Using artificial intelligence in sn analysis: building models to understand and predict user behavior
Palabras clave:
Big data analysis, Machine learning in SNs, Prediction of user activity, Classification algorithms, Detection of behavior patternsResumen
Introduction: The study explores the use of artificial intelligence (AI) and sentiment analysis to predict personality traits and behaviors from the extensive data available on social networks. It aims to understand the dynamics of user interaction and the spread of viral content through AI-driven models.
Methods: The research applies various AI and machine learning techniques, particularly focusing on natural language processing (NLP), to analyze social media data. The methodology includes sentiment analysis to categorize text into distinct emotional responses and predictive analytics to forecast trends in user engagement and content virality.
Results: Results indicate that AI can effectively predict user behaviors and personality traits such as neuroticism, which correlates with higher aggression and more frequent, prolonged use of social media. The study identifies key patterns and trends that influence user interactions on social networks.
Discussion: The discussion centers on the implications of AI in social media analytics, addressing both the technological advancements and the ethical considerations of profiling user behavior. It emphasizes the need for robust models that can handle the complexity and variability of data in social networks.
Conclusion: The research demonstrates that AI and machine learning are invaluable tools for analyzing social networks, providing insights that can enhance user engagement strategies and content delivery. The study advocates for continued development and refinement of AI models to better understand and predict user behavior.
Citas
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