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Local meaning structures: mixed-method sociosemantic network analysis

Abstract

This paper proposes a mixed-method sociosemantic network analysis of meaning structures in practice. While social and institutional fields impose meaning structures, to achieve practical goals, field participants gather in groups and locally produce idiocultures of their own. Such idiocultures are difficult to capture structurally; hence, the impact of practice on meaning structures is underrated. To account for this impact, we automatically map local meaning structures—ensembles of semantic associations embedded in specific social groups—to identify the focal elements of these meaning structures, and qualitatively examine contextual usage of such elements. Employing a combination of ethnographic and social network data on two St. Petersburg art collectives, we find the seemingly field-imposed meaning structures to be instantiated differently, depending on group practice. Moreover, we find meaning structures to emerge from group practice and even change the field-wide meaning structures.

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Notes

  1. 1.

    While this study focuses on meaning structures in the framework of social fields, we keep in mind that group culture is more than classifications related to fields. It is also shared stories, common points of concern, relational expectations (White 1992; Fuhse and Mützel 2011; Godart and White 2010), and so on. These are not only molded by the interplay of fields and practice, but also affected by socioeconomic and neighborhood differences, personalities, and many other factors.

  2. 2.

    For a different approach to comparing meaning structures of different groups, see Bottero and Crossley (2011), Giuffre (2001, 2009), Yeung (2005) and for a comparison of cultural meanings across social positions see McLean (2007, pp. 114–119).

  3. 3.

    For alternative sociosemantic approaches, relating social networks to semantic networks after the latter were constructed, see Basov and Brennecke (2017) for multiplex networks, and Lee and Martin (2018) and Godart and Galunic (2019) for multilayer networks.

  4. 4.

    Topic models require researcher to impose the number of topics for the algorithm to find; this number is difficult to justify (see, e.g., Bail 2014). Similar to coding (including coding as part of supervised topic modeling (McAuliffe and Blei 2008), this does not suit the purpose of capturing meaning structures in practice.

  5. 5.

    Displaying the concept associations as a network, it is tempting to read sequences of associations as sentences or stories, similar to the chains of implication for personality characteristics and relationship qualities in Yeung’s analysis of communes’ meaning structures (Yeung 2005, pp. 405–407; see also Bearman and Stovel 2000). To be honest , we initially yielded to this temptation. But when we took into account the textual context of the associations, we soon discovered that it is misleading to interpret indirect associations in this type of semantic networks. As a consequence, we doubt that measures accounting for overall network structure, that is, the core of social network analysis, are useful for analyzing semantic networks of direct collocation when focusing on meaning structures. In this kind of analysis, our network visualizations are merely convenient ways of jointly presenting dyads of concepts that signal meaning structures. As a consequence, we limited ourselves to usage of dyad-based network statistics, such as degree centrality in this paper. Elsewhere, we experiment with statistical models that focus on local micropatterns of social ties and concept associations (Basov 2019).

  6. 6.

    In addition, there are good technical reasons for such a qualitative inspection. When meaning resides in the context of a sentence, it cannot be teased out automatically. For instance, consider differences in the attitude of the speaker to political performances: ‘all performances must be political performances’ in contrast to ‘political performances are senseless.’ Computer algorithm maps an association political—performances in both cases. Hence, a proper interpretation of the semantic networks requires manual checking the actual quotes from which concept associations were taken.

  7. 7.

    We refrain from using thesauri to collapse synonymous words for the very same reason we refrain from taking words in their dictionary meanings or coding: to avoid imposing meanings. Dictionary synonymy does not determine the sociolinguistic use of the words in context (Wittgenstein 1953; Labov 1972).

  8. 8.

    In Russian language, subjects, verbs, and adjectives are often to be found close to each other. For languages in which, for example, subject and verb tend to be found far apart, a larger window size may be required in order to link parts of speech important to expressing meaning structures. However, for the reasons described below, we would still recommend to refrain from using a window larger than an average sentence.

  9. 9.

    As our focus is on the artistic field, this comparison does not include Chtodelat Academics.

  10. 10.

    The capability of our approach to extract only focal shared concept associations out of thousands of concepts and associations produced by the collectives comes in handy to make the amounts of concepts, associations, and hence quotes manageable in qualitative analysis.

  11. 11.

    Here and further: The first letter in the encoded member’s name refers to the collective: P for Parazit and C for Chtodelat; the second letter was randomly assigned.

  12. 12.

    A highly visible gallery in St. Petersburg.

  13. 13.

    http://youngart.ru/en/.

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Acknowledgements

This study was supported by Russian Foundation for Basic Research (18-011-00796) and by Russian Foundation for Humanities (15-03-00722). Meetings of the authors were possible thanks to the generous support of their academic mobility by The Centre for German and European Studies (Bielefeld University, St. Petersburg State University, and German Academic Exchange Service (DAAD) with funds from the German Foreign Office). The authors express their gratitude to those who helped in data collection and processing: Maria Veits, Olga Volkova, Alexey Evstifeev, Alexander Kopiy, and Olga Nikiforova. Furthermore, our field study would not be possible without the two creative collectives which so generously agreed to let us collect data about them. The authors are grateful for comments received on this paper from Loet Leydesdorff, Dafne Muntanyola, Margarita Kuleva, and Anisya Khokhlova as well as the participants of the XIXth International Sociological Association World Congress of Sociology, 36th, and 34th Sunbelt Social Networks Conference of the International Network for Social Network Analysis, workshop ‘Network Theory and Methods: Combining Structure, Content and Meanings?,’ 12th European Sociological Association conference, 2nd and 3rd International conference: ‘Networks in the Global World’ in St. Petersburg, and 1st European Social Networks Conference. Finally, the authors thank anonymous reviewers for their insightful comments. All errors and mistakes are our own.

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Basov, N., de Nooy, W. & Nenko, A. Local meaning structures: mixed-method sociosemantic network analysis. Am J Cult Sociol 9, 376–417 (2021). https://doi.org/10.1057/s41290-019-00084-9

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Keywords

  • meaning structure
  • field
  • interaction
  • practice
  • mixed method
  • art collective