@inproceedings{giorgi-etal-2021-discovering,
    title = "Discovering Black Lives Matter Events in the {U}nited {S}tates: Shared Task 3, {CASE} 2021",
    author = {Giorgi, Salvatore  and
      Zavarella, Vanni  and
      Tanev, Hristo  and
      Stefanovitch, Nicolas  and
      Hwang, Sy  and
      Hettiarachchi, Hansi  and
      Ranasinghe, Tharindu  and
      Kalyan, Vivek  and
      Tan, Paul  and
      Tan, Shaun  and
      Andrews, Martin  and
      Hu, Tiancheng  and
      Stoehr, Niklas  and
      Re, Francesco Ignazio  and
      Vegh, Daniel  and
      Atzenhofer, Dennis  and
      Curtis, Brenda  and
      H{\"u}rriyeto{\u{g}}lu, Ali},
    booktitle = "Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.case-1.27",
    doi = "10.18653/v1/2021.case-1.27",
    pages = "218--227",
    abstract = "Evaluating the state-of-the-art event detection systems on determining spatio-temporal distribution of the events on the ground is performed unfrequently. But, the ability to both (1) extract events {``}in the wild{''} from text and (2) properly evaluate event detection systems has potential to support a wide variety of tasks such as monitoring the activity of socio-political movements, examining media coverage and public support of these movements, and informing policy decisions. Therefore, we study performance of the best event detection systems on detecting Black Lives Matter (BLM) events from tweets and news articles. The murder of George Floyd, an unarmed Black man, at the hands of police officers received global attention throughout the second half of 2020. Protests against police violence emerged worldwide and the BLM movement, which was once mostly regulated to the United States, was now seeing activity globally. This shared task asks participants to identify BLM related events from large unstructured data sources, using systems pretrained to extract socio-political events from text. We evaluate several metrics, accessing each system{'}s ability to identify protest events both temporally and spatially. Results show that identifying daily protest counts is an easier task than classifying spatial and temporal protest trends simultaneously, with maximum performance of 0.745 and 0.210 (Pearson $r$), respectively. Additionally, all baselines and participant systems suffered from low recall, with a maximum recall of 5.08.",
}