Convergence of artificial intelligence with social media

The integration of AI and social media offers businesses enhanced audience analysis and content optimization, but also raises ethical concerns due to misinformation. This paper examines AI’s impact on social media using a mixed-method approach, analyzing 1540 scholarly documents with bibliometric and literature review techniques. Researchers identified ten dominant themes, such as Conversational Agents, Human Emotion, Deep Fakes, and Algorithmic Bias. Each theme was further analyzed qualitatively to understand its specific context. The study provides a comprehensive overview of AI in social media, highlighting its applications and implications for strategic decisions and future research.

Social media is pivotal in modern communication, influencing public opinion, fostering relationships, and enabling businesses to connect with customers. The use of AI for analytics has enhanced companies’ understanding of customer journeys, allowing personalized experiences through data-driven insights. Novel algorithms are continually developed to meet the demands of social media, improving content delivery and ad targeting. However, AI raises ethical issues, such as privacy breaches and algorithmic bias. A comprehensive review of AI and social media (AISoMe) convergence shows a need for broader analysis beyond specific applications like branding and sentiment analysis. This study employs bibliometric and qualitative analyses to identify dominant themes, their evolution, and business values from AISoMe. The study aims to uncover emerging concepts, inform policy-making, and guide future research in the AISoMe field, potentially influencing various sectors and regulatory decisions.

Previous studies have explored AI’s role in social media analytics, focusing on research trends, emerging topics, and the impact of AI. These studies often concentrate on specific aspects such as business applications, technical methods, societal effects, and ethical concerns.

Business-focused reviews highlight how AI enhances branding and marketing on social media by understanding target markets and recognizing trends. Technical reviews delve into AI algorithms like natural language processing and machine learning for analyzing social media data. Societal impact reviews examine AI’s role in identifying mental health issues and supporting humanitarian efforts. Ethical reviews discuss AI’s use in detecting misinformation, hate speech, and online harassment, alongside privacy and bias concerns.

Our study differs by offering a holistic perspective, connecting these diverse research areas. Using a bibliometric approach, we map out the relationships and patterns among various research clusters. This integrated view reveals dominant themes, emerging trends, and potential synergies, providing a comprehensive understanding of AI in social media analytics. Our qualitative analysis further enriches this perspective, bridging gaps between different research clusters for a more nuanced and interconnected understanding of the field.

This research employs bibliometric and qualitative content analysis to explore the intersection of AI and social media. Using the Scopus database, we searched for keywords related to artificial intelligence (AI, deep learning, machine learning) and various social media platforms (Facebook, Instagram, TikTok, YouTube, LinkedIn, Snapchat, WhatsApp). These terms were chosen to capture the broad and specific aspects of AI applications in social media contexts. The focus was on the Business, Management, and Accounting (BMA) field due to the significant implications of AI-driven social media insights for business strategies. The search yielded 1540 documents, which were analyzed using VOSviewer software for bibliometric network visualization.

VOSviewer helped identify and visualize keyword co-occurrences, setting a minimum occurrence threshold to highlight dominant themes. Common keywords were manually filtered out to maintain clarity. The clustering method used was Association Strength, which revealed relationships among keywords and identified key themes and emerging trends in the literature. A qualitative content analysis followed, ensuring thematic integrity by carefully categorizing papers according to their relevance despite overlapping keywords. This approach provided a comprehensive understanding of AI’s integration into social media and guided future research directions by highlighting significant themes and potential gaps in the literature.

This paper presents a mixed methods review of scientific articles exploring the relationship between social media and artificial intelligence (AI). A quantitative analysis of 1540 documents identified ten key topics using keyword co-occurrence analysis. The most prominent topic was conversational agents and user experience, followed by human emotion and content recommendation, collective intelligence in emergency management, and algorithmic activism on social media. Deep fakes and fake news were significant concerns, with generative AI, algorithmic bias, deep sentiment analysis, metaverse technologies, and NLP for mental health detection rounding out the top ten topics.

The qualitative research emphasized the role of AI in various societal contexts, such as disaster management and mental health detection. AI and NLP methods can analyze user-generated data during disasters to provide real-time insights to emergency responders. In mental health, AI techniques can detect depression indicators in social media interactions, but ethical and privacy issues need careful consideration.

AI integration with social media offers numerous business benefits, including personalized interactions, content promotion, automated customer service, and enhanced user experience. Emotional understanding through AI can improve content moderation and marketing strategies. AI’s role in crisis management allows for real-time insights and effective response strategies.

Algorithmic activism provides businesses with insights into public sentiment and social movements, fostering positive brand image and consumer trust. The paper also discusses the challenges and opportunities of AI-generated fake news and deepfakes, emphasizing the importance of ethical use. Generative AI is seen as transformative for branding, marketing, and innovation.

Explainable AI (XAI) and deep sentiment analysis enhance trust, transparency, and sentiment classification, offering businesses accurate insights. The emergence of metaverse technologies creates immersive brand engagement opportunities. Advances in NLP facilitate early detection of mental health conditions, improving healthcare outcomes and workforce productivity.