Exploring the Influence of Social Media Sentiment on Stock Market Prices and Investor Decision Making

Abstract

The rapid expansion of social media platforms has transformed the way information and opinions are generated and disseminated, raising important questions about their influence on financial markets. This study empirically investigates the relationship between social media sentiment and stock market behavior by integrating sentiment analysis with traditional financial indicators. Using a mixed-methods experimental framework, sentiment scores extracted from social media content are synchronized with stock returns, trading volume, and volatility measures. The results demonstrate a statistically significant association between sentiment fluctuations and market dynamics, with positive sentiment linked to higher returns and negative sentiment associated with increased volatility and abnormal trading activity.Advanced econometric and deep learning models further reveal that sentiment variables enhance predictive accuracy by capturing nonlinear patterns and temporal dependencies that are often overlooked by conventional models. Visual and tabular analyses confirm that sentiment effects are more pronounced during high-information and high-volatility periods, indicating the role of social media as a rapid conduit for investor psychology. The findings suggest that incorporating sentiment-based indicators can improve short-term market forecasting and provide deeper insights into investor behavior. Overall, the study highlights the growing importance of social media sentiment as an alternative data source and underscores its practical relevance for investors, analysts, and financial institutions seeking to improve decision-making in increasingly complex and information-rich markets.