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Title:
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Detection of Fake Engagement and Automated Bots in Social Media Marketing Using AI
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Author:
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Vivek, Dr Vinit Kumar Lohan, Dr Deepak Goyal and Dr Bijender Bansal
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Keywords:
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Fake Engagement, Social Media Bots, AI, Machine Learning, Deep Learning, NLP, Bot Detection, Social Media Marketing.
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Abstract:
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The rapid growth of social media platforms has changed the boundaries of internet advertising. Online engagement tools like likes, comments, shares and follows enable companies and organizations to conduct one-on-one interactions with their target market. But the spread of bogus engagement methods and social media bots has taken a major toll on the credibility, validity and honesty of social media statistics. Fake engagement activities distort the popularity numbers and hence adversely influence marketing choices, brand reputation and trust of the platform. Therefore, identifying bots and fraudulent participation has become an important research problem in artificial intelligence and cybersecurity. In this work, a hybrid AI-based detection system is proposed for the identification of social media bots and fake interaction using ML, DL, Behavioral analysis, content analysis, and NLP approaches. The system detects validity or suspicion of a social media activity by analyzing user behavior, interaction patterns, textual content, and network links. Machine learning techniques such as LSTM networks, Decision Tree, Random Forest, SVM and Logistic Regression were proposed and tested. Experimental findings demonstrated that the suggested hybrid AI model obtained superior results compared to the standard approaches with 98.7% accuracy, good recall, F1-score and low false detection rates. The suggested framework offers a reliable, practical and scalable way to enhance openness and trustworthiness when used to social media marketing.
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