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Title:
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Review of Artificial Intelligence Approaches for Detecting Fake Engagement in Social Media Marketing
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Author:
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Vivek, Dr Vinit Kumar Lohan, Dr Bijender Bansal and Dr Deepak Goyal
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Keywords:
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AI, Machine Learning, Deep Learning, Fake Engagement, Social Media Bots, Social Media Marketing, Bot Detection.
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Abstract:
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The exponential growth of social media has revolutionized the internet advertising industry, enabling companies and consumers to connect with each other via likes, comments, shares and follows. However, the rampant phony interactions and social media bots have raised questions about the authenticity, transparency, and accuracy of online marketing statistics. Fake engagement activities are deliberately distorting popularity signals and impacting marketing decisions, brand reputation and customer perception. As a consequence, research into artificial intelligence and cybersecurity for identifying bot activity and fraudulent transactions has become more important. In this review paper, we summarise the state of the art of AI-based systems for the detection of social media bots and fraudulent interactions. In this article we review how ML, DL, NLP, behavioral analysis and network analysis have been used by different researchers to detect suspicious social media activity. Several artificial intelligence models are studied and evaluated according to their detection performance and practical application. These models include Decision Tree, Random Forest, SVM, Neural Networks and LSTM. The report also discusses current obstacles, limitations, knowledge gaps, and future objectives for research on systems for the detection of fraudulent interactions. The study emphasizes the need of hybrid AI techniques to improve the accuracy, scalability and resilience of social media bot detection systems.
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