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            <title>Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020</title>
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            <namePart type="given">Ali</namePart>
            <namePart type="family">Hürriyetoğlu</namePart>
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            <namePart type="given">Erdem</namePart>
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    <abstract>This paper presents the conflict event modelling experiment, conducted at the Joint Research Centre of the European Commission, particularly focusing on the limitations of the input data. This model is under evaluation as to potentially complement the Global Conflict Risk Index (GCRI), a conflict risk model supporting the design of European Union’s conflict prevention strategies. The model aims at estimating the occurrence of material conflict events, under the assumption that an increase in material conflict events goes along with a decrease in material and verbal cooperation. It adopts a Long-Short Term Memory Cell Recurrent Neural Network on country-level actor-based event datasets that indicate potential triggers to violent conflict such as demonstrations, strikes, or elections-related violence. The observed data and the outcome of the model predictions consecutively, consolidate an early warning alarm system that signals abnormal social unrest upheavals, and appears promising as an approach towards a conflict trigger model. However, event-based systems still require overcoming certain obstacles related to the quality of the input data and the event classification method.</abstract>
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%0 Conference Proceedings
%T Conflict Event Modelling: Research Experiment and Event Data Limitations
%A Halkia, Matina
%A Ferri, Stefano
%A Papazoglou, Michail
%A Van Damme, Marie-Sophie
%A Thomakos, Dimitrios
%Y Hürriyetoğlu, Ali
%Y Yörük, Erdem
%Y Zavarella, Vanni
%Y Tanev, Hristo
%S Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020
%D 2020
%8 May
%I European Language Resources Association (ELRA)
%C Marseille, France
%@ 979-10-95546-50-4
%G English
%F halkia-etal-2020-conflict
%X This paper presents the conflict event modelling experiment, conducted at the Joint Research Centre of the European Commission, particularly focusing on the limitations of the input data. This model is under evaluation as to potentially complement the Global Conflict Risk Index (GCRI), a conflict risk model supporting the design of European Union’s conflict prevention strategies. The model aims at estimating the occurrence of material conflict events, under the assumption that an increase in material conflict events goes along with a decrease in material and verbal cooperation. It adopts a Long-Short Term Memory Cell Recurrent Neural Network on country-level actor-based event datasets that indicate potential triggers to violent conflict such as demonstrations, strikes, or elections-related violence. The observed data and the outcome of the model predictions consecutively, consolidate an early warning alarm system that signals abnormal social unrest upheavals, and appears promising as an approach towards a conflict trigger model. However, event-based systems still require overcoming certain obstacles related to the quality of the input data and the event classification method.
%U https://aclanthology.org/2020.aespen-1.8
%P 42-48

Markdown (Informal)

[Conflict Event Modelling: Research Experiment and Event Data Limitations](https://aclanthology.org/2020.aespen-1.8) (Halkia et al., AESPEN 2020)

• Conflict Event Modelling: Research Experiment and Event Data Limitations (Halkia et al., AESPEN 2020)

ACL

• Matina Halkia, Stefano Ferri, Michail Papazoglou, Marie-Sophie Van Damme, and Dimitrios Thomakos. 2020. Conflict Event Modelling: Research Experiment and Event Data Limitations. In Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020, pages 42–48, Marseille, France. European Language Resources Association (ELRA).