Statistical and Computational Approaches Community

Who this Community is for

This Community is for scholars with an interest in quantitative methodologies across the full spectrum of computational and statistical approaches. It welcomes researchers working with statistical, computational, and data-intensive methods across disciplines, including advanced modelling techniques, natural language processing, generative AI, image and video analysis, and related quantitative methods used to address important academic and societal questions.

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Community focus and ethos

The Statistical and Computational Approaches Community brings together scholars interested in quantitative and computational methodologies across the full spectrum of statistical and data-driven approaches. It provides a space in which researchers from different disciplinary backgrounds can interact around shared methodological interests. Methods are approached not merely as technical tools, but as frameworks that shape research questions, analytical perspectives, and standards of evidence. By connecting researchers working with diverse quantitative approaches—from traditional statistics to AI-based methods—the Community supports the discovery of applications beyond disciplinary boundaries and the development of joint projects that draw on complementary expertise.

The Community plays a central role in shaping and coordinating the Community-led Statistical and Computational Approaches track at the MethodsNET Conference. The track highlights cutting-edge methodological developments and empirical applications, while fostering critical discussion of theoretical foundations, reproducibility, and ethical challenges in computational and data-intensive social research.

Beyond the conference, the Community supports ongoing collaboration and knowledge sharing through webinars, newsletters, and other year-round activities. Particular attention is given to supporting students and early-career researchers as they navigate a rapidly evolving methodological landscape, by creating opportunities to connect with experts, ask questions, and receive guidance on specific techniques. Whether engaging with machine learning algorithms, implementing advanced statistical models, or exploring AI-powered analysis, the Community provides support for this learning journey.

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Topics relevant for submissions

• Advancements and Applications of LLMs for Social Science – Innovative technical developments in LLMs, including model pre-training, fine-tuning, and evaluation; empirical applications for classification, similarity, question-answering, and other tasks using open and proprietary models; leveraging LLMs to expand qualitative research.
• Multilingual and Cross-Cultural Applications – Research examining the application of LLMs across different languages, contexts, and historical periods; analyzing cultural and language evolution across time and addressing associated methodological challenges.
• Networks and Social Systems – Network analysis, social information diffusion, and digital communication dynamics.
• Multimodal Social Data – Text, images, audio, and video as social and cultural data; data integration and triangulation.
• Digital Data Collection and Measurement – Novel digital traces, biased/incomplete observational data, data quality, and sampling.
• Socio-Technical Systems and Human–Machine Interaction – Algorithmic decision-making, collaborative filtering, and human–AI systems.
• Algorithmic Accountability, Ethics, and Governance – Fairness, bias, trustworthiness, inclusivity, and research ethics.
• Theory, Epistemology, and Reproducibility in Computational Social Science – Theoretical foundations, scientific norms, and reproducible research.
• Computational Social Science – The application of computational methods to study social phenomena, covering topics like inequality, segregation, and online platforms.
• Social Complexity and Agent-based Modelling (ABM) – Modelling and simulation of collective behavior and opinion dynamics.
• Urban Complex System –  A dynamic network of interacting social, economic, infrastructural, and environmental components whose collective behavior produces emergent patterns such as urban mobility, land use and housing markets, infrastructure networks, innovation and productivity, environmental sustainability, and resilience to shocks.

Co-convenors

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