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ABSTRACT
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Methodology: To address this issue, a robust methodology was employed, combining data preprocessing, feature extraction, and machine learning classification techniques. Twitter user data, including account creation details, posting patterns, and engagement metrics, were collected for analysis.
Data Preprocessing: so The initial phase involved meticulous data preprocessing to clean and organize the collected data. This step aimed to enhance the quality of the dataset, ensuring accurate and reliable information for subsequent analysis.
Feature Extraction: so Utilizing advanced feature extraction techniques, relevant characteristics were identified to distinguish between genuine and fake Twitter identities.
Machine Learning Classification: The heart of the research involved training machine learning models to discern patterns indicative of fake identities. Supervised learning algorithms, including Support Vector Machines and Random Forests, were employed to classify Twitter accounts based on the extracted features. Thus The models were fine-tuned using cross-validation to optimize performance.
Results and Evaluation: The models demonstrated high accuracy in detecting fake identities on Twitter, showcasing the efficacy of the proposed methodology.
Conclusion: Thus This research underscores the potential of machine learning models in mitigating the prevalence of fake identities on Twitter. By implementing a systematic approach to data analysis and leveraging advanced classification techniques, the study contributes to enhancing the overall security and trustworthiness of online social platforms.
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