Heinrich, Michael (2025)
Buchpublikation, Mitherausgabe und Buchkapitel, Transcript Verlag.
Heinrich, Michael (2025)
Buchpublikation, Transcript Verlag.
Heinrich, Michael (2025)
Handbuch der Architekturwissenschaften, Hrsg. K. Berr, A. Hahn & P. Lohmann, Springer Verlag.
Heinrich, Michael (2025)
Bedürfnisorientierung in der Innenarchitektur: Ansätze und Methoden aus Forschung und Praxis, Transcript Verlag.
Heinrich, Michael (2025)
Für eine nachhaltige Architektur der Stadt, Wagenbach Verlag, 2025.
Heinrich, Michael (2025)
Keynote, Bestand transformieren, Räume neu denken! BDIA-Summit 2025, Bund Deutscher Innenarchitekten, Jahreskonferenz, Berlin.
Heinrich, Michael (2025)
Einzelausgabe zur Heftreihe "Die Geschichte hinter dem Bild", Landeszentrale für politische Bildung, Thüringen.
Heinrich, Michael (2025)
Keynote, LEADER-Region Tourismusverband Moststraße/ Niederösterreichische Landesausstellung, Niederösterreich.
Heinrich, Michael (2025)
Vortrag, Image Neuroscience and the Arts, Neurocognitive Image Lab, International Studies University, Internationale Fachkonferenz, Shanghai, China.
Heinrich, Michael (2025)
Kontaktkreis der Münchner Architekturverbände, Bayerische Architektenkammer, München.
Reiche, Michael; Leidner, Jochen L. (2025)
The 24th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SOMET 2025), Kitakyushu, Japan, September 23-26, 2025.
Menzner, Tim (2025)
Doctoral Consortium contribution, Proceedings of the Ninth Euopean Conference on Information Literacy (ECIL'25), from 22-25 September 2025, Bamberg, Germany .
Media bias is an enduring feature of news dissemination, reflecting the subjective perspectives of its creators across history. From archaic records like "The Victory Stele of Naram-Sin" to contemporary news channels, bias permeates media, influencing political, social, and public health narratives. This research aims to investigate the persistent phenomenon of media bias and the potential of large language models (LLMs)(Kojima et al., 2022) in its detection and classification, in order to deploy publicly available software tools aiming to enhance media literacy among news consumers.
Traditionally, media bias served the interests of ruling powers; even with the rise of modern journalism, objectivity is often compromised by commercial pressures and inherent human biases. (Rodrigo-Ginés et al., 2024). As media landscapes evolve, bias continues to shape public opinion, impacting democratic processes and public health perceptions—evident during the COVID-19 pandemic, where polarized media narratives swayed public health decisions and fueled misinformation. (Recio-Román et al., 2023)
Current research on the effects of labeling media bias or propaganda, whether automatically or with human involvement, highlights the complexity of the issue. Depending on different circumstances, labeling can lead to negative outcomes (such as reinforcing filter bubbles by providing means to avoid news with a different perspective), no change in news consumption behavior at all, or, in some cases, an actual improvement in media literacy as intended (Zavolokina et al., 2024).
This research aims to develop a technical solution for the automatic labeling of biased media content, emphasizing several proposals that we hope will lead to a positive effect on media literacy among those presented with the system’s assessments.
These proposals include using a fine-grained taxonomy of bias types rather than a simple binary left/right labeling, focusing on detailed explanations for each model decision in natural language, marking bias at the sentence level rather than at the article or publication level to provide more insights, fine-tuning autoregressive models like GPT-3.5 or Mistral with high-quality examples instead of using “simple” bidirectional models like BERT(Brown et al., 2020) or non-finetuned models, and focusing on the German language, which has not yet been properly explored for such systems.
Understanding readers' perceptions when exposed to bias-labeled content is another facet of this research. It will explore how bias labeling influences readers' views on credibility and neutrality and whether real-time bias indicators affect news consumption behaviors. As mentioned, practical applications serve as a cornerstone of this research. One aim is to implement bias detection systems in real-world settings, such as search engines and news aggregators, to promote balanced information consumption. The development of user tools, like browser extensions highlighting media bias, intends to address public need for transparent information evaluation.
In essence, this research contributes to media literacy enhancement by demystifying media bias through advanced computational methods. By refining detection mechanisms, classifying bias more effectively, and implementing practical tools, it aims to fortify democratic discourse and public understanding, thereby addressing the pervasive influence of media bias in today’s interconnected world.
References
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language Models are Few-Shot Learners. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 1877–1901). Curran.
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems, 35, 22199–22213.
Recio-Román, A., Recio-Menéndez, M., & Román-González, M. V. (2023). Influence of Media Information Sources on Vaccine Uptake: The Full and Inconsistent Mediating Role of Vaccine Hesitancy. Computation (Basel). https://doi.org/10.3390/computation11100208
Rodrigo-Ginés, F.-J., Carrillo-de-Albornoz, J., & Plaza, L. (2024). A systematic review on media bias detection: What is media bias, how it is expressed, and how to detect it. Expert Systems with Applications, 237, 121641.
Keywords: Media Bias, Large Language Models, Bias Detection, Natural Language Processing, Journalism, Public Opinion, Taxonomy
Reiche, Michael; Leidner, Jochen L. (2025)
11th Intelligent Systems Conference 2025 (Intellisys'25), 28-29 August 2025, Amsterdam, The Netherlands.
Busch-Geertsema, Annika; Lanzendorf, Martin; Müggenburg, Hannah; Wilde, Mathias (2025)
Handbuch Verkehrspolitik.
DOI: 10.1007/978-3-658-04777-1_33-2
Der vorliegende Beitrag befasst sich mit neueren Erkenntnissen der Mobilitätsforschung zur Erklärung von Verkehrshandeln. Aus der Perspektive verschiedener Disziplinen wird die Wechselwirkung zwischen Mobilität und sozialen Strukturen analysiert und hinsichtlich ihrer Bedeutung für eine sozial-ökologische Transformation des Mobilitätssystems bewertet. Zunächst wird das Verhältnis von Raumstrukturen und Mobilität geklärt, bevor grundlegende Theorien der Umweltpsychologie vorgestellt werden. Anschließend wird die Bedeutung von Routinen sowie von Umbruchsituationen im Lebenslauf für die Veränderung des Verkehrshandelns diskutiert und das darauf basierende Konzept der Mobilitätsbiografien erläutert. Folgerungen für eine sozial-ökologische Transformation des Mobilitätssystems und die dafür notwendige Mobilitätspolitik schließen den Beitrag ab.
Grosch, Christian (2025)
The Human Side of Service Engineering 182.
DOI: 10.54941/ahfe1006400
Demmler, Uwe (2025)
Steuerrecht aktuell 2025 (1), 48-53.
18. Wissenschaftstag der Europäischen Metropolregion Nürnberg, Amberg, Amberger Congress Centrum (ACC), Freitag, 11. Juli 2025.
Böck, Felix; Ochs, Michaela; Henrich, Andreas; Landes, Dieter; Leidner, Jochen L.; Sedelmaier, Yvonne (2025)
Böck, Felix; Ochs, Michaela; Henrich, Andreas; Landes, Dieter; Leidner, Jochen L....
User Modeling and User-Adapted Interaction.
Learning is at the heart of every progress the human species makes. It is most effective when it considers who we are as individuals, what learning approach we prefer and what we already know to begin with. In the digital age, we strive to capture such information in the form of a digital representation -- the so-called learner model --, to tailor learning-related systems to this information and build upon it to create more personalised learning experiences. Over recent years, the proliferation of diverse models across various educational applications and disciplines has made it challenging to access targeted research.
In this survey, we aim to address this gap, reviewing the latest advances in learner modelling and conducting a comprehensive analysis of the existing approaches, focusing on developments from 2014 to 2023. With the help of a systematic literature review, we want to provide designers and developers of learner models with a structured overview and simplified entrance into the topic and the field of learner models. We investigate the question: What do learner models look like and how are they filled, kept up-to-date, and used?
To this end, we analyse and classify existing approaches. Our findings provide a comprehensive and structured overview of the field of learner modelling, allowing researchers to navigate and understand the diverse approaches more easily and providing developers of learner models or adaptive systems with a practical tool to access relevant information according to their needs.
Heinrich, Michael (2025)
Impulsvortrag, kreativgelöst: Zukunft Bauen. Neue kreative Impulse aus Architektur, Design, Forschung sowie Stadt- und Leerstandsplanung. Thüringen Kreativ/ Thüring. Wirtschaftsministerium.
Arbeiter, Georg; Patiño Studencki, Lucila (2025)
Hochschule Coburg
Friedrich-Streib-Str. 2
96450 Coburg