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Visualizing hidden communities of interest: A case-study analysis of topic-based social networks in astrobiology

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Abstract

Author networks in science often rely on citation analyses. In such cases, as in others, network interpretation usually depends on supplementary data, notably about authors’ research domains when disciplinary interpretations are sought. More general social networks also face similar interpretation challenges as to the semantic content specificities of their members. In this research-in-progress, we propose to infer author networks not from citation analyses but from topic similarity analyses based on a topic-model of published documents. Such author networks reveal, as we call them, “hidden communities of interest” (HCoIs) whose semantic content can easily be interpreted by means of their associated topics in the model. We use an astrobiology corpus of full-text articles (N = 3,698) to illustrate the approach. Having conducted an LDA topic-model on all publications, we identify the underlying communities of authors by measuring author correlations in terms of topic distributions. Adding publication dates makes it possible to examine HCoI evolution over time. This approach to social networks supplements traditional methods in contexts where textual data are available.

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Abbreviations

HCoI:

Hidden communities of interest

SNA:

Social network analysis

LDA:

Latent Dirichlet analysis

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Acknowledgements

C.M. acknowledges funding from Canada Social Sciences and Humanities Research Council (Grant 430-2018-00899) and Canada Research Chairs (CRC-950-230795). F.L. acknowledges funding from the Canada Social Sciences and Humanities Research Council (756-2024-0557) and the Canada Research Chair in Philosophy of the Life Sciences at UQAM. The authors thank the audience of ISSI 2023 for most helpful comments on an earlier version of this paper published in the conference proceedings as: Malaterre, C., & Lareau, F. (2023). Visualizing hidden communities of interest: A preliminary analysis of topic-based social networks in astrobiology. Proceedings of ISSI 2023. The 19th Conference of the International Society for Scientometrics and Informetrics, Bloomington, IN.

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Authors and Affiliations

  1. Département de philosophie, Université du Québec à Montréal (UQAM), MontrealMontréal, Canada

    Christophe Malaterre

  2. Centre interuniversitaire de recherche sur la science et la technologie, Université du Québec à Montréal (UQAM), Montréal, Canada

    Christophe Malaterre

  3. Département d’informatique, Université du Québec à Montréal (UQAM), Montréal, Canada

    Francis Lareau

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  1. Christophe Malaterre
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Contributions

Conceptualization: CM, FL; Data curation: FL; Formal analysis and investigation: CM, FL; Funding acquisition: CM; Investigation: CM, FL; Methodology: CM, FL; Project administration: CM; Resources: CM; Software: FL; Supervision: CM; Validation: CM, FL; Visualization: CM; Writing – original draft preparation: CM; Writing—review and editing: CM, FL. Both authors approved the final submitted manuscript.

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Correspondence to Christophe Malaterre.

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Malaterre, C., Lareau, F. Visualizing hidden communities of interest: A case-study analysis of topic-based social networks in astrobiology. Scientometrics 129, 6167–6181 (2024). https://doi.org/10.1007/s11192-024-05047-7

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