Amelie Dorn

Greeting and farewell formulae are highly conventionalized routines that index social relations, regional norms and situational formality. This paper investigates pragmatic variation in greetings and farewells in rural Austria, focusing on age, region, setting and prosodic design. We analyze greeting and farewell formulae and metalinguistic comments produced by 152 adults from 13 rural locations across two project phases. The dataset combines spontaneous interactions with controlled elicitations in two greeting experiments contrasting a formal urban service encounter and an informal local dialect encounter. In spontaneous greetings, the supraregional call formula hallo predominates and is significantly rarer among older speakers. Across age groups, participants strongly preferred grüß Gott in the formal urban store scenario and griaß di in the informal local bakery scenario. Pronominal address is largely avoided, with T forms restricted mainly to western locations and familiar contexts. Prosodically, participants use [ˈhalo] and [haˈloː] at similar rates, whereas interviewers strongly favor the marked variant [haˈloː], suggesting different interactional roles in greeting management. Older speakers produce significantly more metalinguistic comments, revealing a mismatch between politeness ideologies and recorded practice. Overall, the findings reveal age-graded and context-dependent patterns of pragmatic variation in rural Austria.

The focus of our contribution is on lexical Austriacisms, i.e., lexical features of the Austrian standard language. Whereas in previous studies, only a small set of Austriacisms has been examined, with food terms being particularly popular, this contribution considers 76 lexical variables with 205 variants (Austriacisms and their co-variants), which are examined through complex variationist corpus analyses. The data is provided by the Austrian Media Corpus (amc), which represents the language use of the Austrian print media landscape in the 21st century. The analyses are both (short-term) diachronic and synchronic, taking into account the variation in vivo. Irrespective of the frequency-based “starting point” of a variant at the beginning of the 21st century, its relative frequency remains at comparable levels in the course of the observation period. Contrary to the threat scenarios of previous studies, our corpus analyses indicate the relative stability of the majority of Austriacisms over the 23 years studied (2001–2023).

We investigate frequencies of (1) new vocabulary and (2) evaluative-expressive morphopragmatic forms such as expressive compounds and diminutives in two corpora of German in Austria in the first 22 months of the COVID-19 pandemic: (1) the amc (Austrian Media Corpus), a corpus of newspaper articles using more formal registers and (2) the stf (STANDARD forum corpus), a corpus of user-generated content of more informal forum posts on the COVID live ticker of an online newspaper. Results show significant correlations between frequencies of new COVID-19 vocabulary in both corpora despite the different registers used. However, the timing of emergence and of frequency peaks differs in both corpora, as will be illustrated by the three COVID-19 neologisms Babyelefant ‘baby elephant (= distance measure)’, Covidiot_in ‘covidiot’ and Corona-Leugner_in ‘Corona denier’: New lemmas tend to emerge and increase in the formal amc first before being picked up by the users of the informal stf, leading to later but mostly higher frequency peaks of the lemmas in the stf. The results show also significant correlations between daily new COVID-19 cases and numbers of forum posts as well as between daily new COVID-19 cases and frequencies of morphopragmatic forms produced by forum users, indicating a higher posting activity and the use of more emotional vocabulary in more problematic periods of the pandemic.

Language is subject to constant change. The use of words can change across generations, just as the meaning of words can change over the years. The FWF Top Citizen Science project “The ABCs of Dialect” aims to transcribe and reflect old dialect words contained on the more than 100-year-old paper slips of the Dictionary of Bavarian Dialects in Austria (WBÖ). On the one hand, historical data is digitally processed, analysed and made accessible via the Zooniverse platform; on the other hand, interested citizens should be inspired to rediscover their linguistic and regional heritage. A crowdsourcing approach enables more extensive processing and transcription of the materials, while in a special reflection section citizens simultaneously reflect on and discuss the use and meaning of the classified words.

This article provides insights into dealing with complexities in the Digital Humanities project exploreAT!. By exploring a non-standard language collection for cultural insights, a three-fold approach is presented looking into concrete realizations and solutions of tackling challenges in terms of Open Innovation infrastructure, technology and the topic of choice, food. Methods and processes applied and developed in the project are aimed to serve as examples for future projects with similar data sets.

Cultural heritage images are among the primary media for communicating and preserving the cultural values of a society. The images represent concrete and abstract content and symbolise the social, economic, political, and cultural values of the society. However, an enormous amount of such values embedded in the images is left unexploited partly due to the absence of methodological and technical solutions to capture, represent, and exploit the latent information. With the emergence of new technologies and availability of cultural heritage images in digital formats, the methodology followed to semantically enrich and utilise such resources become a vital factor in supporting users need. This paper presents a methodology proposed to unearth the cultural information communicated via cultural digital images by applying Artificial Intelligence (AI) technologies (such as Computer Vision (CV) and semantic web technologies). To this end, the paper presents a methodology that enables efficient analysis and enrichment of a large collection of cultural images covering all the major phases and tasks. The proposed method is applied and tested using a case study on cultural image collections from the Europeana platform. The paper further presents the analysis of the case study, the challenges, the lessons learned, and promising future research areas on the topic.

This article reports on the experience of co-designing an educational video game aimed at promoting good dietary habits in youngsters and fostering Sustainable Development Goals (SDGs), such as SDG 3 (Good Health and Well-Being), SDG 10 (Reduced Inequalities), and SDG 17 (Partnerships for the Goals). To ensure the quality of the results, we developed a methodology under a social innovation paradigm that enabled the co-creation of the game. The methodology was driven by a series of three workshops, during which we adopted several different gamification strategies to support a Participatory Design (PD) process with the stakeholders, a group of local pre-teen and teen girls at social risk (N = 22). Captured requirements materialized into intermediate prototype evaluations that motivated a progressive refinement of the game.

Both, historical as well as contemporary images depicting particular aspects of a certain culture, or a certain cultural practice, are widely available in a variety of formats. Typically, historical-cultural images can be found in museums, national archives and libraries both in analogue and digital formats. The Europeana image collection serves as a valuable example of a digital image collection available to the public, that is freely accessible and searchable. A significant amount of the collection, however, does not contain a rich semantic description of the cultural and social aspects represented in the images that would go beyond some metadata like author and title. To enable the enrichment of these images, we propose a project which brings together experts from digital humanities, linguistics, artificial intelligence and semantic web technology. The project aims at analysing the contents of the images with a combination of computer vision, natural language processing and manual curation to represent them with a more descriptive and representative controlled vocabulary. This combination of different types of expertise to address the problem enables us to closely collaborate and learn from each other by taking different roles and perspectives. So far, the collaboration has contributed to the understanding of the detailed requirements from the digital humanities and socio-linguistic perspectives for the representation and processing of cultural images using semantic web technologies, such as multi-disciplinary thesauri, ontologies, computer vision and AI.

Introduction and Objectives: Public and Patient Involvement and Engagement (PPIE) in medicine and healthcare has received increased attention in recent years, also in the scientific field. However, involving patients in research activities in order to tap into their pool of lived experience and knowledge, and to enable closing research gaps, is, however, still scarce. In this

Digital cultural heritage objects can benefit greatly from the application of Artificial Intelligence such as computer vision based tools to automatically extract valuable information from them. Novel methods and technologies have been used in the last few years to perform image classification, object detection, caption generation, and other techniques on different types of digital objects from different disciplines. In this pilot study, carried out in the context of the Digital Humanities project ChIA, we present an approach for testing different commercial (Clarifai, IBM Watson, Microsoft Cognitive Services, Google Cloud Vision) and open-source (YOLO) computer vision (CV) tools on a set of selected cultural food images from the Europeana collection with regard to producing relevant concepts. The project generally aims at improving access to implicit cultural knowledge contained in images, and increase analysis possibilities for scientific research as well as for content providers and educational purposes. Preliminary results showed that not only quantitative output results are important, but also the quality of concepts generated. Types of digital objects can pose a challenge to CV solutions.