The 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE @ EMNLP 2022


Proceedings of the workshop are on



Call for Papers and Shared Task Participation (CASE @ EMNLP 2022): Challenges and Applications of Automated Extraction of Socio-political Events from Text



Important Dates


September 7, 2022: Submission deadline on Softconf

July 15, 2022: Latest ARR submission deadline for ARR

October 2, 2022: Latest ARR commitment deadline 

October 9, 2022: Notification of Acceptance

October 16, 2022: Camera-ready papers due

Workshop dates: December 7-8, 2022

Location: Hybrid -> Abu Dhabi & Online

Please see below for the important dates of the shared tasks.


There are two options for submissions that are



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, local and global NGOs, and 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 (Coleman et al. 2014; Porta and Diani, 2015) 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. Black Lives Matter protests and conflicts in Syria 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.

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 (SPE) 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), deep learning (DL), 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 SPEs, such as political violence and 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 SPE information collection have neither been comparable to each other nor been of sufficient quality (Wang et al. 2016; Schrodt 2020). 

SPEs are varied and nuanced. Both the political context and the local language used may affect whether and how they are reported. Therefore, all steps of information collection (event definition, language resources, and manual or algorithmic steps) may need to be constantly updated, leading to a series of challenging questions: Do events related to minority groups are represented well? Are new types of events covered? Are the event definitions and their operationalization comparable across systems? This workshop aims to seek answers to these questions as well. Inspiring innovative technological and scientific solutions for tackling these issues and quantifying the quality of the results. 



Call for Papers

We invite contributions from researchers in computer science, NLP, ML, DL, AI, socio-political sciences, conflict analysis and forecasting, peace studies, as well as computational social science scholars involved in the collection and utilization of SPE data. Social and political scientists will be interested in reporting and discussing their approaches and observing 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 has not been a comparable effort for handling SPEs. We fill this gap. We invite work on all aspects of automated coding and analysis of SPEs and events in general from mono- or multi-lingual text sources. This includes (but is not limited to) the following topics

  • Extracting events in and beyond a sentence, event coreference resolution
  • New datasets, training data collection and annotation for event information
  • 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, discursive, and pragmatic aspects of event manifestation
  • Methodologies for development, evaluation, and analysis of event datasets
  • Applications of event databases, e.g. early warning, conflict prediction, policymaking 
  • Estimating what is missing in event datasets using internal and external information
  • Detection of new SPE types, e.g. creative protests, cyber activism, COVID19 related
  • Release of new event datasets
  • Bias and fairness of the sources and event datasets
  • Ethics, misinformation, privacy, and fairness concerns pertaining to event datasets
  • Copyright issues on event dataset creation, dissemination, and sharing

We encourage submissions of new system description papers on our available benchmarks (ProtestNews @ CLEF 2019, AESPEN @ LREC 2020, and CASE @ 2021). Please contact the organizers if you would like to access the data.



This call solicits short and long papers reporting original and unpublished research on the topics listed above. The papers should emphasize obtained results rather than intended work and should indicate clearly the state of completion of the reported results. The page limits and content structure announced at ACL ARR page ( should be followed for both short and long papers. 

Papers should be submitted on the START page of the workshop ( or on ARR page (TBA on the workshop website) in PDF format, in compliance with the ACL publication author guidelines for ACL publications 

The reviewing process will be double-blind and papers should not include the author’s names and affiliations. Each submission will be reviewed by at least three members of the program committee. The workshop proceedings will be published on ACL Anthology.


Task 1 & 2: Multilingual Protest Event Detection:

Task 1- Multilingual protest news detection: This is the same shared task organized at CASE 2021 (For more info: But this time there will be additional data and languages at the evaluation stage. Contact person: Ali Hürriyetoğlu ( Github:  

Task 2- Automatically replicating manually created event datasets: The participants of Task 1 will be invited to run the systems they will develop to tackle Task 1 on a news archive (For more info Contact person: Hristo Tanev ( Github:, please also see 



Task 3- Event causality identification: Causality is a core cognitive concept and appears in many natural language processing (NLP) works that aim to tackle inference and understanding. We are interested to study event causality in news, and therefore, introduce the Causal News Corpus. The Causal News Corpus consists of 3,559 event sentences, extracted from protest event news, that have been annotated with sequence labels on whether it contains causal relations or not. Subsequently, causal sentences are also annotated with Cause, Effect, and Signal spans. Our two subtasks (Sequence Classification and Span Detection) work on the Causal News Corpus, and we hope that accurate, automated solutions may be proposed for the detection and extraction of causal events in news. Contact person: Fiona Anting Tan ( Github: 

Please follow the workshop page for the updates or contact the contact person related to the task you are interested in.



Participants in the Shared Task are expected to submit a paper to the workshop. Submitting a paper is not mandatory for participating in the Shared Task. Papers must follow the CASE 2022 workshop submission instructions (ACL 2022 style template: T.B.D.) and will undergo regular peer review. Their acceptance will not depend on the results obtained in the shared task but on the quality of the paper. Authors of accepted papers will be informed about the evaluation results of their systems prior to the paper submission deadline (see the important dates).

Important Dates for the Shared Task:

The important dates for the tasks are

 ** Task 1 & 2:

Training data available: The training data from CASE 2021 is used.

New test data available: Sept 15, 2022

Test end: Sep 25, 2022

System Description Paper submissions due: Oct 2, 2022

Notification to authors after review: Oct 09, 2022

Camera-ready: Oct 16, 2022

** Task 3:

Training data available: Apr 15, 2022

Validation data available: Apr 15, 2022

Validation labels available: Aug 01, 2022

Test data available: Aug 01, 2022

Test start: Aug 01, 2022

Test end: extended from Aug 15 to Aug 31, 2022

System Description Paper submissions due: Sep 07, 2022

Notification to authors after review: Oct 09, 2022

Camera ready: Oct 16, 2022



Three prominent scholars have accepted our invitation as keynote speakers:

  1. J. Craig Jenkins ( is Academy Professor Emeritus of Sociology at The Ohio State University. He directed the Mershon Center for International Security Studies from 2011 to 2015 and is now senior research scientist. Jenkins is author of more than 100 referred articles and book chapters, as well as author or editor of several books including The Politics of Insurgency: The Farm Worker’s Movement of the 1960s (1986); The Politics of Social Protest: Comparative Perspectives on States and Social Movements, with Bert Klandermans (University of Minnesota Press, 1995); Identity Conflicts: Can Violence be Regulated?, with Esther Gottlieb (Transaction Publishers, 2007) and Handbook of Politics: State and Society in Global Perspective, with Kevin T. Leicht (Springer, 2010). He has received numerous awards, including the Robin M. Williams Jr. Award for Distinguished Contributions to Scholarship, Teaching and Service from the Section on Peace, War and Social Conflict of the American Sociological Association (2015), fellow of the American Association for the Advancement of Science (2009), Joan Huber Faculty Fellow (2003), chair of the Section on Committees of the American Sociological Association (1998-2000), chair of the Section on Political Sociology, ASA (1995-96), and chair of the Section on Collective Behavior and Social Movements, ASA (1994-95). He was elected to the Sociological Research Association in 1993 and was a national security fellow at the Mershon Center for International Security at Ohio State in 1988, a Mershon Center professor from 2003-06 and chair of the Sociology Department, 2006-2010. Jenkins has received numerous grants from funding agencies, including the National Science Foundation, National Endowment for Humanities and Russell Sage Foundation. In 2010-11, he received a Liev Eriksson Mobility Grant from the Norway Research Council. In 2011-12, Jenkins was a Fulbright Fellow to Norway and a visiting professor at the Peace Research Institute of Oslo (PRIO) in Oslo, Norway. In 2017, Jenkins and co-investigator Maciek Slomczynski received a $1.4 million grant from the National Science Foundation for a four-year project on “Survey Data Recycling: New Analytic Framework, Integrated Database and Tools for Cross-National Social, Behavioral and Economic Research.” Jenkins has served as deputy editor of American Sociological Review (1986-1989), and on the editorial boards of Journal of Political and Military Sociology, International Studies Quarterly, Sociological Forum, and Sociological Quarterly.


  2. Scott Althaus ( is Merriam Professor of Political Science, Professor of Communication, and Director of the Cline Center for Advanced Social Research at the University of Illinois Urbana-Champaign. He also has faculty appointments with the School of Information Sciences and the National Center for Supercomputing Applications. His work with the Cline Center applies text analytics methods and Artificial Intelligence algorithms to extract insights from millions of news stories in ways that produce new forms of knowledge that advance societal well-being around the world. His own research interests explore the communication processes that support political accountability in democratic societies and that empower political discontent in non-democratic societies. His interests focus on four areas of inquiry: (1) how journalists construct news coverage about public affairs, (2) how leaders attempt to shape news coverage for political advantage, (3) how citizens use news coverage for making sense of public affairs, and (4) how the opinions of citizens are communicated back to leaders. He has particular interests in popular support for war, data science methods for extreme-scale analysis of news coverage, cross-national comparative research on political communication, the psychology of information processing, and communication concepts in democratic theory. His current projects include using data mining methods to help journalists cover terrorist attacks in responsible ways, a solo-authored book manuscript to be published by Cambridge University Press about the dynamics of popular support for war in the United States, and a co-authored book manuscript (with Tamir Sheafer and Gadi Wolfsfeld) in press with Oxford University Press on understanding the role of media in supporting governmental accountability and increasing the government’s responsiveness to citizen needs.

    Title: A total error approach to validating event data that is transparent, scalable, and practical to implement (Presented by Scott Althaus on behalf of the Cline Center for Advanced Social Research)


    Abstract: There are at least two reasonable ways to make your way toward where you want to go: looking down to carefully place one foot in front of the other, and looking up to focus on where you hope to arrive. Looking up beats looking down if there’s a particular destination in mind, and for constructing valid event data that destination usually takes the form of high-quality human judgment. Yet many approaches to generating event data on protests and acts of political violence using fully-automated systems implicitly adopt a “looking down” approach by benchmarking validity as a series of incremental improvements over prior algorithmic efforts. And even those efforts that adopt a “looking up” approach often treat human-generated gold standard data as if it was prima facie valid, without ever testing or confirming the accuracy of this assumption. It stands to reason that if we want to automatically produce valid event data that approaches the validity of human judgment, then we also need to validate the human judgment tasks that provide the point of comparison. But because of obvious difficulties in implementing such a rigorous assessment within the time and budget constraints of typical research projects, this more rigorous double-validation approach is rarely attempted.


    This presentation outlines a “looking up” approach for double-validating fully-automated event data developed by the Cline Center for Advanced Social Research at the University of Illinois Urbana-Champaign (USA), illustrates that approach with a test of the precision and recall for two widely-used event classification systems (the PETRARCH-2 coder used in Phoenix and TERRIER, as well as the BBN ACCENT coder used in W-ICEWS), and demonstrates the utility of the approach for developing fully-automated event data algorithms with levels of validity that approach the quality of human judgment.


    The first part of the talk reviews the Cline Center’s total error framework for identifying 19 types of error that can affect the validity of event data and addresses the challenge of applying a total error framework when authoritative ground truth about the actual distribution of relevant events is lacking (Althaus, Peyton, and Shalmon, 2022). We argue that carefully constructed gold standard datasets can effectively benchmark validity problems even in the absence of ground truth data about event populations. We propose that a strong validity assessment for event data should, at a minimum, possess three characteristics. First, there should be a standard describing ideal data; a gold standard that, in the best case, takes the form of ground truth. Second, there should be a direct “apples to apples” comparison of outputs from competing methods given identical input. Third, the test should use appropriate metrics for measuring agreement between the gold standard and data produced by competing approaches.


    The second part of the talk presents the results of a validation exercise meeting all three criteria that is applied to two algorithmic event data pipelines: the Python Engine for Text Resolution and Related Coding Hierarchy (PETRARCH-2) and the BBN ACCENT event coder. It then reviews a recent Cline Center project that has built a fully-automated event coder which produces dramatic improvements in validity over both PETRARCH-2 and BBN ACCENT by leveraging the total error framework and a reliance on the double-validation approach using high-quality gold standard benchmark datasets.



  3. Thien Huu Nguyen ( is an assistant professor in the Department of Computer and Information Science at the University of Oregon. He obtained his Ph.D. in natural language processing (NLP) at New York University (working with Ralph Grishman) and did a postdoc at the University of Montreal (working with Yoshua Bengio). Thien’s research areas involve information extraction, language grounding, and deep learning where he developed one of the first deep learning models for entity recognition, relation extraction, and event extraction. His current research explores multi-domain and multilingual NLP that aims to learn transferable representations to perform information extraction tasks over different domains and languages. Thien is the director of the NSF IUCRC Center for Big Learning (CBL) at the University of Oregon. His research has been supported by NSF, IARPA, Army Research Office, Adobe Research, and IBM Research.

    Title: Event Extraction in the Era of Large Language Models: Structure Induction and Multilingual Learning


    Abstract: Events such as protests, disease outbreaks, and natural disasters are prevalent in text from different languages and domains. Event Extraction (EE) is an important task of Information Extraction that aims to identify events and their structures in unstructured text. The last decade has witnessed significant progress for EE research, featuring deep learning and large language models as the state-of-the-art technologies. However, a key issue of existing EE methods involves modeling input text sequentially to solve each EE tasks separately, thus limiting the abilities to encode long text and capture various types of dependencies to improve EE performance. In this talk, I will present some of our recent efforts to address this issue where text structures are explicitly learned to realize important objects and their interactions to facilitate the predictions for EE.


    In addition, current EE research still mainly focuses on a few popular languages, e.g., English, Chinese, Arabic, and Spanish, leaving many other languages unexplored for EE. In this talk, I will also introduce our current research focus on developing evaluation benchmarks and models to extend EE systems to multiple new languages, i.e., multilingual and cross-lingual learning for EE. Finally, I will highlight some research challenges that can be studied in future work for EE.




The default time is the local Abu Dhabi time (UTC+4).


December 7, 2022

14:00-15:30 Afternoon Session 1 – Event Extraction (Session chair: Hristo Tanev)

14:00 – 14:10 Welcome & Introduction

14:10 – 14:35 EventGraph: Event Extraction as Semantic Graph Parsing
Huiling You, David Samuel, Samia Touileb and Lilja Øvrelid

14:35 – 15:00 A Hybrid Knowledge and Transformer-Based Model for Event Detection with Automatic Self-Attention Threshold, Layer and Head Selection
Thierry Desot, Orphee De Clercq and Veronique Hoste

15:00 – 15:25 Improving Zero-Shot Event Extraction via Iterative Sentence Simplification
Sneha Mehta, Huzefa Rangwala and Naren Ramakrishnan

15:30-16:00 Afternoon Coffee Break

16:00-17:30 Afternoon Session 2 – Data and enabling technologies (Milena Slavcheva)

16:00 – 16:25 Open-Vocabulary Argument Role Prediction For Event Extraction, Yizhu Jiao, Sha Li, Yiqing Xie, Ming Zhong, Heng Ji and Jiawei Han (Invited Findings of EMNLP paper)

16:25 – 16:50 A Multi-Modal Dataset for Hate Speech Detection on Social Media: Case-study of Russia-Ukraine Conflict
Surendrabikram Thapa, Aditya Shah, Farhan Ahmad Jafri, Usman Naseem and Imran Razzak

16:50 – 17:15 Cross-modal Transfer Between Vision and Language for Protest Detection  Ria Raj, Kajsa Andreasson, Tobias Norlund, Richard Johansson and Aron Lagerberg

17:15 – 17:40 CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization, Hossein Rajaby Faghihi, Bashar Alhafni, Ke Zhang, Shihao Ran, Joel Tetreault and Alejandro Jaimes (Invited Findings of EMNLP paper)

18:00-19:00 Afternoon Session 3 

Keynote: Thien Huu Nguyen: Event Extraction in the Era of Large Language Models: Structure Induction and Multilingual Learning

Poster Session (both December 7 and 8)

The same slot, which is 11:00 – 12:30 (Abu Dhabi local time), is reserved both on December 7 and December 8. Authors who could be present will present in person in Dhabi. Some of the remaining posters can be found on Underline page of the workshop. Finally, GatherTown can be accessed via the Underline page of the workshop. Workshop posters are presented in the Mezzanine Lounge and its adjacent halls.

For more details, please see “Poster Session” section below December 8.

December 8, 2022

14:00-15:30 Afternoon Session 1 – Political event information collection (Benjamin Radford)

14:00 – 14:10 Welcome and Intro

14:10 – 14:35 Hybrid Knowledge Engineering Leveraging a Robust ML Framework to Produce an Assassination Dataset
Abigail Sticha and Paul R. Brenner

14:35 – 15:00 Political Event Coding as Text-to-Text Sequence Generation
Yaoyao Dai, Benjamin Radford and Andrew Halterman

15:00 – 15:25 Zero-Shot Ranking Socio-Political Texts with Transformer Language Models to Reduce Close Reading Time
Kiymet Akdemir and Ali Hürriyetoğlu

15:30-16:00 Afternoon Coffee Break

16:00-17:30 Afternoon Session 2

16:00 – 16:05 Welcome and Intro

16:05 – 16:15 Task 1: Extended Multilingual Protest News Detection – Shared Task 1, CASE 2021 and 2022
Ali Hürriyetoğlu, Osman Mutlu, Fırat Duruşan, Onur Uca, Alaeddin Gürel, Benjamin J. Radford, Yaoyao Dai, Hansi Hettiarachchi, Niklas Stoehr, Tadashi Nomoto, Milena Slavcheva, Francielle Vargas, Aaqib Javid, Fatih Beyhan and Erdem Yörük

16:15 – 16:25 Task 2: 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 and Bertrand De Longueville

16:25 – 16:35 Task 3: Event Causality Identification with Causal News Corpus – Shared Task 3, CASE 2022
Fiona Anting Tan, Hansi Hettiarachchi, Ali Hürriyetoğlu, Tommaso Caselli, Onur Uca, Farhana Ferdousi Liza and Nelleke Oostdijk

Best of Shared tasks

16:35 – 16:50 ARC-NLP at CASE 2022 Task 1: Ensemble Learning for Multilingual Protest Event Detection
Umitcan Sahin, Oguzhan Ozcelik, Izzet Emre Kucukkaya and Cagri Toraman

16:50 – 17:05 ARGUABLY @ Causal News Corpus 2022: Contextually Augmented Language Models for Event Causality Identification
Guneet Kohli, Prabsimran Kaur and Jatin Bedi 

17:05 – 17:20 1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selector
Xingran Chen, Ge Zhang, Adam Nik, Mingyu Li and Jie Fu

17:30-18:30 Afternoon Session 3 

Keynote: Scott Althaus & Craig Jenkins – A total error approach to validating event data that is transparent, scalable, and practical to implement

Poster Session (both December 7 and 8)

The same slot, which is 11:00 – 12:30 (Abu Dhabi local time), is reserved both on December 7 and December 8. Authors who could be present will present in person in Dhabi. Some of the remaining posters can be found on Underline page of the workshop. Finally, GatherTown can be accessed via the Underline page of the workshop. Workshop posters are presented in the Mezzanine Lounge and its adjacent halls.

SNU-Causality Lab @ Causal News Corpus 2022: Detecting Causality by Data Augmentation via Part-of-Speech tagging
Juhyeon Kim, Yesong Choe and Sanghack Lee

LTRC @ Causal News Corpus 2022: Extracting and Identifying Causal Elements using Adapters
Hiranmai Sri Adibhatla and Manish Shrivastava

IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach
Sergio Gastón Burdisso, Juan Pablo Zuluaga-Gomez, Esau Villatoro-Tello, Martin Fajcik, Muskaan Singh, Pavel Smrz and Petr Motlicek

IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre- trained Autoregressive Language Model
Martin Fajcik, Muskaan Singh, Juan Pablo Zuluaga-Gomez, Esau Villatoro-Tello, Sergio Gastón Burdisso, Petr Motlicek and Pavel Smrz

NoisyAnnot@ Causal News Corpus 2022: Causality Detection using Multiple Annotation Decisions
Quynh Anh Nguyen and Arka Mitra

GGNN@Causal News Corpus 2022:Gated Graph Neural Networks for Causal Event Classification from Social-Political News Articles
Paul Trust, Rosane Minghim, Evangelos Milos and Kadusabe Provia

1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Classification of Socio-Political Event Data
Adam Nik, Ge Zhang, Xingran Chen, Mingyu Li and Jie Fu

1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam- Search-based Position Selector
Xingran Chen, Ge Zhang, Adam Nik, Mingyu Li and Jie Fu

SPOCK @ Causal News Corpus 2022: Cause-Effect-Signal Span Detection Using Span-Based and Sequence Tagging Models
Anik Saha, Alex Gittens, Jian Ni, Oktie Hassanzadeh, Bulent Yener and Kavitha Srinivas

CSECU-DSG @ Causal News Corpus 2022: Fusion of RoBERTa Transformers Variants for Causal Event Classification
Abdul Aziz, MD. Akram Hossain and Abu Nowshed Chy

ARGUABLY @ Causal News Corpus 2022: Contextually Augmented Language Models for Event Causality Identification
Guneet Kohli, Prabsimran Kaur and Jatin Bedi

ClassBases at the CASE-2022 Multilingual Protest Event Detection Task: Multilingual Protest News Detection and Automatically Replicating Manually Created Event Datasets
Peratham Wiriyathammabhum

EventGraph at CASE 2021 Task 1: A General Graph-based Approach to Protest Event
Huiling You, David Samuel, Samia Touileb and Lilja Øvrelid

NSUT-NLP at CASE 2022 Task 1: Multilingual Protest Event Detection using Transformer-based Models
Manan Suri, Krish Chopra and Adwita Arora

CamPros at CASE 2022 Task 1: Transformer-based Multilingual Protest News Detection
Neha Kumari, Mrinal Anand, Tushar Mohan, Ponnurangam Kumaraguru and Arun Balaji Buduru

ARC-NLP at CASE 2022 Task 1: Ensemble Learning for Multilingual Protest Event
Umitcan Sahin, Oguzhan Ozcelik, Izzet Emre Kucukkaya and Cagri Toraman

CEIA-NLP at CASE 2022 Task 1: Protest News Detection for Portuguese
Diogo Fernandes, Adalberto Junior, Gabriel da Mata Marques, Anderson da Silva Soares and Arlindo Rodrigues Galvao Filho

SPARTA at CASE 2021 Task 1: Evaluating Different Techniques to Improve Event Extraction
Arthur Müller and Andreas Dafnos


Organization Committee 

Ali Hürriyetoğlu (KNAW Humanities Cluster DHLab, the Netherlands)

Hristo Tanev (European Commission, Joint Research Centre (EU JRC), Italy),

Vanni Zavarella (EU JRC, Italy)

Reyyan Yeniterzi (Sabancı University, Turkey)

Erdem Yörük (KU, Turkey)

Deniz Yüret (KU, Turkey)

Osman Mutlu (KU, Turkey)

Fırat Duruşan (KU, Turkey)

Ali Safaya (KU, Turkey)

Bharathi Raja Asoka Chakravarthi (Insight SFI Centre for Data Analytics, United Kingdom)

Benjamin J. Radford (UNC Charlotte, United States)

Francielle Vargas (University of São Paulo, Brazil)

Farhana Ferdousi Liza (University of East Anglia, United Kingdom)

Milena Slavcheva (Bulgarian Academy of Sciences, Bulgaria)

Ritesh Kumar (Dr. Bhimrao Ambedkar University, India)

Daniela Cialfi (The ‘Gabriele d’Annunzio’ University, Italy)

Tiancheng Hu (ETH Zürich, Switzerland)

Niklas Stöhr (ETH Zürich, Switzerland)

Fiona Anting Tan (National University of Singapore, Singapore)

Tadashi Nomoto (National Institute of Japanese Literature, Japan)



Program Committee 

Fatih Beyhan (Sabanci University, Turkey)

Elizabeth Boschee (Information Sciences Institute, United States)

Tommaso Caselli (University of Groningen, the Netherlands)

Xingran Chen (University of Michigan – Ann Arbor, United States)

Martin Fajcik (IDIAP Research Institute, Switzerland)

Andrew Halterman (Michigan State University, United States)

Hansi Hettiarachchi (Birmingham City University, United Kingdom)

Li Zhuoqun (Chinese Academy of Sciences, China )

Pasquale Lisena (EURECOM, France)

Arka Mitra (ETH Zurich, Switzerland)

Manolito Octaviano Jr. (National University, Manila, Philippines)

Fabiana Rodrigues de Góes (University of São Paulo, Brazil)

Surendrabikram Thapa (Virginia Tech, United States)

Paul Trust (University College Cork)

Onur Uca (Mersin University, Turkey)

Yongjun Zhang (Stony Brook University, United States)

Ge Zhang (University of Michigan – Ann Arbor, United States)

Juan Pablo Zuluaga-Gomez (IDIAP Research Institute, Switzerland)




Find us on the Sociolinguistic Events Calendar:



Bhatia, S., Lau, J. H., & Baldwin, T. (2020). You are right. I am ALARMED–But by Climate Change Counter Movement. arXiv preprint arXiv:2004.14907.

Boroş, E. (2018). Neural Methods for Event Extraction. Ph.D. thesis, Université Paris-Saclay.

Chang, K. W., Prabhakaran, V., & Ordonez, V. (2019, November). Bias and fairness in natural language processing. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts.

Chen M., Zhang H., Ning Q., Li M., Ji H., Roth D. (2021). Event-centric Natural Language Understanding. Proc. The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI2021) Tutorial. URL: 

Coleman, P. T., Deutsch, M., & Marcus, E. C. (Eds.). (2014). The handbook of conflict resolution: Theory and practice. John Wiley & Sons.

Della Porta, D., & Diani, M. (Eds.). (2015). The Oxford handbook of social movements. Oxford University Press.

Hürriyetoğlu, A., Zavarella, V., Tanev, H., Yörük, E., Safaya, A., & Mutlu, O. (2020, May). Automated Extraction of Socio-political Events from News (AESPEN): Workshop and Shared Task Report. In Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020 (pp. 1-6).

Lau, J. H., & Baldwin, T. (2020, July). Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis?. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 2908-2913).

Pustejovsky, J., Castano, J. M., Ingria, R., Sauri, R., Gaizauskas, R. J., Setzer, A., … & Radev, D. R. (2003). TimeML: Robust specification of event and temporal expressions in text. New directions in question answering, 3, 28-34.

Schrodt, P. A. (2020, May). Keynote Abstract: Current Open Questions for Operational Event Data. In Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020.

Wang, W., Kennedy, R., Lazer, D., & Ramakrishnan, N. (2016). Growing pains for global monitoring of societal events. Science, 353(6307), 1502-1503.




[1], accessed on September 28, 2020.

[2], accessed on September 28, 2020.

[3], accessed on September 28, 2020.


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