Topics and Grants

Team members are either social scientists who are acquainted with formal and ICT methods, or computer scientists or modelers who have been working in social science fields. Current research interests include the study of knowledge dynamics in various communities, notably those of the digital public space, by relying on methods borrowing both to ICT sociology, social network analysis, public sphere studies, on one side, and to complex system modeling, textual corpora analysis and NLP, and AI methods, on the other side.

Two of the main focus points of the team relate to socio-semantic dynamics and algorithms, respectively supported by an ERC Consolidator grant, Socsemics and an ANR collaborative grant, RECORDS.

Socio-Semantics Dynamics

Knowledge dynamics are crucially influenced by the shape of interactions i.e., the underlying social network, and the distribution of information, specifically their joint evolution. The team is very much involved in understanding what we denote as socio-semantic dynamics, both from a descriptive viewpoint (by developing methods to appraise the combined configuration of information and interactions) and a normative viewpoint (by proposing intrinsically socio-semantic models, intertwining social and semantic dynamics).

Selected recent publications
  • Roth, C., & Basov, N. (2020). The socio-semantic space of John Mohr. Poetics, 78(1), 101437. [open access version]
  • Baltzer, A., Karsai, M., & Roth, C. (2019). Interactional and Informational Attention on Twitter. Information, 10(8), 250. [open access version]
  • Lerique, S., & Roth, C. (2018). The semantic drift of quotations in blogspace: A case study in short‐term cultural evolution. Cognitive Science, 42(1), 188-219. [publisher version]
Related grants

socsemics
Socsemics (2018-2023) is supported by an ERC Consolidator funding and is directed by Camille Roth. It aims at developing a set of integrated methods to address the possible existence of interactional and informational “bubbles” in the digital public space.
More information on the project website.

The team also collaborates with the Center for German and European studies (St-Petersburg / Bielefeld) under an RSF-funded multi-year multi-institution project called “Creation of knowledge on ecological hazards in Russian and European local communities” where socio-semantic dynamics are a focal point.

Algorithms

The team studies the effect of algorithms, from both a computational and a sociological perspective. Computationally, we focus on specific algorithmic contexts (such as video or music streaming platforms) to examine the potential behavioral biases induced by algorithmic recommendation. Sociologically, we study the socio-technical background underlying the conception of algorithms, for instance by carrying out interview-based surveys of developers to understand the implementation gap between desired algorithmic principles and actual coding practices.

Selected recent publications
  • Roth, C., Mazières, A., & Menezes, T. (2020). Tubes and bubbles topological confinement of YouTube recommendations. PloS one, 15(4), e0231703. [open access version]
  • Roth, C. (2019). Algorithmic Distortion of Informational Landscapes. Intellectica 70 (1): 97–118. [publisher version]
Related grant

RECORDS
The ANR-funded grant RECORDS (2020-2023) focuses on the understanding of practices surrounding online content platforms, and specifically in the context of musical streaming through a unique partnership with one of the major platforms in this area, Deezer.
More information on the the project website.

Miscellaneous computational social science

Selected publications
  • Gravier, M., & Roth, C. (2020). Bureaucratic Representation and the Rejection Hypothesis: A Longitudinal Study of the European Commission’s Staff Composition (1980–2013). Journal of Public Administration Research and Theory, 30(1), 4-21. [publisher version]
  • Roth, C. (2019). Digital, digitized, and numerical humanities. Digital Scholarship in the Humanities, 34(3), 616-632. [publisher version]
  • Menezes, T., & Roth, C. (2019). Automatic discovery of families of network generative processes. In Dynamics on and of Complex Networks, Volume III: “Machine Learning and Statistical Physics” (pp. 83-111). Springer, Cham. [publisher version]
  • Mazières, A., & Roth, C. (2018). Large-scale diversity estimation through surname origin inference. Bulletin of Sociological Methodology, 139(1), 59-73. [publisher version]