@inproceedings{tanev-etal-2023-detecting,
title = "Detecting and Geocoding Battle Events from Social Media Messages on the Russo-{U}krainian War: Shared Task 2, {CASE} 2023",
author = "Tanev, Hristo and
Stefanovitch, Nicolas and
Halterman, Andrew and
Uca, Onur and
Zavarella, Vanni and
Hurriyetoglu, Ali and
De Longueville, Bertrand and
Della Rocca, Leonida",
editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
Tanev, Hristo and
Zavarella, Vanni and
Yeniterzi, Reyyan and
Y{\"o}r{\"u}k, Erdem and
Slavcheva, Milena},
booktitle = "Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.case-1.21",
pages = "160--166",
abstract = "The purpose of the shared task 2 at the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) 2023 workshop was to test the abilities of the participating models and systems to detect and geocode armed conflicts events in social media messages from Telegram channels reporting on the Russo Ukrainian war. The evaluation followed an approach which was introduced in CASE 2021 (Giorgi et al., 2021): For each system we consider the correlation of the spatio-temporal distribution of its detected events and the events identified for the same period in the ACLED (Armed Conflict Location and Event Data Project) database (Raleigh et al., 2010). We use ACLED for the ground truth, since it is a well established standard in the field of event extraction and political trend analysis, which relies on human annotators for the encoding of security events using a fine grained taxonomy. Two systems participated in this shared task, we report in this paper on both the shared task and the participating systems.",
}
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<abstract>The purpose of the shared task 2 at the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) 2023 workshop was to test the abilities of the participating models and systems to detect and geocode armed conflicts events in social media messages from Telegram channels reporting on the Russo Ukrainian war. The evaluation followed an approach which was introduced in CASE 2021 (Giorgi et al., 2021): For each system we consider the correlation of the spatio-temporal distribution of its detected events and the events identified for the same period in the ACLED (Armed Conflict Location and Event Data Project) database (Raleigh et al., 2010). We use ACLED for the ground truth, since it is a well established standard in the field of event extraction and political trend analysis, which relies on human annotators for the encoding of security events using a fine grained taxonomy. Two systems participated in this shared task, we report in this paper on both the shared task and the participating systems.</abstract>
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%0 Conference Proceedings
%T Detecting and Geocoding Battle Events from Social Media Messages on the Russo-Ukrainian War: Shared Task 2, CASE 2023
%A Tanev, Hristo
%A Stefanovitch, Nicolas
%A Halterman, Andrew
%A Uca, Onur
%A Zavarella, Vanni
%A Hurriyetoglu, Ali
%A De Longueville, Bertrand
%A Della Rocca, Leonida
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Zavarella, Vanni
%Y Yeniterzi, Reyyan
%Y Yörük, Erdem
%Y Slavcheva, Milena
%S Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F tanev-etal-2023-detecting
%X The purpose of the shared task 2 at the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) 2023 workshop was to test the abilities of the participating models and systems to detect and geocode armed conflicts events in social media messages from Telegram channels reporting on the Russo Ukrainian war. The evaluation followed an approach which was introduced in CASE 2021 (Giorgi et al., 2021): For each system we consider the correlation of the spatio-temporal distribution of its detected events and the events identified for the same period in the ACLED (Armed Conflict Location and Event Data Project) database (Raleigh et al., 2010). We use ACLED for the ground truth, since it is a well established standard in the field of event extraction and political trend analysis, which relies on human annotators for the encoding of security events using a fine grained taxonomy. Two systems participated in this shared task, we report in this paper on both the shared task and the participating systems.
%U https://aclanthology.org/2023.case-1.21
%P 160-166
Markdown (Informal)
[Detecting and Geocoding Battle Events from Social Media Messages on the Russo-Ukrainian War: Shared Task 2, CASE 2023](https://aclanthology.org/2023.case-1.21) (Tanev et al., CASE-WS 2023)
ACL
- Hristo Tanev, Nicolas Stefanovitch, Andrew Halterman, Onur Uca, Vanni Zavarella, Ali Hurriyetoglu, Bertrand De Longueville, and Leonida Della Rocca. 2023. Detecting and Geocoding Battle Events from Social Media Messages on the Russo-Ukrainian War: Shared Task 2, CASE 2023. In Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text, pages 160–166, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.