Tracking COVID-19 protest events in the United States. Shared Task 2: Event Database Replication, CASE 2022

Vanni Zavarella, Hristo Tanev, Ali Hürriyetoğlu, Peratham Wiriyathammabhum, Bertrand De Longueville


Abstract
The goal of Shared Task 2 is evaluating state-of-the-art event detection systems by comparing the spatio-temporal distribution of the events they detect with existing event databases. The task focuses on some usability requirements of event detection systems in real worldscenarios. Namely, it aims to measure the ability of such a system to: (i) detect socio-political event mentions in news and social media, (ii) properly find their geographical locations, (iii) de-duplicate reports extracted from multiple sources referring to the same actual event. Building an annotated corpus for training and evaluating jointly these sub-tasks is highly time consuming. One possible way to indirectly evaluate a system’s output without an annotated corpus available is to measure its correlation with human-curated event data sets. In the last three years, the COVID-19 pandemic became motivation for restrictions and anti-pandemic measures on a world scale. This has triggered a wave of reactions and citizen actions in many countries. Shared Task 2 challenges participants to identify COVID-19 related protest actions from large unstructureddata sources both from mainstream and social media. We assess each system’s ability to model the evolution of protest events both temporally and spatially by using a number of correlation metrics with respect to a comprehensive and validated data set of COVID-related protest events (Raleigh et al., 2010).
Anthology ID:
2022.case-1.29
Volume:
Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Ali Hürriyetoğlu, Hristo Tanev, Vanni Zavarella, Erdem Yörük
Venue:
CASE
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
209–216
Language:
URL:
https://aclanthology.org/2022.case-1.29
DOI:
10.18653/v1/2022.case-1.29
Bibkey:
Cite (ACL):
Vanni Zavarella, Hristo Tanev, Ali Hürriyetoğlu, Peratham Wiriyathammabhum, and Bertrand De Longueville. 2022. Tracking COVID-19 protest events in the United States. Shared Task 2: Event Database Replication, CASE 2022. In Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE), pages 209–216, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
Tracking COVID-19 protest events in the United States. Shared Task 2: Event Database Replication, CASE 2022 (Zavarella et al., CASE 2022)
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PDF:
https://aclanthology.org/2022.case-1.29.pdf
Video:
 https://aclanthology.org/2022.case-1.29.mp4