Diplomatic events consistently prompt widespread public discussion and debate. Public sentiment plays a critical role in diplomacy, as a good sentiment provides vital support for policy implementation, helps resolve international issues, and shapes a nation's international image. Traditional methods for gauging public sentiment, such as large-scale surveys or manual content analysis of media, are typically time-consuming, labor-intensive, and lack the capacity for forward-looking analysis. We propose a novel framework that identifies specific modifications for diplomatic event narratives to shift public sentiment from negative to neutral or positive. First, we train a language model to predict public reaction towards diplomatic events. To this end, we construct a dataset comprising descriptions of diplomatic events and their associated public discussions. Second, guided by communication theories and in collaboration with domain experts, we predetermined several textual features for modification, ensuring that any alterations changed the event's narrative framing while preserving its core facts.We develop a counterfactual generation algorithm that employs a large language model to systematically produce modified versions of an original text. The results show that this framework successfully shifted public sentiment to a more favorable state with a 70\% success rate. This framework can therefore serve as a practical tool for diplomats, policymakers, and communication specialists, offering data-driven insights on how to frame diplomatic initiatives or report on events to foster a more desirable public sentiment.

After more than a year of Israeli bombing, tens of thousands of Palestinian deaths, and a humanitarian catastrophe in Gaza, the world was largely united in saying "enough is enough". United Nations General Assembly (UNGA) resolution 12667 in December was clear in its demand: An immediate ceasefire in Gaza. Countries as diverse as Vietnam, Zimbabwe and Colombia echoed that call. And yet, bucking that consensus were nine "no" votes – chief among them, as is typical when it comes to resolutions calling for Israel to adhere to international law or human rights, was the United States. The US has provided unwavering support to Israel throughout its war on Gaza, even as Israel faces accusations of genocide at the International Court of Justice (ICJ) and its prime minister has an International Criminal Court (ICC) arrest warrant to his name.

Predicting future international events from textual information, such as news articles, has tremendous potential for applications in global policy, strategic decision-making, and geopolitics. However, existing datasets available for this task are often limited in quality, hindering the progress of related research. In this paper, we introduce WORLDREP (WORLD Relationship and Event Prediction), a novel dataset designed to address these limitations by leveraging the advanced reasoning capabilities of large-language models (LLMs). Our dataset features high-quality scoring labels generated through advanced prompt modeling and rigorously validated by domain experts in political science. We showcase the quality and utility of WORLDREP for real-world event prediction tasks, demonstrating its effectiveness through extensive experiments and analysis. Furthermore, we publicly release our dataset along with the full automation source code for data collection, labeling, and benchmarking, aiming to support and advance research in text-based event prediction.

While most prior research into the universe of political discourses is based in the genres of debate and speeches, studies of academic political discourse have been sparse. One of the goals of the project SKILL, from which this paper stems, is to fill this gap. SKILL - A social science lab for research-based learning - is dedicated to building and applying AI technologies to facilitate analysis of argumentation in scholarly articles in political science, especially in the context of teaching International Relations (IR). The ultimate goal of SKILL is to provide students with AI tools which would facilitate comprehension of original articles used as part of teaching syllabi and which would coach them in producing expert argumentation in the field. In order to gain insight into the structure and properties of arguments in the domain of political science theory, we developed an annotation scheme which enables analysis of scholarly IR discourse in terms of interaction between argumentation and types of domain content contributing to arguments. The scheme comprises two orthogonal dimensions: discourse and content domain.

On October 7, within hours of the armed group Hamas launching a surprise attack on Israel, social media platforms were rife with support for Israel – and also fake news. What stood out in the clamour was the fact that a fair amount of it was produced and distributed by accounts from India. In the days after Israel declared war on Hamas, a blue-tick-verified handle posted a video on the social media platform X of a Pakistani parliamentarian threatening to obliterate Israel with an "atom bomb" if it did not end its atrocities against Muslims. That video received more than 840,000 views. But it was from 2021 and is not related to the current war.

Measuring the intensity of events is crucial for monitoring and tracking armed conflict. Advances in automated event extraction have yielded massive data sets of "who did what to whom" micro-records that enable data-driven approaches to monitoring conflict. The Goldstein scale is a widely-used expert-based measure that scores events on a conflictual-cooperative scale. It is based only on the action category ("what") and disregards the subject ("who") and object ("to whom") of an event, as well as contextual information, like associated casualty count, that should contribute to the perception of an event's "intensity". This paper takes a latent variable-based approach to measuring conflict intensity. We introduce a probabilistic generative model that assumes each observed event is associated with a latent intensity class. A novel aspect of this model is that it imposes an ordering on the classes, such that higher-valued classes denote higher levels of intensity. The ordinal nature of the latent variable is induced from naturally ordered aspects of the data (e.g., casualty counts) where higher values naturally indicate higher intensity. We evaluate the proposed model both intrinsically and extrinsically, showing that it obtains comparatively good held-out predictive performance.

Many dynamical systems in the real world are naturally described by latent states with intrinsic orderings, such as "ally", "neutral", and "enemy" relationships in international relations. These latent states manifest through countries' cooperative versus conflictual interactions over time. State-space models (SSMs) explicitly relate the dynamics of observed measurements to transitions in latent states. For discrete data, SSMs commonly do so through a state-to-action emission matrix and a state-to-state transition matrix. This paper introduces the Ordered Matrix Dirichlet (OMD) as a prior distribution over ordered stochastic matrices wherein the discrete distribution in the kth row stochastically dominates the (k+1)th, such that probability mass is shifted to the right when moving down rows. We illustrate the OMD prior within two SSMs: a hidden Markov model, and a novel dynamic Poisson Tucker decomposition model tailored to international relations data. We find that models built on the OMD recover interpretable ordered latent structure without forfeiting predictive performance. We suggest future applications to other domains where models with stochastic matrices are popular (e.g., topic modeling), and publish user-friendly code.

AI has extraordinary potential and developing countries must move forward quickly in this field to leverage their technological prowess, productivity, and competitiveness. Certainly, investing in R&D, developing capacities, and retaining AI talent is much easier said than done. Besides adopting a national AI strategy, if there is none, developing countries could put into practice a roadmap with clearly defined priorities and projects that bolster the economy. They can also build partnerships and reach out to other countries and organizations that are willing to cooperate in frontier technologies. A niche strategy might help to leapfrog in a few select sectors, as in the case of some small states that have become active players in the digital sphere. Interestingly enough, Kenya became last August the first African country to teach coding as a subject in schools. As stated in the UNCTAD 2021 Digital Economy report, developing countries risk becoming mere providers of data, while having to pay for digital intelligence produced with their data. Current international regulatory frameworks tend to be either too narrow in scope or too limited geographically, failing to enable cross-border data flows with an equitable sharing of economic gains. In a nutshell, developing countries need to find the optimal balance between promoting domestic economic development, protecting public policy interests, and integrating into the global digital ecosystem.

How do international crises unfold? We conceptualize of international relations as a strategic chess game between adversaries and develop a systematic way to measure pieces, moves, and gambits accurately and consistently over a hundred years of history. We introduce a new ontology and dataset of international events called ICBe based on a very high-quality corpus of narratives from the International Crisis Behavior (ICB) Project. We demonstrate that ICBe has higher coverage, recall, and precision than existing state of the art datasets and conduct two detailed case studies of the Cuban Missile Crisis (1962) and Crimea-Donbas Crisis (2014). We further introduce two new event visualizations (event icongraphy and crisis maps), an automated benchmark for measuring event recall using natural language processing (sythnetic narratives), and an ontology reconstruction task for objectively measuring event precision. We make the data, online appendix, replication material, and visualizations of every historical episode available at a companion website www.crisisevents.org and the github repository.

Dr Maurizio Tinnirello is an independent researcher, and visiting lecturer in International Relations, Conflict and Security at Northumbria University and the Amsterdam University of Applied Sciences. He has held academic positions in both the Global South and North, and he has also worked as an international researcher and policy consultant on global security and military corruption issues. He has been the Vice-Chair and Program Chair of the Science, Technology and Art in International Relations section at The International Studies Association since 2019. Dr Tinnirello holds a PhD from the School of Politics and International Relations, and an MA in International Conflict Analysis, from the University of Kent, UK. He was a recipient of a Marie Skłodowska-Curie Action Initial Training Award, and a visiting PhD fellow at Coimbra University.