Two-day workshop – preliminary scheduled for the week commencing 10 November 2025 – hosted at the London School of Economics and Political Science.
Computational social science (CSS) is typically perceived as an offshoot of traditional quantitative research methods with a greater emphasis on large-scale data and computationally-intensive statistics and models. However, new types of scholarship challenge this perspective, arguing that CSS should be (or already is) closer to qualitative research methodology than most researchers assume (Da ubler & Benoit, 2022; Meng, 2021; Rodriguez & Storer, 2020; Tanweer et al., 2021; Theobold et al., 2024). These debates will likely intensify as CSS leans heavily into new developments in generative artificial intelligence (AI) which appear to offer great promise for processing and analysing and collecting new forms of data, and scaling the types of analyses typically found in qualitative work (e.g. Cirone & Spirling, 2021; Chopra & Haaland, 2023; Davidson 2024; Geiecke & Jaravel, 2024; Wheeler, 2025).
This workshop will bring together scholars whose work addresses these emerging issues, moving beyond field-specific questions to develop a new methodological community with a broader shared research agenda.
Research papers dealing with the following themes are particularly encouraged:
- How innovations from CSS and the closely associated recent developments in AI can improve qualitative research methodology, for example via scaling up and automation;
- How qualitative research methodology can help address the limits of CSS, for example through research standards usually identified as qualitative such as ethics, reflexivity and contextualisation;
- The potential for combining CSS and qualitative research methods within mixed-methods research designs;
- The potential for computational qualitative methods as a new type of methodological family, for example computational discourse analysis;
- What building the bridge between CSS and qualitative research does to the question of data: from “BIG Qual” to data quality;
- The difficulties and challenges of collaborating across the CSS / qualitative divide.
Potential contributors should send a 250-word abstract to a.alejandro@lse.ac.uk and D.N.DeKadt@lse.ac.uk by 26 March 2025. A notification of acceptance will be sent by the end of April. Participants whose abstracts are accepted will be expected to draft a full manuscript in advance of the workshop. Each paper will be read ahead of time by workshop participants and will receive close feedback and discussion in dedicated hour-long sessions. Some of the manuscripts presented at the workshop will be invited for submission as a special issue to a strong methodology journal.
This workshop is co-sponsored by the Department of Methodology (LSE) and the Data Science Institute (LSE). A limited number of travel and accommodation bursaries may be available for PhD students, junior scholars, and scholars from the Global South. Please specify whether you would like to be considered for this funding when submitting your abstract.
Workshop organisers:
Dr Audrey Alejandro is an Associate Professor in Qualitative Text Analysis at the Department of Methodology, London School of Economics and Political Science. She researches the politics of knowledge and discourse in society and international politics. Her work has been published in journals such as International Journal of Qualitative Methods, Qualitative Inquiry, PS: Political Science and Politics, European Journal of International Relations, International Studies Review, European Political Science, and Global Studies Quarterly.
Dr Daniel de Kadt is an Assistant Professor in Quantitative Research Methods at the Department of Methodology, London School of Economics and Political Science. He researches elections and political behaviour in the context of emerging democracies. His work has been published in journals such as the American Political Science Review, British Journal of Political Science, Journal of Politics, Nature, and Political Science Research and Methods.