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handle: 10261/361464
AbstractThis article provides a structured description of openly available news topics and forecasts for armed conflict at the national and grid cell level starting January 2010. The news topics, as well as the forecasts, are updated monthly at conflictforecast.org and provide coverage for more than 170 countries and about 65,000 grid cells of size 55 × 55 km worldwide. The forecasts rely on natural language processing (NLP) and machine learning techniques to leverage a large corpus of newspaper text for predicting sudden onsets of violence in peaceful countries. Our goals are a) to support conflict prevention efforts by making our risk forecasts available to practitioners and research teams worldwide, b) to facilitate additional research that can utilize risk forecasts for causal identification, and c) to provide an overview of the news landscape.
Topic Models, Conflict, Topic models, conflict, civil war, 4610 Library and Information Studies, forecasting, Information technology, Machine Learning, topic models, 46 Information and Computing Sciences, JF20-2112, Machine learning, News topics, Civil War, Machine Learning and Artificial Intelligence, 4407 Policy and Administration, news topics, 44 Human Society, Random Forest, 4609 Information Systems, T58.5-58.64, News Topics, 16 Peace, Justice and Strong Institutions, machine learning, Civil war, Networking and Information Technology R&D (NITRD), Political institutions and public administration (General), random forest, Random forest, Forecasting
Topic Models, Conflict, Topic models, conflict, civil war, 4610 Library and Information Studies, forecasting, Information technology, Machine Learning, topic models, 46 Information and Computing Sciences, JF20-2112, Machine learning, News topics, Civil War, Machine Learning and Artificial Intelligence, 4407 Policy and Administration, news topics, 44 Human Society, Random Forest, 4609 Information Systems, T58.5-58.64, News Topics, 16 Peace, Justice and Strong Institutions, machine learning, Civil war, Networking and Information Technology R&D (NITRD), Political institutions and public administration (General), random forest, Random forest, Forecasting
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| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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