CASE 2021 : Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) @ACL2021
CASE 2021 : Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) @ACL2021

CASE 2021 : Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) @ACL2021

Bangkok, Thailand
Event Date: August 05, 2021 - August 06, 2021
Submission Deadline: April 26, 2021
Notification of Acceptance: May 28, 2021
Camera Ready Version Due: June 07, 2021




Today, 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. The need for precise and high-quality information about a wide variety of events ranging from political violence, environmental catastrophes, and conflict, to international economic and health crises has rapidly escalated (Porta and Diani, 2015; Coleman et al. 2014). Governments, multilateral organizations, local and global NGOs, and social movements present an increasing demand for this data 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. For instance, Black Lives Matter protests[1] and conflict in Syria[2] events are only two examples where we must understand, analyze, and improve the real-life situations using such data.

Event extraction has long been a challenge for the natural language processing (NLP) community as it requires sophisticated methods in defining event ontologies, creating language resources, and developing algorithmic approaches (Pustojevsky et al. 2003; Boroş, 2018; Chen et al. 2021). Social and political scientists have been working to create socio-political event databases such as ACLED, EMBERS, GDELT, ICEWS, MMAD, PHOENIX, POLDEM, SPEED, TERRIER, and UCDP following similar steps for decades. These projects and the new ones increasingly rely on machine learning (ML) and NLP methods to deal better with the vast amount and variety of data in this domain (Hürriyetoğlu et al. 2020). Automation offers scholars not only the opportunity to improve existing practices, but also to vastly expand the scope of data that can be collected and studied, thus potentially opening up new research frontiers within the field of socio-political events, such as political violence & social movements. But automated approaches as well suffer from major issues like bias, generalizability, class imbalance, training data limitations, and ethical issues that have the potential to affect the results and their use drastically (Lau and Baldwin 2020; Bhatia et al. 2020; Chang et al. 2019). Moreover, the results of the automated systems for socio-political event information collection may not be comparable to each other or not of sufficient quality (Wang et al. 2016; Schrodt 2020).

Call for Papers

We invite contributions from researchers in computer science, NLP, ML, AI, socio-political sciences, conflict analysis and forecasting, peace studies, as well as computational social science scholars involved in the collection and utilization of socio-political event data. Social and political scientists will be interested in reporting and discussing their approaches and observe what the state-of-the-art text processing systems can achieve for their domain. Computational scholars will have the opportunity to illustrate the capacity of their approaches in this domain and benefit from being challenged by real-world use cases. Academic workshops specific to tackling event information in general or for analyzing text in specific domains such as health, law, finance, and biomedical sciences have significantly accelerated progress in these topics and fields, respectively. However, there is not a comparable effort for handling socio-political events. We hope to fill this gap and contribute to social and political sciences in a similar spirit. We invite work on all aspects of automated coding of socio-political events from mono- or multi-lingual text sources. This includes (but is not limited to) the following topics

  • Extracting events in and beyond a sentence
  • Training data collection and annotation processes
  • Event coreference detection
  • Event-event relations, e.g., subevents, main events, causal relations
  • Event dataset evaluation in light of reliability and validity metrics
  • Defining, populating, and facilitating event schemas and ontologies
  • Automated tools and pipelines for event collection related tasks
  • Lexical, Syntactic, and pragmatic aspects of event information manifestation
  • Development and analysis of rule-based, ML, hybrid, and human-in-the-loop approaches for creating event datasets
  • COVID-19 related socio-political events
  • Applications of event databases
  • Online social movements
  • Bias and fairness of the sources and event datasets
  • Estimating what is missing in event datasets using internal and external information
  • Novel event detection
  • Release of new event datasets
  • Ethics, misinformation, privacy, and fairness concerns pertaining to event datasets
  • Copyright issues on event dataset creation, dissemination, and sharing
  • Qualities of the event information on various online and offline platforms

Best Deals

Credits and Sources

[1] CASE 2021 : Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) @ACL2021

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