Social media is instantaneous information exchange whereby news, opinions, and rumors can easily spread and immediately affect investor behavior and market patterns. Therefore, research in the impact of social media on the stock market volatility, such as those based on Twitter, Reddit, and Facebook, is indeed much needed. This literature review presented the in-depth study of social media activity and stock market volatility, emphasizing the role of sentiment analysis and predictive modeling in financial markets. This paper intends to complete the crucial research lacuna by investigating different social media platforms other than Twitter and Reddit, especially during big global events like the COVID-19 pandemic. The methodological approach will be a combination of a systematized literature review and selection based on comprehensive keyword searches in Google Scholar, Research Gate, and Web of Science. According to the analysis, it would appear that models using Naïve Bayes, XGBoost, and Random Forests, combined with sentiment analysis such as VADER and GARCH, can yield rather good predictions concerning short-term movements in the stock market. However, many of the studies are burdened with a number of limitations, including the short period of data collected and their reliance on a single social media platform. By weighing both the advantages and disadvantages of social media, we contribute to insights of more accurate predictive models and point out further directions of research-most of all regarding refined techniques of sentiment analysis and nonlinear effects of social media on the market dynamics. Also, the need for multi-platform and cross-cultural studies should be emphasized.Social media is instantaneous information exchange whereby news, opinions, and rumors can easily spread and immediately affect investor behavior and market patterns. Therefore, research in the impact of social media on the stock market volatility, such as those based on Twitter, Reddit, and Facebook, is indeed much needed. This literature review presented the in-depth study of social media activity and stock market volatility, emphasizing the role of sentiment analysis and predictive modeling in financial markets. This paper intends to complete the crucial research lacuna by investigating different social media platforms other than Twitter and Reddit, especially during big global events like the COVID-19 pandemic. The methodological approach will be a combination of a systematized literature review and selection based on comprehensive keyword searches in Google Scholar, Research Gate, and Web of Science. According to the analysis, it would appear that models using Naïve Bayes, XGBoost, and Random Forests, combined with sentiment analysis such as VADER and GARCH, can yield rather good predictions concerning short-term movements in the stock market. However, many of the studies are burdened with a number of limitations, including the short period of data collected and their reliance on a single social media platform. By weighing both the advantages and disadvantages of social media, we contribute to insights of more accurate predictive models and point out further directions of research-most of all regarding refined techniques of sentiment analysis and nonlinear effects of social media on the market dynamics. Also, the need for multi-platform and cross-cultural studies should be emphasized.