# Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report

arXiv: https://arxiv.org/abs/2108.07865 · HTML: https://mlsanity.com/paper/2108.07865/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 56 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading](https://mlsanity.com/paper/2105.12825/) (2021)
- [Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis?](https://mlsanity.com/paper/2005.13213/) (2020)
- [On the Ethical Limits of Natural Language Processing on Legal Text](https://mlsanity.com/paper/2105.02751/) (2021)
- [Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence](https://mlsanity.com/paper/2105.12936/) (2021)
- [Overview of CLEF 2019 Lab ProtestNews: Extracting Protests from News in a Cross-context Setting](https://mlsanity.com/paper/2008.00345/) (2020)
- [Regressing Location on Text for Probabilistic Geocoding](https://mlsanity.com/paper/2107.00080/) (2021)
- [Few-Shot Upsampling for Protest Size Detection](https://mlsanity.com/paper/2105.11260/) (2021)
- [You are right. I am ALARMED -- But by Climate Change Counter Movement](https://mlsanity.com/paper/2004.14907/) (2020)
- [ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument Aggregation](https://mlsanity.com/paper/2106.10862/) (2021)

## Built on by (most cited first, 9 of 9)

- [The Causal News Corpus: Annotating Causal Relations in Event Sentences from News](https://mlsanity.com/paper/2204.11714/) (2022)
- [Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022): Workshop and Shared Task Report](https://mlsanity.com/paper/2211.11359/) (2022)
- [Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022](https://mlsanity.com/paper/2211.12154/) (2022)
- [1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selector](https://mlsanity.com/paper/2210.17157/) (2022)
- [1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Classification of Socio-Political Event Data](https://mlsanity.com/paper/2211.02729/) (2022)
- [Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification](https://mlsanity.com/paper/2308.07876/) (2023)
- [When Better Codebooks Are Not Enough: Predictive Performance and Behavioral Reliability in LLM Political Event Coding](https://mlsanity.com/paper/2606.06781/) (2026)
- [Machines & Influence: An Information Systems Lens](https://mlsanity.com/paper/2111.13365/) (2021)
- [Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2023): Workshop and Shared Task Report](https://mlsanity.com/paper/2312.01244/) (2023)

## Abstract (arXiv)

This workshop is the fourth issue of a series of workshops on automatic extraction of socio-political events from news, organized by the Emerging Market Welfare Project, with the support of the Joint Research Centre of the European Commission and with contributions from many other prominent scholars in this field. The purpose of this series of workshops is to foster research and development of reliable, valid, robust, and practical solutions for automatically detecting descriptions of socio-political events, such as protests, riots, wars and armed conflicts, in text streams. This year workshop contributors make use of the state-of-the-art NLP technologies, such as Deep Learning, Word Embeddings and Transformers and cover a wide range of topics from text classification to news bias detection. Around 40 teams have registered and 15 teams contributed to three tasks that are i) multilingual protest news detection, ii) fine-grained classification of socio-political events, and iii) discovering Black Lives Matter protest events. The workshop also highlights two keynote and four invited talks about various aspects of creating event data sets and multi- and cross-lingual machine learning in few- and zero-shot settings.