The media room exhibits insight from world and local news, aiming to identify SDG-related events from millions of worldwide multilingual news, and to exhibit best practices towards solving SDG-related problems. This is offered in collaboration with EventRegistry.

Here we provide an up-to-the-minute view of global news related to SDG 2 and the efforts to address this SDG, offering a comprehensive media perspective on progress across various fronts. By analyzing the flow of daily news, this tool enables policymakers to stay informed about the ongoing conversations and challenges faced around SDG 2 acceleration through AI. On the right-hand side, a dynamic word cloud is updated daily to highlight the most discussed topics in the media, giving a snapshot of global attention to SDG-related issues. Accompanying this is a sentiment analysis tool that measures and visualizes the tone of the coverage—from negative (0) to highly positive (1)—for any selected date, providing insight into how global media is framing the issue.

On the left, a curated list of daily news articles is available, each linking directly to its original source, enabling deeper exploration. The word cloud organizes these key themes using Wikidata concepts, ensuring a consistent understanding of important terms, regardless of the language of the articles. The sentiment analysis also offers critical insights into the emotional tone of media coverage, revealing how SDG 2 topics and related risks are perceived and communicated by journalists and the global public. This analysis allows us to delve into the various topics and subtopics highlighted in global news coverage related to the chosen SDG.

By analyzing signals from worldwide media, this tool offers a comprehensive understanding of how different issues related to the SDG are being discussed across the globe. The data is provided in collaboration with Event Registry, a cutting-edge AI news engine that aggregates and analyzes news content. Through this partnership, we bring you real-time insights into the evolving narrative surrounding SDG progress, allowing policymakers to stay informed about key global developments. For further exploration, you can visit eventregistry.org, where you can access more visualizations related to worldwide news queries and even experiment with their News API to gain deeper, customizable insights into global media trends. These tools are invaluable for identifying emerging trends, understanding the global media landscape, and supporting data-driven decisions in policymaking related to this SDG.

 

Developed in collaboration with 

This perspective is providing the IRCAI user with the access to text-mining tools to improve effectiveness in reviewing a topic over a large dataset of published science and patented technology.

In this view, you can observe the relationships between key concepts (represented as edges) relevant to the selected SDG. These connections reflect the strength of relationships between concepts, with the intensity of the link indicating how frequently these topics co-occur in scholarly articles. Stronger connections suggest that the concepts are frequently discussed together in the context of the SDG, while weaker links show less frequent co-occurrence. The data for this analysis is sourced from OpenAlex, a comprehensive research database encompassing over 128 million articles published globally since the 1940s.

By analyzing these relationships, policymakers can gain deeper insights into how different concepts within the SDG are interconnected, helping to identify areas of high research focus as well as potential gaps. Originally developed as part of the European Commission-funded NAIADES project, this tool has since been expanded to cover all 17 SDGs, providing a broad, cross-cutting view of research trends and interrelated concepts across the entire sustainable development agenda. This resource empowers policymakers to understand the nuanced relationships between concepts, guiding more informed decisions and fostering an integrated approach to SDG implementation.

The following perspective allows you to explore the research trends related to the SDG you’ve selected, represented through a Gantt chart. This visualization highlights the 15 most relevant trends identified within the chosen SDG 2, some of which may also intersect with other SDGs.

These interconnected trends are color-coded for easy identification, providing a comprehensive view of how various global issues are evolving in relation to one another. The data used for this analysis is sourced from OpenAlex, a comprehensive research database containing over 128 million articles published worldwide since the 1940s. This extensive dataset offers valuable insights into the main research topics that shape the discourse on the SDG, allowing policymakers to track the progression of academic and scientific focus over time and understand how these trends evolve and intersect. By visualizing these trends, policymakers can stay informed about emerging areas of research and their relevance to sustainable development goals, enabling data-driven decision-making that considers both historical context and future directions.

The observation of policies applied worldwide on SDGs is fundamental to better understand the progress of the global action. Explore the topics related to the legal and regulatory landscape from open data using sophisticated data analytics and machine learning methods.

This tool allows you to observe the current weight of various SDG topics in the policy and legislative landscape for the selected SDG. The interactive SDG barcode visually represents the prominence of different topics within policies related to the chosen SDG.

By hovering your mouse over the rectangles in the barcode, you can identify and explore the specific SDG topics being prioritized in current policies and legislation. The initial policy data is sourced from OECD.AI, offering a comprehensive overview of policy trends and legislative priorities. This analysis is part of the AI4GOV project, funded by the European Commission, which aims to integrate AI-driven insights into government decision-making processes, ensuring that policies align with the SDG framework. This tool provides policymakers with valuable insights into how different SDG topics are being addressed at the policy level, helping to guide more targeted and effective legislative action for sustainable development.

This complementary perspectve allows you to observe the evolving relationships between key concepts identified in legal and regulatory documents related to the selected SDG. By analyzing the frequency of these concepts within policy documents, you can gain insights into how different topics are interrelated and how their prominence shifts over time in the context of the chosen SDG.

The visualization helps you explore how concepts are linked within the policy landscape, providing an understanding of the areas that are receiving more focus and those that might need further attention. The relationships between these concepts are mapped according to their frequency and the SDG they correspond to in the policy documents. The initial policy data is sourced from OECD.AI, offering valuable insights into policy trends and legislative frameworks. This analysis is part of the AI4GOV project, funded by the European Commission, which aims to integrate artificial intelligence into government processes for more informed, data-driven decision-making on SDG implementation. This tool provides policymakers with a dynamic view of how SDG-related concepts are being addressed and prioritized within the legal and regulatory landscape, enabling more effective policy adjustments and interventions.

Key indicators that report on the status of water sustainability will further understanding of this important topic. With this tool, you can utilize drop-down menus and animations to explore the various aspects of and progress towards SDG 2.

This view offers a detailed, individual perspective on each of the SDG indicators, allowing you to break down and better understand the parameters within both global and local contexts. It enables you to track the evolution of each indicator over time, providing valuable insights into how it has progressed across different regions and countries.

By selecting the SDG-related indicators from the dropdown menus, you can delve deeper into the data. Scroll down to view the curves of the selected indicators represented for each country, allowing for a comparative analysis of how various nations are performing on specific SDG targets. The data is sourced from well-established open data providers such as UN Stats and the World Bank, through the SDG Tracker initiative of Our World in Data. This ensures that the insights are based on reliable and comprehensive sources, although it’s important to note that the data coverage may not be complete for all countries. Despite this, the visualization provides a valuable signal that can be used to explore multilevel relationships between different SDG indicators.

This view presents the influence of multiple factors on the selected indicator, measured using SHAP (SHapley Additive exPlanations) values—a powerful method that offers transparent, data-driven insights into the predictions of complex models. The visualization showcases the top 25 features that most significantly contribute to changes in the selected indicator, providing clarity on which variables have the greatest impact.

By selecting a specific country and year, you can analyze how multiple indicators collectively affect the poverty baseline, offering a comprehensive understanding of the underlying drivers of poverty. This feature allows you to explore the interactions between different factors and how they influence poverty outcomes. Additionally, the tool allows you to toggle between two different plot types, providing complementary perspectives on the data. This flexibility enables more nuanced exploration, helping policymakers and analysts to gain a deeper understanding of the relationships between indicators and make more targeted, evidence-based policy decisions.

This work was prepared in the context of the project An AI-driven Observatory Against Poverty granted by the