Pavlos Fafalios

The circular economy ( CE ) is increasingly promoted as a pathway to sustainable development, yet systematic evidence on the structure of the CE labor market remains limited. Little is known about how CE ‐related job opportunities are distributed across countries, which job characteristics dominate this market, and how such patterns compare with indicators from other sources. In this study, we address these gaps by analyzing CE ‐related job postings from LinkedIn across EU countries and selected non‐EU countries. Using a structured data collection and normalization approach, we examine cross‐country differences in CE labor market size and key job attributes (work mode, job type, experience level, and job function). We further compare the introduced LinkedIn indicator with Eurostat employment data and research output. The findings reveal pronounced regional disparities, a negative association with official CE employment statistics, and a positive relationship with research activity, highlighting the potential of professional networking platforms in understanding labor markets.

This workshop focuses on the convergence of computational and interdisciplinary methods for analyzing online discourse, such as claims, arguments, and opinions on contentious issues. As mis- and disinformation, bias, and echo chambers continue to grow, NLP-based techniques—including argument mining, stance detection, and fact verification—have become increasingly important. At the same time, these tasks depend on strong conceptual foundations that span communication studies, computational linguistics, and computer science. BeyondFacts aims to bring together researchers from a wide range of communities—such as the social sciences, political science, computational journalism, and computer science—to advance the machine-based interpretation and analysis of societal debates using methods from web mining, AI, and NLP.

Historical newspapers are invaluable resources that capture the cultural, social, economic and political dimensions of their time, providing unique insights into past events and daily life. However, the potential of these archives remains underutilised due to the limitations of traditional research methods, which rely on laborious manual examination of individual pages or articles. Digital archives have begun to address this issue by enhancing accessibility, but the lack of effective tools for parsing and analysing the contents still restricts their utility for large-scale historical research. Optical Character Recognition (OCR) technology plays a foundational role in converting scanned images into searchable text. However, OCR alone cannot adequately handle the complexities of historical newspapers, which often feature degraded image quality, inconsistent typography and intricate layouts. In parallel, the effective segmentation and ordering of newspaper content is essential for generating well-structured and logically ordered data from specific newspaper sections. This article focuses on the development of a pipeline that addresses these challenges, using the daily newspaper Le Sémaphore de Marseille as a case study (35,703 issues covering the period 1827–1944). The proposed system integrates layout analysis, OCR and Generative AI-driven information extraction techniques to extract specific data elements from scanned images, namely ship arrivals data, and to store them in a machine-readable format for further analysis. This well-structured output enables historians to analyse historical trade patterns, economic trends and regional interactions more efficiently and at a much larger scale. An evaluation of the key pipeline components demonstrates the effectiveness of the system, achieving an F1-score of 96% in paragraph segmentation and information extraction, while also highlighting the need for further improvement in the critical OCR component.

Food resource management plays a pivotal role in addressing global challenges related to food security, sustainability, public health, and economic development. To facilitate the collection and identification of food resources, various collections and systems have emerged from the scientific community. One such system is FoodEx2, which has been developed by the European Food Safety Authority and is used for classifying and describing food-related information. In this paper, we describe how FoodEx2 can be transformed into a standardized thesaurus using well-established technologies and standards that enhance its interoperability and exchange of data resources. The new thesaurus also promotes its usage through the adoption of unique and global identifiers for its contents and through a variety of tools that can be used for accessing and visualizing it. In addition, we describe how the thesaurus can be reconstructed from the original sources as they evolve.

This paper presents a quantitative approach that captures the LinkedIn information that can be derived regarding companies and jobs that operate in a circular economy context. To do so, we perform this analysis for the regions of Spain, providing metrics for the standardization of the companies identified, for measurement reasons, while job posts and other information is analyzed. Finally, heatmaps and other graphs demonstrate the distribution of LinkedIn information for the various regions of Spain, concluding that Spain's regions do not perform in a homogenous way in LinkedIn regarding the circular economy.

Expressing opinions and interacting with others on the Web has led to the production of an abundance of online discourse data, such as claims and viewpoints on controversial topics, their sources and contexts (events, entities). This data constitutes a valuable source of insights for studies into misinformation spread, bias reinforcement, echo chambers or political agenda setting. Computational methods, mostly from the field of NLP, have emerged that tackle a wide range of tasks in this context, including argument and opinion mining, claim detection, checkworthiness detection, stance detection or fact verification. However, computational models require robust definitions of classes and concepts under investigation. Thus, these computational tasks require a strong interdisciplinary and epistemological foundation, specifically with respect to the underlying definitions of key concepts such as claims, arguments, stances, check-worthiness or veracity. This requires a highly interdisciplinary approach combining expertise from fields such as communication studies, computational linguistics and computer science. As opposed to facts, claims are inherently more complex. Their interpretation strongly depends on the context and a variety of intentional or unintended meanings, where terminology and conceptual understandings strongly diverge across communities. From a computational perspective, in order to address this complexity, the synergy of multiple approaches, coming both from symbolic (knowledge representation) and statistical AI seem to be promising to tackle such challenges. This workshop aims at strengthening the relations between these communities, providing a forum for shared works on the modeling, extraction and analysis of discourse on the Web. It will address the need for a shared understanding and structured knowledge about discourse data in order to enable machine-interpretation, discoverability and reuse, in support of scientific or journalistic studies into the analysis of societal debates on the Web. Beyond research into information and knowledge extraction, data consolidation and modeling for knowledge graphs building, the workshop targets communities focusing on the analysis of online discourse, relying on methods from machine learning, natural language processing, large language models and Web data mining.