Understanding Human Behavior on Social Media through Machine Learning: A Literature-Guided Approach

Abstract

Social media platforms like Facebook and YouTube have become a daily interaction partner of humans for opinion sharing, entertainment, and behavioral expression. With the vast increase of human interaction data in social media, machine learning (ML) uses powerful tools and techniques to predict human behavior based on their online activity. The purpose of this study is to explore how machine learning predicts human behavior on social media by reviewing recent literature. This study will highlight neural networks and support vector machines by examining their effectiveness in social media contexts. This literature review study will analyze behavioral prediction tasks, including sentiment analysis, mental health detection, and personality inference. This paper provides a complete view of current trends, challenges, and moral considerations. The findings suggest that while ML can reveal significant behavioral insights, data privacy, model bias, and interpretability remain pressing concerns.

Keywords: machine learning, human behavior, social media analysis, behavior prediction, mental health detection, artificial intelligence, psychology