Ahead of the 8th Geography of Innovation Conference (28–30 January 2026, Corvinus University of Budapest), we invite you to a pre-conference workshop on the emerging WebAI Research in the Geography of Innovation.

The workshop is free of charge and will take place on 27 January 2026, 15:00–18:00, at ELTE Centre for Economic and Regional Studies, H-1097 Budapest, Tóth Kálmán u. 4, room K.0.11-12. (ground floor).

Advances in natural language processing provide tools to transform rich textual content on the web into structured indicators of innovation and technological orientation (Bergeaud et al., 2025). The emerging WebAI paradigm proposes a systematic use of artificial intelligence to analyze organizational web data (texts, hyperlinks, and other digital traces) as a new empirical lens on innovation systems and their geography (Dahlke et al., 2025). Accordingly, WebAI can generate large-scale, fine-grained indicators of firms’ innovation activities, technological and product profiles, and associations and relations that go beyond traditional patent, publication, and survey-based indicators – especially for smaller firms, services, and digital and “soft” types of innovation that are typically underrepresented in official statistics (Abbasiharofteh et al., 2023, Abbasiharofteh et al., 2024, Toetzke et al., 2025).

This enables new forms of spatial analysis: for example, tracing how emerging general-purpose technologies such as AI diffuse across regions (Dahlke et al., 2024). At the same time, critical work on large language models highlights the need for domain-specific model design, transparency, rigorous validation, and bias control when turning these digital traces into spatial indicators that may inform place-based innovation policy (Hajikhani & Cole, 2024). In addition, the inclusion of explicit geodata into web-derived and regional innovation analysis remains understudied (Schmidt et al., 2022, Dahlke et al., 2025). In this sense, WebAI provides both a new yet underexplored empirical lens on the geography of innovation and a methodological agenda for integrating alternative digital data with established regional science and innovation metrics.

The workshop will:
● introduce the WebAI methodology and its relevance for economic geography and the Geography of Innovation studies;
● include interactive group work where participants jointly develop future research directions, discuss methodological challenges (e.g. multi-data joints, validation, representativeness, ethics), and explore opportunities for collaboration.
● not expect participants to prepare a research presentation, but to participate actively.

We welcome participants from all disciplines represented at GEOINNO (economic geography, regional science, economics and management, sociology, network science, political science, planning, and data science) whether you already work with web data and AI, or are simply curious about their potential for your research.

The workshop will be followed by an optional evening social event in Budapest, offering additional time for informal networking and idea exchange in the city’s vibrant center.
Register until Jan 6, 2026 via this Google Form:

https://forms.gle/TEBxLoRFvNcMnwU79 

Further information & details will be available via the GEOINNO 2026 conference website. Contact: mabb@business.aau.dk; geoinno2026@uni-corvinus.hu.

REFERENCES
Abbasiharofteh, M., Kinne, J., & Krüger, M. (2024). Leveraging the digital layer: the strength of weak and strong ties in bridging geographic and cognitive distances. Journal of economic geography, 24(2), 241-262.
Abbasiharofteh, M., Krüger, M., Kinne, J., Lenz, D., & Resch, B. (2023). The digital layer: alternative data for regional and innovation studies. Spatial Economic Analysis, 18(4), 507–529.
Bergeaud, A., Jaffe, A. B., & Papanikolaou, D. (2025). Natural language processing and innovation research. NBER Working Paper No. 33821.
Dahlke, J., Beck, M., Kinne, J., Lenz, D., Dehghan, R., Wörter, M., & Ebersberger, B. (2024). Epidemic effects in the diffusion of emerging digital technologies: Evidence from artificial intelligence adoption. Research Policy, 53, 104917.
Dahlke, J. et al. (2025). The WebAI paradigm of innovation research: Extracting insight from organizational web data through AI. ZEW Discussion Paper No. 25-019.
Hajikhani, A., & Cole, C. (2024). A critical review of large language models: Sensitivity, bias, and the path toward specialized AI. Quantitative Science Studies, 5(3), 736–756.
Schmidt, S., Kinne, J., Lautenbach, S., Blaschke, T., Lenz, D. & Resch, B. (2022). Greenwashing in the US metal industry? A novel approach combining SO2 concentrations from satellite data, a plant-level firm database and web text mining. Science of the Total Environment 835, 155512.
Toetzke, M. et al. (2025). Analyzing the dynamics of innovation networks in climate technologies using large language models. Working paper.