Is it possible accurately classify political relations within evolving event ontologies without extensive annotations? This study investigates zero-shot learning methods that use expert knowledge from existing annotation codebook, and evaluates the performance of advanced ChatGPT (GPT-3.5/4) and a natural language inference (NLI)-based model called ZSP. ChatGPT uses codebook’s labeled summaries as prompts, whereas ZSP breaks down the classification task into context, event mode, and class disambiguation to refine task-specific hypotheses. This decomposition enhances interpretability, efficiency, and adaptability to schema changes. The experiments reveal ChatGPT’s strengths and limitations, and crucially show ZSP’s outperformance of dictionary-based methods and its competitive edge over some supervised models. These findings affirm the value of ZSP for validating event records and advancing ontology development. Our study underscores the efficacy of leveraging transfer learning and existing domain expertise to enhance research efficiency and scalability.

This study investigates the use of Natural Language Processing (NLP) methods to analyze politics, conflicts and violence in the Middle East using domain-specific pre-trained language models. We introduce Arabic text and present ConfliBERT-Arabic, a pre-trained language models that can efficiently analyze political, conflict and violence-related texts. Our technique hones a pre-trained model using a corpus of Arabic texts about regional politics and conflicts. Performance of our models is compared to baseline BERT models. Our findings show that the performance of NLP models for Middle Eastern politics and conflict analysis are enhanced by the use of domain-specific pre-trained local language models. This study offers political and conflict analysts, including policymakers, scholars, and practitioners new approaches and tools for deciphering the intricate dynamics of local politics and conflicts directly in Arabic.

Natural Language Processing (NLP) tools have been rapidly adopted in political science for the study of conflict and violence. In this paper, we present an application to analyze various lethal and non-lethal events conducted by organized criminal groups and state forces in Mexico. Based on a large corpus of news articles in Spanish and a set of high-quality annotations, the application evaluates different Machine Learning (ML) algorithms and Large Language Models (LLMs) to classify documents and individual sentences, and to identify specific behaviors related to organized criminal violence and law enforcement efforts. Our experiments support the growing evidence that BERT-like models achieve outstanding classification performance for the study of organized crime. This application amplifies the capacity of conflict scholars to provide valuable information related to important security challenges in the developing world.

Analyzing conflicts and political violence around the world is a persistent challenge in the political science and policy communities due in large part to the vast volumes of specialized text needed to monitor conflict and violence on a global scale. To help advance research in political science, we introduce ConfliBERT, a domain-specific pre-trained language model for conflict and political violence. We first gather a large domain-specific text corpus for language modeling from various sources. We then build ConfliBERT using two approaches: pre-training from scratch and continual pre-training. To evaluate ConfliBERT, we collect 12 datasets and implement 18 tasks to assess the models’ practical application in conflict research. Finally, we evaluate several versions of ConfliBERT in multiple experiments. Results consistently show that ConfliBERT outperforms BERT when analyzing political violence and conflict.

This article introduces Hadath, a supervised protocol for coding event data from text written in Arabic. Hadath contributes to recent efforts in advancing multi-language event coding using computer-based solutions. In this application, we focus on extracting event data about the conflict in Afghanistan from 2008 to 2018 using Arabic information sources. The implementation relies first on a Machine Learning algorithm to classify news stories relevant to the Afghan conflict. Then, using Hadath, we implement the Natural Language Processing component for event coding from Arabic script. The output database contains daily geo-referenced information at the district level on who did what to whom, when and where in the Afghan conflict. The data helps to identify trends in the dynamics of violence, the provision of governance, and traditional conflict resolution in Afghanistan for different actors over time and across space.