There is no doubt that Online Social Networks have changed the way people communicate and interact. These platforms have helped to break down barriers and to ease worldwide communication, to share multimedia content or to easily know what is happening in almost any point in the world. Facebook (2004) or Twitter (2006) but also other social media platforms such as Youtube (2005) or messaging services such as WhatsApp (2009) have promoted this new era. However, the meteoric rise of the use of these platforms has also been followed by continuous attempts to subvert their original purposes, using them by the so-called malicious actors for evil purposes. Examples of this can be the generation of mistrust against vaccines, the creation of content supporting climate denial theories, or disinformation campaigns trying to manipulate people's opinion to alter the results of democratic elections. Therefore, there is a wide variety of different types of attempts to undermine social networks and to distorse public discourse. In this project, we seek to reveal and detect the presence of malicious actors in Online Social Networks and to profile these actors through a multidisciplinary approach, including experts in the use of novel computational techniques from the Artificial Intelligence and Computer Vision fields and experts from the Behavioural Sciences field. The project will also leverage a multimodality approach, analysing text, images, videos, audio network structures, interactions between users and trust perceptions. From the Behavioural Sciences research field, different approaches, including psychology, discourse analysis, sociology and human-computer interaction will allow us to build a taxonomy of the different malicious actors and to profile them. From the Artificial Intelligence field, tools such as Deep Learning, advanced Natural Language Processing, and Social Network Analysis provide us with the necessary instruments to build a software tool for the analysis and detection of these malicious actors with novel features such as multilingualism, explainability, and a strong focus on an open-source solution.

BIO-INTEL-MOB aims to revolutionize peri-urban mobility, logistics, and governance through AI-driven, citizen-centric, and climate-neutral solutions. Addressing fragmented mobility networks, inefficient logistics, and governance gaps, the project integrates Advanced Mobility Data Space (aMDS), Peri-Urban Mobility Hubs (PUMHs), Green-Safe Routing Algorithm (GSRA), bio-packaging logistics units (bioPLU), and Digital Twin ecosystems. By leveraging AI-powered risk detection, emission monitoring, real-time analytics, multimodal transport optimization, and VR/AR-enabled participatory governance, BIO-INTEL-MOB enhances urban-peri-urban accessibility and connectivity, reduces private car dependency by 35%, and optimizes multimodal transport efficiency by 35%. The project’s interventions will lead to a 30% reduction in urban-peri-urban congestion, a 25% decrease in logistics-related emissions and packaging waste. Further, BIO-INTEL-MOB will cut VRU-related accident risks by 35%, improve air quality and noise pollution by 20%, and reduce the human health impact of transport-related pollution by 30%. The project conducts large-scale pilot demonstrations in Rome, Cascais, Riga & Vilnius, alongside satellite pilots in Melsungen, Ciampino, Urla, and Rhodes, ensuring scalability and transferability. Through policy-aligned smart city governance (SCGo) with policy engine, citizen-voice-app and knowledge-hub, and AI-driven multimodal transport innovations, BIO-INTEL-MOB strengthens citizen participation in city planning by 40%, creating data-driven and participatory ecosystems. The project’s AI-driven models will ensure a 40% increase in participatory governance efficiency, 25% faster policy compliance processes, and a 35% improvement in co-designing of city policies and infrastructure. The outcomes contribute to the EU Green Deal, Digitalization Strategy, and Climate-Neutral Cities Mission, positioning peri-urban areas as resilient, connected, and low-emission living spaces.

The project proposes both valuable enhancement of current Artificial Intelligence (AI) foundation models and a major upgrade of the European Lexicographic Infrastructure (ELEXIS), which resulted from a Horizon 2020 project with the same name (2018-2022). The upgrade represents a transformative reconstruction of the infrastructure, based on recent developments in AI, in particular those related to the emergence of Large Language Models (LLMs). Besides the integration of national, regional, and institutional efforts in the field of lexicography, the new infrastructure will offer upgraded technology, language data, tools, and services that are crucial for improving transformer-based models with multilingual knowledge management originating from high-quality lexicographic resources. The advent of machine-readable knowledge representations (knowledge bases and graphs) that are linguistically sound and can be injected into LLMs, enables the introduction of a virtuous cycle where the relevance of the integrated linguistic knowledge is verified by better outputs from LLM applications that, subsequently, can be used for improving of knowledge representations and language data. To verify the effect of incorporating linguistic knowledge in LLMs, the creation of reliable benchmarks and other means of evaluation of machine-generated output is foreseen as a result. As such, the infrastructure design will contribute to the yet unresolved tasks of Natural Language Understanding. The establishment of a new virtual lexicographic infrastructure will be carried out by a broad and diverse consortium, including partners from all relevant fields: lexicography, Computational Linguistics, and AI. For long-term sustainability, the infrastructure will rely on several prominent infrastructural initiatives: CLARIN and DARIAH, two ESFRI Landmark infrastructures, and ALT-EDIC, as the new pan-European initiative dedicated to the development of European open massively multilingual language models.