AI4Sci develops hybrid AI methods at the intersection of machine learning, distributional semantics and knowledge representation in order to analyze online discourse and in particular scientific controversies taking place on the web with an applications related to the COVID-19 pandemic. Scientific insights form a central part of public discourse, in particular in the context of the COVID-19 pandemic. However, due to the inherent complexity of scientific claims as well as the mechanisms of online platforms, where controversial topics are shown to generate more user interaction, retention and virality, scientific findings tend to be represented in a simplified, decontextualized and often misleading way. In this context, AI4Sci addresses the challenge of providing hybrid AI methods for tracing and interpreting scientific claims in online discourse, as a means to tackle and understand misinformation in society. Progress in areas such as transfer learning and neural NLP have opened up new possibilities for the AI-based interpretation of online discourse. On the other hand, it has been shown that structured knowledge can improve transparency and performance of neural models, while neural language models themselves carry relational knowledge. Building on these insights, the project will develop hybrid AI methods, able to classify and disambiguate online discourse about scientific findings as observable in online news media and the social Web. AI4Sci will build on recent advances in AI at the intersection of neural NLP, distributional semantics and symbolic knowledged to develop methods geared towards the particular problem of extracting and classifying scientific claims about controversial topics together with related contextual information from online discourse and matching them to their respective scientific context. The hybrid methodology of AI4Sci will also contribute to widely recognised issues such as transparency and reproducibility of neural models. Given the very discipline-specific contexts of scientific claims, both in science as well as online discourse, AI4Sci will evaluate methods in two use-cases centered around the COVID-19 pandemic, involving the life sciences as well as the social sciences. The joint expertise of LIRMM (France) and GESIS (Germany) combines backgrounds in symbolic AI and knowledge graphs , with expertise in NLP/NLU. In particular with respect to mining and understanding online discourse on the (social) Web, the two partners complement each other with applications in the context of computational social science (GESIS) and life science (LIRMM). This will advance the AI-related agendas of both organisations and contribute to the AI strategies at the national and international level. The project will build on joint work and GESIS- and LIRMM-hosted corpora, such as knowledge graphs about online discourse, unique Web and social Web crawls as well as scientific data and bibliographic archives, which will accelerate and facilitate the AI4Sci work programme. AI4Sci brings together a highly diverse team of 4 established and 3 young researchers from LIRMM and GESIS, which will be enhanced by two PhD projects.

Recent years witness an upsurge in the quantities of digital research data, offering new insights and opportunities for improved understanding. Text and data mining is emerging as a powerful tool for harnessing the power of structured and unstructured content and data, by analysing them at multiple levels and in several dimensions to discover hidden and new knowledge. However, text mining solutions are not easy to discover and use, nor are they easily combinable by end users. OpenMinTeD aspires to enable the creation of an infrastructure that fosters and facilitates the use of text mining technologies in the scientific publications world, builds on existing text mining tools and platforms, and renders them discoverable and interoperablethrough appropriate registriesand a standards-based interoperability layer, respectively. It supports training of text mining users and developers alike and demonstrates the merits of the approach through several use cases identified by scholars and experts from different scientific areas, ranging from generic scholarly communication to literaturerelated tolife sciences, food and agriculture, and social sciences and humanities. Through its infrastructural activities, OpenMinTeD’s vision is tomake operational a virtuous cycle in which a) primary content is accessed through standardised interfaces and access rules b) by well-documented and easily discoverable text mining services that process, analyse, and annotate text c) to identify patterns and extract new meaningful actionable knowledge, which will be used d) for structuring, indexing, and searching content and, in tandem, e) acting as new knowledge useful to draw new relations between content items and firing a new mining cycle. To achieve its goals, OpenMinTeD brings together different stakeholders, content providers and scientific communities, text mining and infrastructure builders, legal experts, data and computing centres, industrial players, and SMEs.

The GRETA project aims to improve understanding on the conditions and barriers for energy citizenship emergence. Energy citizenship has come to represent a form of active participation within energy systems that ultimately supports local and global decarbonisation goals. It can manifest in many different ways, such as individual homeowners choosing renewable energy solutions or electric vehicles, participation in energy communities, or advocating for climate change. But not everyone has the possibility to participate. This can be due to a range of factors, including being unaware of issues or their practical solutions; being excluded from debates and decision-making; being prevented from taking action due to lack of resource or lack of power. Through a multinational survey and six participatory case studies, GRETA will develop frameworks and models aimed to reveal what factors affect energy citizenship. These will be utilized within case studies to identify problems, frame solutions and reach consensus on roadmaps for change, formalized through Energy Citizenship Contracts. Findings throughout the project will be utilized to inform and encourage policymakers to advocate energy citizenship.

TWIN4DEM brings together scholars of the social sciences and humanities, computational social sciences (CSS) and democracy stakeholders to jointly address one of the most pressing contemporary issues: what causes democracies to backslide? Combining various advanced CSS methods, TWIN4DEM prototypes the first ever digital twins of four European democratic systems (Czechia, France, Hungary and the Netherlands). In doing so, TWIN4DEM delivers four major breakthroughs. First TWIN4DEM develops a new agent-based conceptual model allowing to identify the causal pathways leading to executive aggrandisementn - the excessive concentration of powers into national executives - and threatening rule-of-law institutions. This will allow the systematic identification and testing of new hypotheses on the multidimensional causes of democratic backslide. Second, TWIN4DEM releases new cross-cutting tools to process and aggregate textual and non-textual data more efficiently and in real-time in an open, FAIR and GDPR-compliant manner. TWIN4DEM tools will not only allow democracy researchers to process more effectively the abundance of data on political life but also to enhance the transparency and legitimacy of democratic decision-making. Third, TWIN4DEM simulates, together with national policy makers and civil society organizations, policy scenarios to prevent and react against democratic backslide. This will enhance the effectiveness of interventions aiming at shielding rule-of-law institutions against external and internal threats. As a result, European democracies will be more resilient. Fourth, by formulating guidelines on scaling up the use of CSS in democracy research in a participatory, open and ethics-driven manner, TWIN4DEM paves the way for using such methods in a way that empowers citizens and reinvigorates the quality of democratic governance.