Conference Agenda

In this study we investigate the drivers and changes in users' posting probability on social networks via a sparse network model and change point detection framework. With the model, we examine the impact of user behaviour on the Reddit forum Wallstreetbets upon markets. Results show that changes in users' behaviour significantly predicted returns, integrated volatility, and jump volatility, even when controlling for network activity and established metrics measuring influential user impact. Including changes in behaviour of users on Reddit into models to explain the market movements, leads to adj. R^2 of up to 0.45 for return and 0.8 for jump explainability, vastly outperforming the competing models. Studies often focus upon influential users in networks, but we show that changes in behaviour of less important users explain a larger part of returns, integrated variance and jump volatility than important users do.

During highly contentious and polarized events, such as the COVID-19 Pandemic, the vast amount of information circulating online increases the risk of an infodemic. Gallotti et al. (2020) introduced the Infodemic Risk Index (IRI), a novel metric to quantify the impact of this phenomenon which assesses the user’s exposure to unreliable content based on their number of followers. However, despite its practicality, the IRI exhibits significant volatility over time, particularly in certain countries where it fluctuates sharply.

In this study, we aim to investigate the causes behind these fluctuations, identifying key factors contributing to IRI instability. We analyzed Twitter data presented in Gallotti et al. (2020) spanning February 2020 - May 2022, and measure the index volatility by calculating the standard deviation over time for a total of 50 countries. Our findings reveal two key contributors to this instability. On one hand, volatility is partially correlated with the unequal distribution of followers, indicating that countries with highly followed users experience greater fluctuations. On the other hand, drawing from the concept of the news media diet (Bertani, 2024), we measured the uncorrelated entropy to assess the diversity of media consumption. We found the tendency of a negative correlation between the average media entropy and the IRI volatility, suggesting that limited media diversity contributes to index instability. This result has been tested by considering each news media source separately, finding that news sources classified as fake or political shows the same behaviour with a higher level of significance. This emphasizes how much news media outlets have an important role in catalyzing public attention during polarized events. Finally, further analyses on the way they attract attention might be insightful in order to contain the spread of misinformation.