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Identifying geopolitical event precursors using attention-based LSTMs

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dc.contributor.authorHossain, K. S. M. Tozammelen
dc.contributor.authorHarutyunyan, Hrayren
dc.contributor.authorNing, Yueen
dc.contributor.authorKennedy, Brendanen
dc.contributor.authorRamakrishnan, Narenen
dc.contributor.authorGalstyan, Aramen
dc.date.accessioned2023-04-26T17:46:08Zen
dc.date.available2023-04-26T17:46:08Zen
dc.date.issued2022-10en
dc.description.abstractForecasting societal events such as civil unrest, mass protests, and violent conflicts is a challenging problem with several important real-world applications in planning and policy making. While traditional forecasting approaches have typically relied on historical time series for generating such forecasts, recent research has focused on using open source surrogate data for more accurate and timely forecasts. Furthermore, leveraging such data can also help to identify precursors of those events that can be used to gain insights into the generated forecasts. The key challenge is to develop a unified framework for forecasting and precursor identification that can deal with missing historical data. Other challenges include sufficient flexibility in handling different types of events and providing interpretable representations of identified precursors. Although existing methods exhibit promising performance for predictive modeling in event detection, these models do not adequately address the above challenges. Here, we propose a unified framework based on an attention-based long short-term memory (LSTM) model to simultaneously forecast events with sequential text datasets as well as identify precursors at different granularity such as documents and document excerpts. The key idea is to leverage word context in sequential and time-stamped documents such as news articles and blogs for learning a rich set of precursors. We validate the proposed framework by conducting extensive experiments with two real-world datasets-military action and violent conflicts in the Middle East and mass protests in Latin America. Our results show that overall, the proposed approach generates more accurate forecasts compared to the existing state-of-the-art methods, while at the same time producing a rich set of precursors for the forecasted events.en
dc.description.notesThis research is based upon a study supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA).en
dc.description.sponsorshipOffice of the Director of National Intelligence (ODNI); Intelligence Advanced Research Projects Activity (IARPA)en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/frai.2022.893875en
dc.identifier.eissn2624-8212en
dc.identifier.other893875en
dc.identifier.pmid36388399en
dc.identifier.urihttp://hdl.handle.net/10919/114807en
dc.identifier.volume5en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectevent forecastingen
dc.subjectevent precursorsen
dc.subjectsocial unrest modelingen
dc.subjectattention-methoden
dc.subjectdeep learningen
dc.subjectlong short-term memory (LSTM)en
dc.titleIdentifying geopolitical event precursors using attention-based LSTMsen
dc.title.serialFrontiers in Artificial Intelligenceen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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