Challenges and Applications of Automated Extraction of Socio-political Events from Text ( CASE 2023): Workshop and Shared Task Report (2023)

Abstract: We provide a summary of the sixth edition of the CASE workshop that is held in the scope of RANLP 2023. The workshop consists of regular papers, three keynotes, working papers of shared task participants, and shared task overview papers. This workshop series has been bringing together all aspects of event information collection across technical and social science fields. In addition to contributing to the progress in text based event extraction, the workshop provides a space for the organization of a multimodal event information collection task.

## introduction nowadays, the unprecedented quantity of easily accessible data on social, political, and economic processes offers ground-breaking potential in guiding data-driven analysis in social and human sciences and in driving informed policy-making processes. governments, multilateral organizations, and local and global ngos present an increasing demand for high-quality information about a wide variety of events ranging from political violence, environmental catastrophes, and conflict, to international economic and health crises @xcite to prevent or resolve conflicts, provide relief for those that are afflicted, or improve the lives of and protect citizens in a variety of ways. the citizen actions against the covid measures in the period 2020-2022 and the war between russia and ukraine are only two examples where we must understand, analyze, and improve the real-life situations using such data. finally, these efforts respond to "growing public interest in up-to-date information on crowds" as well. 1 the workshop challenges and applications of automated extraction of socio-political events @xcite collect and annotate a dataset in 3 different granularity: whether a document is related to criminal activities, whether each sentence is related to lethal behaviors, and each sentence's membership to 11 predefined categories of events. following these granularities, the authors design three binary classification tasks and apply numerous non-neural and neural models to the annotated dataset; they observe good performance and provide analysis for data slices with lower performance. the experiments conducted on hyperparameters (key instance ratio n and instance supervision b) show an improvement from mil-n compared to vanilla mil and other variants by model selection. ## keynotes three scholars delivered three keynote speeches that are summarized below. ## conclusion many aspects of event information modeling and collection are reported in the scope of case 2023. hosting a shared task that is on multimodal problem and having submissions about languages other than english (e.g., bulgarian) are distinguishing aspects of this edition.