This paper investigates the correlation of hate speech and hate crime, in a inter-disciplinary approach, using a computational hate speech and hate crime detection method in a socio-political science framework, coupling Natural Language Processing with Political Sciences. The study focuses on Greece in the turbulent period from 2015 to 2022 (a period marked by economic, refugee, foreign policy, and pandemic crises); it analyzes tweets to discern linguistic patterns used to verbally attack predefined target groups consisting of ethnic and religious minorities in the country. Furthermore, it investigates hate crimes reported in the press, against the same target groups and during the same period and proceeds to examine correlations between xenophobic attitudes expressed verbally through social media, and those manifested as physical attacks in real life.

Common European Data Spaces (CEDS) are aimed at creating a single market for data across the EU that will power AI innovation. CEDS cover 14 sectors/domains and will allow secure, trustworthy data/AI models exchange between companies, public administrations etc. The Common European Language Data Space (LDS) is part of CEDS and is already made available in beta phase. The paper presents its technical design and implementation, its governance framework as well as use cases that demonstrate its value. LDS aspires to become part of the future European Language Technology ecosystem.

The Hellenic National Corpus (HNC) is an integrated online environment offering access to standard Modern Greek language material and to related analysis tools. The HNC corpus has been developed in two main phases, and currently comprises over 97 million words exclusively of written language, sourced from printed resources or scraped from the internet. The material has been automatically lemmatized and morphologically annotated, while a subset of 100,000 words has been further manually corrected, in order to produce a freely downloadable error-free corpus. Through the dedicated platform, the users have access to concordances, morphological analysis of words and statistical information (frequency) at word, lemma, part of speech and n-gram levels. Future steps include the expansion of the material in both historical and coverage dimensions: the inclusion of material from older phases of the language is foreseen, as well as the addition of dialectal material besides standards language.

Bridging NLP with political science, this paper examines both the potential and the limitations of a computational hate speech detection method in addressing real-world questions. Using Greece as a case study, we analyze over 4 million tweets from 2015 to 2022—a period marked by economic, refugee, foreign policy, and pandemic crises. The analysis of false positives highlights the challenges of accurately detecting different types of verbal attacks across various targets and timeframes. In addition, the analysis of true positives reveals distinct linguistic patterns that reinforce populist narratives, polarization and hostility. By situating these findings within their socio-political context, we provide insights into how hate speech manifests online in response to real-world crises.

This paper has two objectives: to present (a) the creation of ParlaMint-GR, the Greek part of the ParlaMint corpora of debates in the parliaments of Europe, and (b) preliminary results on its comparison with a corpus of Greek party manifestos, aiming at the investigation of the ideologies of the Greek political parties and members of the Parliament. Additionally, a gender related comparison is explored. The creation of the ParlaMint-GR corpus is discussed, together with the solutions adopted for various challenges faced. The corpus of party manifestos, available through CLARIN:EL, serves for a comparative study with the corpus of speeches delivered by the members of the Greek Parliament, with the aim to identify the ideological positions of parties and politicians.

The current scientific and technological landscape is characterised by the increasing availability of data resources and processing tools and services. In this setting, metadata have emerged as a key factor facilitating management, sharing and usage of such digital assets. In this paper we present ELG-SHARE, a rich metadata schema catering for the description of Language Resources and Technologies (processing and generation services and tools, models, corpora, term lists, etc.), as well as related entities (e.g., organizations, projects, supporting documents, etc.). The schema powers the European Language Grid platform that aims to be the primary hub and marketplace for industry-relevant Language Technology in Europe. ELG-SHARE has been based on various metadata schemas, vocabularies, and ontologies, as well as related recommendations and guidelines.

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI – including many opportunities, synergies but also misconceptions – has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.

We present a replication of a data-driven and linguistically inspired Verbal Aggression analysis framework that was designed to examine Twitter verbal attacks against predefined target groups of interest as an indicator of xenophobic attitudes during the financial crisis in Greece, in particular during the period 2013-2016. The research goal in this paper is to re-examine Verbal Aggression as an indicator of xenophobic attitudes in Greek Twitter three years later, in order to trace possible changes regarding the main targets, the types and the content of the verbal attacks against the same targets in the post crisis era, given also the ongoing refugee crisis and the political landscape in Greece as it was shaped after the elections in 2019. The results indicate an interesting rearrangement of the main targets of the verbal attacks, while the content and the types of the attacks provide valuable insights about the way these targets are being framed as compared to the respective dominant perceptions and stereotypes about them during the period 2013-2016.