.
Natural Language Toolkit (NLTK): Useful for text and sentiment analysis.
TensorFlow or PyTorch: Ideal for deep learning-based approaches like neural networks.
OpenCV: Useful for image processing and analysis.
NetworkX: For analyzing the connections and networks between profiles.
The process typically involves collecting data from profiles, extracting relevant features
(text content, posting frequency, connection patterns), and training a model to distinguish
between genuine and fake profiles.