Now showing 1 - 10 of 209
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    KI-Verfahren für die Hate Speech Erkennung: Die Gestaltung von Ressourcen für das maschinelle Lernen und ihre Zuverlässigkeit
    (J.B. Metzler, 2023-05) ; ;
    Steiger, Stefan
    Die Erkennung von Hate Speech durch KI erfordert umfangreiche Trainingsdaten. Die Zusammenstellung dieser Trainingsmenge entscheidet über die Leistungsfähigkeit der Systeme, denn es können nur Hassbotschaften erkannt werden, die den Trainingsdaten ähnlich sind. Zunächst werden einige der bestehenden Benchmarks und die Entwicklungen bei deren Aufbau besprochen. Anschließend diskutiert der Artikel mögliche Verzerrungen und die Ansätze für deren Messung. Auch der Vergleich über mehrere Kollektionen und das Schaffen von Transparenz können die Wirksamkeit von Trainingsdaten verdeutlichen.
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  • Publication
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    Interdisciplinary Analysis of Science Communication on Social Media during the COVID-19 Crisis
    In times of crisis, science communication needs to be accessible and convincing. In order to understand whether these two criteria apply to concrete science communication formats, it is not enough to merely study the communication product. Instead, the recipient’s perspective also needs to be taken into account. What do recipients value in popular science communication formats concerning COVID-19? What do they criticize? What elements in the formats do they pay attention to? These questions can be answered by reception studies, for example, by analyzing the reactions and comments of social media users. This is particularly relevant since scientific information was increasingly disseminated over social media channels during the COVID-19 crisis. This interdisciplinary study, therefore, focuses both on science communication strategies in media formats and the related comments on social media. First, we selected science communication channels on YouTube and performed a qualitative multi-modal analysis. Second, the comments responding to science communication content online were analyzed by identifying Twitter users who are doctors, researchers, science communicators and those who represent research institutes and then, subsequently, performing topic modeling on the textual data. The main goal was to find topics that directly related to science communication strategies. The qualitative video analysis revealed, for example, a range of strategies for accessible communication and maintaining transparency about scientific insecurities. The quantitative Twitter analysis showed that few tweets commented on aspects of the communication strategies. These were mainly positive while the sentiment in the overall collection was less positive. We downloaded and processed replies for 20 months, starting at the beginning of the pandemic, which resulted in a collection of approximately one million tweets from the German science communication market.
      6
  • Publication
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    Reactions to science communication: discovering social network topics using word embeddings and semantic knowledge
    (2023)
    Cerqueira de Lima, Bernardo 
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    Abrantes Baracho, Renata Maria 
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    Baracho Porto, Patricia 
    Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources. This study aims to devise a framework that can sift through large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information, and how their behavior toward science communication (e.g., through videos or texts) is related to their information-seeking behavior. To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators, or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data.
      6
  • Publication
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    University of Hildesheim at SemEval-2023 Task 1: Combining Pre-trained Multimodal and Generative Models for Image Disambiguation
    Multimodal ambiguity is a challenge for understanding text and images. Large pre-trained models have reached a high level of quality already. This paper presents an implementation for solving a image disambiguation task relying solely on the knowledge captured in multimodal and language models. Within the task 1 of SemEval 2023 (Visual Word Sense Disambiguation), this approach managed to achieve an MRR of 0.738 using CLIP-Large and the OPT model for generating text. Applying a generative model to create more text given a phrase with an ambiguous word leads to an improvement of our results. The performance gain from a bigger language model is larger than the performance gain from using the lager CLIP model.
      13
  • Publication
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    Online Marketing
    (2023) ;
    Kuhlen, Rainer
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    Lewandowski, Dirk
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    Semar, Wolfgang
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      8
  • Publication
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    Daseinsbezogene Informationskompetenz in ländlichen Räumen. Ansätze aus der Forschungspraxis
    Wie lässt sich ein selbstbestimmtes Informationsverhalten charakterisieren? Welchen Einfluss haben ländliche Räume als Wohnorte auf den Zugang zu Informationen? Das dreijährige Forschungsprojekt eruiert individuelle Informationsbedürfnisse und Informationsverhaltensmuster von Bürgerinnen und Bürgern in einem norddeutschen Landkreis und ergänzt die Perspektive der Nutzenden durch die Erhebung der Informationsbereitstellung der Kommunen. Es schließt mit Handlungsempfehlungen an verschiedene Akteursgruppen. Methodisch wurden in zwei Studienphasen eine quantitative Befragung, (Experten-)Interviews, Fokusgruppen sowie eine Analyse der Gemeindewebseiten durchgeführt. Die Ergebnisse zeigen die Bewertung von Informationen und Quellen als die Hauptschwierigkeit im Informationsprozess und zeigen den Bedarf, die eigenen Denk- und Handlungsmuster vermehrt zu reflektieren. In ländlichen Räumen spielen lokale Instanzen wie Bibliotheken und kommunale Bildungsanbieter eine wichtige Rolle in der Kompetenzförderung.
      10
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      9
  • Publication
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    Body like an idol: K-pop fitspiration on Tumblr – an analysis of texts and images
    (2023)
    Linda Achilles 
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    Thomas Mandl 
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    Christa Womser-Hacker 
    Eating disorders are a major health issue in societies today which oftentimes remain untreated. In social media, such as Tumblr, people build communities to exchange information and connect to each other using specific hashtags. Some of these trends which emerge around these hashtags, are related to eating disorders. This study in information science addresses how inspiration for fitness (Fitspiration) inspired by music fandom (in particular K-pop) can be characterized on Tumblr by automatically analyzing text and images of posts. Images are evaluated based on their colorfulness and emotional measures, texts undergo a sentiment and readability analysis, as well as an evaluation of their psycho-linguistic features. Furthermore, a qualitative content analysis of K-pop Fitspiration posts (n=119) is performed and they are compared to the K-pop Thinspiration posts, regular Thinspiration and control group posts. Results reveal, that K-pop Fitspiration posts are oftentimes more similar to posts from the control group than to Thinspiration posts, but that they also share psycho-linguistic features with posts of eating disordered users.
      3
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    Emma stop that, it's my turn now - Comparing Peer Tutoring and Thinking Aloud for Usability-Testing with Children in a school setting
    (ACM, 2023) ; ;
    Lorberg, Kira 
    The subject of this study was to explore children's ability to offer verbal feedback during usability evaluation studies. The aim is to find out whether the use of the method Peer Tutoring or Thinking Aloud can identify more usability findings in usability tests with second graders than observation. 13 Second graders tested an interactive game using two evaluation techniques. The findings indicate that the majority of verbal remarks were identified with the method of Thinking Aloud and that participants also provided more higher quality remarks. More usability findings could be identified than in a purely observational situation. Unexpectedly, the Peer Tutoring method was less beneficial for the identification of usability problems since the participants struggled to cooperate successfully.
      9