The 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text
(CASE @ EACL 2024, March 21-22, 2024)
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 disasters, 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. Citizen actions against the COVID measures in the period 2020-2022 and the Russia – Ukraine war are only two examples where event-centered data can contribute to better understanding of real-life situations. 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, developing algorithmic approaches and ML models (Pustojevsky et al. 2003; Boroş, 2018; Chen et al. 2021). Previous issues of the CASE-series of workshops have featured works which use BERT and other deep learning models, syntactic parsing, semantic argument structure analysis, temporal and space reasoning, lexical learning, and other NLP methods and algorithms. Detecting and extracting information about socio political events is a complex NLP task: events can be described via elaborated syntactic and semantic language structures; event descriptions may enter into different semantic relations between each other, such as coreference, causality, inclusion, spatio-temporal proximity and others.
Events as linguistic phenomena are usually modeled through frames and ontologies and event types are often represented via elaborated taxonomies. Detecting socio-political events in real world texts poses problems, which originate from the dynamics of the activity of the governments, political parties, movements and other socially active groups. They may frequently change their leading figures, strategies and organization. These factors can make existing statistical models and knowledge bases less relevant as the time passes and require development of methods which rely on limited data, such as few-shot learning, man-in-the loop or other specific learning strategies.
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). Unfortunately automated approaches suffer from major issues like bias, limited 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 events (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.
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. 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: 1) Extracting events and their arguments in and beyond a sentence or document, event coreference resolution. 2) Research in NLP technologies, related to event detection, such as: geocoding, temporal reasoning, argument structure detection, syntactic and semantic analysis of event structures, text classification for event type detection, learning event-related lexica, co-reference in event descriptions, machine translation for multilingual event detection, named entity recognition, fake news analysis, text similarity and others with focus on real or potential event detection applications. 3) New datasets, training data collection and annotation for event information. 4) Event-event relations, e.g., subevents, main events, spatio-temporal relations, causal relations. 5) Event dataset evaluation in light of reliability and validity metrics. 6) Defining, populating, and facilitating event schemas and ontologies. 7) Automated tools and pipelines for event collection related tasks. 8) Lexical, syntactic, semantic, discursive, and pragmatic aspects of event manifestation. 9) Methodologies for development, evaluation, and analysis of event datasets. 10) Applications of event databases, e.g. early warning, conflict prediction, policymaking. 11) Estimating what is missing in event datasets using internal and external information. 12) Detection of new and emerging SPE types, e.g. creative protests, 13) Release of new event datasets, 14) Bias and fairness of the sources and event datasets. 15) Ethics, misinformation, privacy, and fairness concerns pertaining to event datasets. 16) Copyright issues on event dataset creation, dissemination, and sharing. 17) Cross-lingual, multilingual and multimodal aspects in event analysis, 18- Climate change and conflict-related resources and approaches related to contentious politics around climate change. Moreover, we will encourage submissions of new system description papers on our available benchmarks.
[1] https://sites.google.com/view/crowdcountingconsortium/faqs
[2] https://acleddata.com/political-violence-targeting-women/#curated
REFERENCES
Bhatia, S., Lau, J. H., & Baldwin, T. (2020). You are right. I am ALARMED–But by Climate Change Counter Movement.
Boroş, E. (2018). Neural Methods for Event Extraction.
Chang, K. W., Prabhakaran, V., & Ordonez, V. (2019, November). Bias and fairness in natural language processing.
Chen M., Zhang H., Ning Q., Li M., Ji H., Roth D. (2021). Event-centric Natural Language Understanding.
Coleman, P. T., Deutsch, M., & Marcus, E. C. (Eds.). (2014). The handbook of conflict resolution: Theory and practice.
Della Porta, D., & Diani, M. (Eds.). (2015). The Oxford handbook of social movements.
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.
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?.
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.
Schrodt, P. A. (2020, May). Keynote Abstract: Current Open Questions for Operational Event Data.
Wang, W., Kennedy, R., Lazer, D., & Ramakrishnan, N. (2016). Growing pains for global monitoring of societal events.
⇒ Paper submission deadline: 18 December 2023
⇒ Paper acceptance notification: 20 January 2024
⇒ Paper camera-ready: 30 January 2024
⇒ Workshop dates: 21-22 March 2024
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
Submission Format
CASE 2023 will solicit 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 results.Submission
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 (https://aclrollingreview.org/cfp) should be followed for both short and long papers.
Papers should be submitted on the START page of the workshop (https://softconf.com/eacl2024/
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.
Climate Activism Stance and Hate Event Detection Shared Task at CASE 2024
Task Description: Hate speech detection and stance detection are some of the most important aspects of event identification during climate change activism events. In the case of hate speech detection, the event is the occurrence of hate speech, the entity is the target of the hate speech, and the relationship is the connection between the two. The hate speech event has targets to which hate is directed. Identification of targets is an important task within hate speech event detection. Additionally, stance event detection is an important part of assessing the dynamics of protests and activisms for climate change. This helps to understand whether the activist movements and protests are being supported or opposed. This task will have three subtasks (i) Hate speech identification (ii) Targets of Hate Speech Identification (iii) Stance Detection.
Codalab Link: https://codalab.lisn.upsaclay.fr/competitions/16206
Registration: In order to register for the shared task, please send a request in codalab. The organizers will approve requests on a daily basis.
GitHub Page: https://github.com/therealthapa/case2024-climate
Previous shared tasks for working on regular papers (no official competition)
PT1: MULTILINGUAL PROTEST NEWS DETECTION
The performance of an automated system depends on the target event type as it may be broad or potentially the event trigger(s) can be ambiguous. The context of the trigger occurrence may need to be handled as well. For instance, the ‘protest’ event type may be synonymous with ‘demonstration’ or not in a specific context. Moreover, the hypothetical cases such as future protest plans may need to be excluded from the results. Finally, the relevance of a protest depends on the actors as in a contentious political event only citizen-led events are in the scope. This challenge becomes even harder in a cross-lingual and zero-shot setting in case training data are not available in new languages. We tackle the task in four steps and hope state-of-the-art approaches will yield optimal results.
Contact person: Ali Hürriyetoğlu (ali.hurriyetoglu@gmail.com)
Github: https://github.com/emerging-
PT2: 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 in studying event causality in the news and, therefore, introduce the Causal News Corpus. The Causal News Corpus consists of 3,767 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 subtasks 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 (tan.f@u.nus.edu)
PT3: MULTIMODAL HATE SPEECH EVENT DETECTION
Hate speech detection is one of the most important aspects of event identification during political events like invasions. In the case of hate speech detection, the event is the occurrence of hate speech, the entity is the target of the hate speech, and the relationship is the connection between the two. Since multimodal content is widely prevalent across the internet, the detection of hate speech in text-embedded images is very important. Given a text-embedded image, this task aims to automatically identify the hate speech and its targets. This task will have two subtasks.
Contact person: Surendrabikram Thapa (surendrabikram@vt.edu)
Github: https://github.com/
Please check the respective sections of the workshops on https://emw.ku.edu.tr/workshops/.
TBA
TBA
ORGANIZATION COMMITTEE
Ali Hürriyetoğlu is a computational linguist who performs research on and develops systems for SPE information collection from textual sources.
Hristo Tanev is a researcher in the area of Event Detection, Information Extraction, Lexical Learning, Fake News Detection and Social Media Mining.
Erdem Yörük is an Associate Professor in the Department of Sociology at KU and an Associate Member in the Department of Social Policy and Intervention at University of Oxford. His work focuses on social welfare and social policy, social movements, political sociology, and comparative and historical sociology.
PROGRAM COMMITTEE
T.B.A.
COLLABORATORS & CONTRIBUTORS
Moreover, the following collaborators have expressed their support and will be contributing to both organization and program committees (without any particular order):
Onur Uca is a researcher in computational social science, text mining and information extraction, pragmatic sociology. Surendrabikram Thapa is a researcher in computational linguistics, social media analysis, NLP in low-resource language, and healthcare analytics.
| Andrew Halterman | Michigan State University |
| Giuseppe Tirone | European Commission, Joint Research Centre |
| Osman Mutlu | Koc University |
| Tadashi Nomoto | National Institute of Japanese Literature |
| Hristo Tanev | European Commission, Joint Research Centre |
| Onur Uca | Mersin University |
| Peratham Wiriyathammabhum | – |
| Marijn Schraagen | Utrecht University |
| Gaurav Singh | S&P Global |
| Fiona Anting Tan | University of Singapore |
| Surendrabikram Thapa | Virginia Tech |
| Alexandra DeLucia | Johns Hopkins University |
| Kumari Neha | Indraprastha Institute of Information Technology Delhi |
| Maria Eskevich | Huygens Institute |
| Guanqun Yang | Stevens Institute of Technology |
| Cagri Toraman | Aselsan, Turkey |
| Debanjana Kar | IBM |
| Man Luo | Arizona State University |
| Nelleke Oostdijk | Radboud University |
| Hansi Hettiarachchi | Birmingham City University |