DETECTING FAKE PROFILES IN ONLINE WITH CLUSTERING TECHNIQUES IN SOCIAL MEDIA STREAMS
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Abstract
Leveraging clustering techniques, the study aims to enhance the identification of fraudulent activities. The proposed approach addresses the challenges of security and efficiency, offering a robust solution for tracking and mitigating the impact of fake profiles on social media. This research investigates the usability and challenges associated with Wiki blog content management systems, emphasizing natural language interfaces and sentiment analysis. Employing a Systematic Mapping Study, the study identifies key trends, challenges, and applied HumanComputer Interaction techniques in Wiki blog content management usability. It introduces Xatkit, a model-driven development framework, to address challenges and provide a higher level of abstraction in Wiki blog content management definition. The research explores sentiment analysis, proposing cross-domain sentiment classification, sentiment-sensitive thesaurus creation, and the optimization of sentiment classification using genetic algorithms. Results indicate the effectiveness of deep learning-based systems in handling emotional requests on social media. The study concludes with an overview of primary studies, highlighting the growing interest in Wiki blog content management usability. Despite advancements, the research underscores the need for further exploration and universally applicable guidelines in Wiki blog content management usability.
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