Social media interactions and stock market bubble

Abstract

Financial market bubbles have recently created severe issues for sustainable economic growth for all countries globally. Thus, understanding the root causes of financial bubbles is extremely urgent and crucial for economic development. This study aims to explore the relationship between social interactions and stock market bubbles and offer a new approach to understand the causes and development of financial market bubbles. Specifically using big data from online social media platforms such as Facebook and Twitter, this study proposes that social interactions have the potential to provide a paradigm for understanding variations in investor sentiment and aid in comprehending the formation of financial bubbles. Different machine learning models of natural language processing are used to extract investors' sentiments about stock markets and financial assets from their tweets and public posts on Twitter and Facebook. Given the prominence of social media in today's corporate contexts, this research gives fresh information for practitioners in financial markets to grasp new frameworks of investor behaviours. The study's findings might help investors be better informed and plan for irrational volatility in asset values in financial markets. More significantly, this research might assist government agencies in understanding new patterns of behavior among retail investors, as well as in monitoring and developing successful financial markets and preventing price bubbles