Identifying geopolitical event precursors using attention-based LSTMs.

Area of Science:

• Computational Social Science
• Artificial Intelligence
• Predictive Analytics

Background:

• Forecasting societal events like civil unrest and conflicts is crucial for policy-making.
• Traditional methods using historical time series data have limitations.
• Recent research explores open-source surrogate data for improved forecasting.

Purpose of the Study:

• To develop a unified framework for simultaneous forecasting of societal events and identification of their precursors.
• To address challenges like missing historical data, event type flexibility, and precursor interpretability.
• To improve the accuracy and timeliness of event forecasts.

Main Methods:

• Utilized an attention-based long short-term memory (LSTM) model.
• Employed sequential text datasets (news articles, blogs) to leverage word context.
• Identified precursors at various granularities, including documents and document excerpts.

Main Results:

• The proposed framework achieved more accurate forecasts compared to existing state-of-the-art methods.
• Successfully identified a rich set of precursors for forecasted events.
• Demonstrated effectiveness on real-world datasets concerning Middle Eastern conflicts and Latin American protests.

Conclusions:

• The attention-based LSTM framework offers a robust solution for forecasting societal events and understanding their precursors.
• This approach enhances predictive modeling by integrating text data analysis.
• The findings have significant implications for planning and policy-making in areas prone to social unrest.