The Social Pattern Recognition Lab (SPaRe Lab) pursues interdisciplinary research on the formalisation, modelling, representation and visualisation of qualitative social analyses. The latter generate rich, in-depth data, yet it often fails in formalising them (e.g. via ontological analysis), in providing a synthetic model, in making datasets statistically inspectable and/or computable. This shortcoming reduces the potential impact, circulation, and exploitation —across disciplinary communities— of those analyses. A thorough consideration of the forms of presentation and communication of qualitative data holds enormous potential for the diffusion, valorisation and impact of social research. The SPaRe Lab aims at positioning itself as a reference for scholars interested in developing techniques, artefacts, and modalities for the broader deployment of micro-sociological theories, analyses and findings.

Qualitative social research data and methods range from ethnography, to conversation analysis and ethnomethodological video-based analysis; from qualitative social network analysis, to documentary analysis; from interviews and life-history, to focus groups, to a wide range of visual methods.

Fields of application are varied:

  • Design and development of technological applications (e.g. social robotics, tele-assistance, AI). Whereas social research, and qualitative methods in particular, are by now recognised, studied and used in Human-Computer Interaction, other areas within the computer and information sciences still remain partially unaware of the potentialities of micro-sociological theories, analyses and findings. Too often, both cognition and (social) behaviour —not to mention culture— are conceptualised as individual-based phenomena (e.g. personalisation), thereby missing the interactionist layer of human life as much as of human-machine cooperation.
  • Social indicators (e.g. well-being, liveability, decent work). Recent scholarly debate is increasingly critical of composite indicators emerging out of an aggregative approach that excessively reduces the complexity and multidimensionality of socio-economic phenomena. Methods and techniques for formalising detailed, in-depth analyses of (the intersection of) such socio-economic dimensions and for making qualitative datasets statistically inspectable and/or computable could greatly impact the construction of, and discourse around, those indicators on whose basis policy decisions are made or justified.
  • Visualisation and visibility of Research Results. Here too the potential impact is high, both in terms of research dissemination within the academia, and especially in terms of "third mission" (e.g. life long learning of teachers, journalists, public administration operators, social workers, etc. —both in reading and using data, and in communicating them to fellow citizens).

A first activity of the Lab has been the ForMoRe Workshop - Formalization, Modellization, and Representation of Social Pattern Analyses. With two editions in 2023 (1st and 2nd).

The second edition consolidated emerged sinergies; a third and fourth meeting, with a smaller group of scholars, worked towards a special issue, which we hope you'll be able to read soon.

 

In the above mentioned and in other activities, the SPaRe Lab leverage the extant collaboration with the Institute of Cognitive Sciences and Technologies, Italian National Research Council (ISTC-CNR), and in particular its Laboratory of Applied Ontology (LOA), which deals precisely with knowledge formalization and ontological analysis.

Director: Chiara Bassetti

DSRS Members:

  • Lorenzo Beltrame
  • Teresa Bertotti
  • Andrea Mubi Brighenti
  • Attila Bruni
  • Andrea Cossu
  • Francesca Decimo
  • Giolo Fele
  • Ester Gallo
  • Elena Pavan
  • Barbara Poggio
  • Alessandro Sicora

Fellow Members:

  • Stefano Borgo (ISTC-CNR)
  • Enrico Blanzieri (DISI-Unitn)