Predicting Country Instability Using Bayesian Deep Learning and Random Forest

TL;DR

This paper explores the use of Bayesian deep learning and random forests to predict country instability by analyzing large-scale, real-time global news data from the GDELT dataset, aiming to improve predictive accuracy.

Contribution

It introduces a novel combination of Bayesian deep learning and random forest models for predicting country instability using extensive social and news media data.

Findings

Enhanced prediction accuracy of country instability.

Effective integration of big data and AI techniques.

Potential for real-time instability monitoring.

Abstract

Country instability is a global issue, with unpredictably high levels of instability thwarting socio-economic growth and possibly causing a slew of negative consequences. As a result, uncertainty prediction models for a country are becoming increasingly important in the real world, and they are expanding to provide more input from 'big data' collections, as well as the interconnectedness of global economies and social networks. This has culminated in massive volumes of qualitative data from outlets like television, print, digital, and social media, necessitating the use of artificial intelligence (AI) tools like machine learning to make sense of it all and promote predictive precision [1]. The Global Database of Activities, Voice, and Tone (GDELT Project) records broadcast, print, and web news in over 100 languages every second of every day, identifying the people, locations, organisations, counts, themes, outlets, and events that propel our global community and offering a free open platform for computation on the entire world. The main goal of our research is to investigate how, when our data grows more voluminous and fine-grained, we can conduct a more complex methodological analysis of political conflict. The GDELT dataset, which was released in 2012, is the first and potentially the most technologically sophisticated publicly accessible dataset on political conflict.