Modeling Sentiment Spread in Reddit's Financial Communities Through Epidemiological and Deep Learning Approaches

We investigate how sentiment towards certain companies spreads in social media.We focus on Reddit's financial communities, particularly those dealing with Google, Apple, and Amazon.We reconstruct users' interaction networks and we track their sentiment over time using a few Deep Learning-based Zero-Shot approaches inspired by recent works.We then introduce an epidemiological-like agent-based model (ABM) to describe sentiment propagation within these networks.Specifically, we employ a Susceptible-Infectious-Susceptible (SIS) framework, where users expressing negative sentiment are modeled as the "infectious" agents.We also introduce trust dynamics which affects the transition probabilities between susceptible and infectious states.We calibrate the model against empirical Reddit data to align with observed sentiment trends.Finally, we validate the model using short-term out-of-sample data, demonstrating its robustness and predictive capability in forecasting sentiment.