(in Vorbereitung) Shrines of Wisdom - Learning, Knowledge, and the Architecture of Libraries
Heinrich, Michael (2025)
Buchpublikation, Mitherausgabe und Buchkapitel, Transcript Verlag.
Peer Reviewed
(in Vorbereitung) Handbuch für Angewandte Ästhetik
Heinrich, Michael (2025)
Buchpublikation, Transcript Verlag.
Peer Reviewed
(in Vorbereitung) Metadisziplinarität in der Architekturforschung
Heinrich, Michael (2025)
Handbuch der Architekturwissenschaften, Hrsg. K. Berr, A. Hahn & P. Lohmann, Springer Verlag.
Peer Reviewed
(in Vorbereitung) Bedürfnisse als Spannungsfeld: Neue Anwendungsstrukturen der Humanorientierung in Architektur und Design
Heinrich, Michael (2025)
Bedürfnisorientierung in der Innenarchitektur: Ansätze und Methoden aus Forschung und Praxis, Transcript Verlag.
Peer Reviewed
(in Vorbereitung) Die Ästhetik gebauter Lebenswelten und ihre gesellschaftliche Relevanz
Heinrich, Michael (2025)
Für eine nachhaltige Architektur der Stadt, Wagenbach Verlag, 2025.
(in Vorbereitung) Die Ästhetik des Baubestands: Potential für Wohlbefinden und Sinnhaftigkeit
Heinrich, Michael (2025)
Keynote, Bestand transformieren, Räume neu denken! BDIA-Summit 2025, Bund Deutscher Innenarchitekten, Jahreskonferenz, Berlin.
(in Vorbereitung) Schönheit. Subjektive Gewissheit und objektiver Zankapfel
Heinrich, Michael (2025)
Einzelausgabe zur Heftreihe "Die Geschichte hinter dem Bild", Landeszentrale für politische Bildung, Thüringen.
(in Vorbereitung) Ästhetik in der Architektur
Heinrich, Michael (2025)
Keynote, LEADER-Region Tourismusverband Moststraße/ Niederösterreichische Landesausstellung, Niederösterreich.
(in Vorbereitung) Psychological needs, their aesthetic correlates and the formal answers of architecture & art
Heinrich, Michael (2025)
Vortrag, Image Neuroscience and the Arts, Neurocognitive Image Lab, International Studies University, Internationale Fachkonferenz, Shanghai, China.
(in Vorbereitung) Architekturwahrnehmung und metadisziplinäre Ästhetik
Heinrich, Michael (2025)
Kontaktkreis der Münchner Architekturverbände, Bayerische Architektenkammer, München.
Subjektive Vorstellungen von Studierenden zu Nachhaltigkeit im Unternehmenskontext – Eine qualitative Analyse im Vergleich zwischen KI und Experteneinschätzung
Schadt, Christian; Michaelis, Christian; Hufnagl, Julia; Koch, Janine; Annen, Silvia (2025)
Schadt, Christian; Michaelis, Christian; Hufnagl, Julia; Koch, Janine...
Jahrestagung der DGfE Sektion Berufs- und Wirtschaftspädagogik 2025 an der TU Darmstadt.
Peer Reviewed
Zusammenarbeit gelingt nicht von selbst - Ein digitales Kooperationsspiel mit wirtschaftsberuflichem Kontext
Heinrichs, Karin; Minnameier, Gerhard ; Schadt, Christian; Gutzwiller-Helfenfinger, Eveline (2025)
Heinrichs, Karin; Minnameier, Gerhard ; Schadt, Christian...
Jahrestagung der DGfE Sektion Berufs- und Wirtschaftspädagogik 2025 an der TU Darmstadt.
Peer Reviewed
MANDALA.ML: A Life Cycle-Centric and Role-Aware Methodology for Agile Machine Learning Projects
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.
Peer Reviewed
Animating geometry with AMD DGF
Reitter, Sander; Meyer, Quirin ; Barczak, Joshua (2025)
GPUOpen.com.
Open Access
Large Language Models for the Automated Detection and Classification of Media Bias and Propaganda to foster Media Literacy among News Audiences
Menzner, Tim (2025)
Doctoral Consortium contribution, Proceedings of the Ninth Euopean Conference on Information Literacy (ECIL'25), from 22-25 September 2025, Bamberg, Germany .
Peer Reviewed
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
Stellt euch vor, Innovation wäre ein Studiengang der Wirtschaftsinformatik
Science Slam, 6. MINT Symposium 2025, Nürnberg.
Handreichung zur Lehre mit (mathematischen) Concept Maps
Pawlowsky, Raik; Rischke, Roman; Wick , Michael (2025)
Tagungsband MINT-Symposium 2025, 395-402.
Open Access Peer Reviewed
Effizienz von Wärmepumpensystemen: Ursachen und Einfluss des Abfalls der Vorlauftemperatur
Floß, Alexander; Schaub, Michael (2025)
Moderne Gebäudetechnik 2025 (6), 47-49.
Die Auswirkungen einer 8-wöchigen Yoga-Intervention auf Angst und stressbedingte Symptome bei Brustkrebspatientinnen nach der Akutbehandlung: Protokoll einer randomisierten kontrollierten Studie
Hiller, Annika; Iser, Lilli; Schulz, Juliane; Antwerpen, Cornelia (2025)
DGMP/DGMS Kongress, Jena, Germany. .
Peer Reviewed
Prädiktoren der Effektivität VR-basierter Interventionen zur Stressreduktion: Ein systematisches Review mit Metaanalyse
Strauch, Hannah; Schuil, Isabel; Simm, Stefan; Grubert, Jens; Kalamkar, Snehanjali (2025)
DGMP/DGMS Kongress, Jena, Germany.
Peer Reviewed