scoping review

This research conducts a scoping review on the application of machine learning (ML) in social media marketing, an increasingly pivotal area in digital marketing strategies. Machine learning plays a crucial role in analyzing user data, identifying consumer behavior, personalizing content, and optimizing marketing campaigns. Through a systematic search of academic journals, conference proceedings, and other relevant sources, this review identifies and synthesizes studies that explore the use of ML in social media marketing. The selected studies are analyzed to provide a comprehensive overview of ML's impact and applications within this domain. Key findings highlight the significant role of ML in enhancing personalization, improving user engagement, and driving more effective marketing strategies. However, challenges such as data privacy concerns, algorithmic biases, and the need for greater transparency are also noted. The practical implications for marketers include the importance of ethical practices in data handling, algorithm development, and consumer trust-building. Additionally, this review identifies gaps in the current literature and suggests directions for future research, offering valuable insights for both researchers and practitioners aiming to leverage machine learning in social media marketing.