How does user-generated content on Social Media affect stock predictions? A case study on GameStop

One of the main challenges in the financial market concerns the forecasting of stock behavior, which plays a key role in supporting the financial decisions of investors. In recent years, the large amount of available financial data and the heterogeneous contextual information led researchers to investigate data-driven models using Artificial Intelligence (AI)-based approaches for forecasting stock prices. Recent methodologies focus mainly on analyzing participants from Reddit without considering other social media and how their combination affects the stock market, which remains an open challenge. In this paper, we combine financial data and textual user-generated information, which are provided as input to various deep learning models, to develop a stock forecasting system. The main novelties of the proposal concern the design of a multi-modal approach combining historical stock prices and sentiment scores extracted by different Online Social Networks (OSNs), also unveiling possible correlations about heterogeneous information evaluated during the GameStop squeeze. In particular, we have examined several AI-based models and investigated the impact of textual data inferred from well-known Online Social Networks ( i.e. , Reddit and Twitter) on stock market behavior by conducting a case study on GameStop. Although users’ dynamic opinions on social networks may have a detrimental impact on the stock prediction task, our investigation has demonstrated the usefulness of assessing user-generated content inferred from various OSNs on the market forecasting problem. • We design a framework combining social textual content and historical financial data. • We fed multimodal time series as input to deep learning models for stock forecasting. • We evaluate how OSN information can affect stock prediction during GME squeeze event. • Effectiveness of the framework has been evaluated in a real scenario, focused on GME.