Utilizing Bayesian Vector Autoregressive Model to Model and Predict the Information Cycle of Crisis Events Related to the Russia-Ukraine Conflict

During crisis events, people routinely post large amounts of information to social media.. The discussions of conflict on social media platforms dominate the interest and perception of active users. Unfortunately, social media can also be manipulated to spread disinformation during crisis events that purposefully leads the public away from the truth. This work aims to explore the information cycle during crisis events related to the Ukraine conflict and help guide stakeholders to participate in a timely manner for the public good. We use the Bayesian Vector Auto-Regressive model to investigate posting behavior among a series of crisis events that occurred during the Ukraine War. The goal of this study is to predict the time-dependent volume of the discussions for new crisis events related to this conflict on social media platforms. We also detect the change point of these events where a sharp drop or a sudden increase occurs. All this information can then be used to help policymakers decide how to react before the maximum dissemination of false information. Our results show that blogs tend to exhibit self-regulation, with a notable negative correlation between event mentions on consecutive days. This suggests a reactive pattern within the blogosphere, wherein spikes in discussions are typically followed by declines, possibly reflecting the pursuit of novelty in blog content. In contrast, news mentions demonstrate a positive momentum, indicating that increases in mentions are likely to endure across multiple days. In essence, by understanding the dynamics of information dissemination on social media during crisis events like the Ukraine conflict, officials can engage with and counteract disinformation. This, in turn, safeguards the public's access to accurate information and enhances crisis response strategies for the greater good.

Information Diffusion

Multivariate Time Series

Posterior

Russo-Ukraine War 

Statistical Data Science

Symposium on Data Science and Statistics (SDSS) 2024