Convergence of artificial intelligence with social media: A bibliometric & qualitative analysis

Abstract

The integration of artificial intelligence (AI) and social media has provided numerous benefits to businesses, including improved audience analysis and content optimization. However, AI has facilitated the spread of misinformation, emphasizing the importance of taking a balanced approach that considers both the technology's positive applications and its ethical risks. This paper looks at the intersection of AI and social media. The researchers use a mixed-method approach to analyze 1540 scholarly documents, combining bibliometric and systematic literature review techniques. The goal of this research is to identify the most important topics and trends, as well as potential business values and implications, in the AI Social Media domain. The first stage of the research involved a quantitative keyword co-occurrence analysis, which resulted in the identification of ten dominant themes. These include Conversational Agents & User Experience, Human Emotion and Content Recommendation & Moderation, Collective Intelligence in Emergency Management, Algorithmic Activism on social media, Deep Fakes and Fake News, Generative Artificial Intelligence, Algorithmic Bias in Content Moderation Systems, Deep Sentiment Analysis, Metaverse Technologies, and NLP & Mental Health Detection. Each identified theme is then subjected to a qualitative thematic literature review, which provides a more in-depth, context-specific understanding of the associated findings. Because of this comprehensive approach, the study provides a broad overview of the current state of AI social media, shedding light on the potential applications and far-reaching implications of this interdisciplinary nexus. The study's findings have the potential to shape strategic decision-making, policy development, and future research directions in this rapidly changing field.

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