Summary of Predicting Country Instability Using Bayesian Deep Learning and Random Forest, by Adam Zebrowski and Haithem Afli
Predicting Country Instability Using Bayesian Deep Learning and Random Forest
by Adam Zebrowski, Haithem Afli
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research aims to develop uncertainty prediction models for countries, utilizing artificial intelligence (AI) tools like machine learning to process massive volumes of qualitative data from various sources. Specifically, the study leverages the Global Database of Activities, Voice, and Tone (GDELT Project), which records news in over 100 languages every second, to analyze political conflict. The GDELT dataset, released in 2012, is the first publicly accessible dataset on political conflict, offering a platform for computation on global events. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Country instability is a significant issue globally, impacting socio-economic growth and potentially causing negative consequences. To address this, researchers are developing uncertainty prediction models that incorporate ‘big data’ collections and global economies and social networks. The goal of the study is to investigate how analyzing more voluminous and fine-grained data can improve methodological analysis of political conflict. |
Keywords
» Artificial intelligence » Machine learning
