Networks, Communication, and Culture in Online and Offline Worlds

Joint Conference of the Section on Sociological Network Research in the German Sociological Association (DGS) and the Working Groups on Digital Networks and Networks and Culture in the German Network Research Association (DGNet) 

19.-20.03.2026 at GESIS – Leibniz Institute for the Social Sciences, Mannheim

Organizors: Lydia Repke, Malte Doehne, Jan Fuhse, Haiko Lietz, Philip Roth

The spread of Internet-based communication has transformed the conditions for developing and maintaining personal relationships. Early studies show that this fundamentally affects how relationships emerge and exert influence (Roth & Laut-Berger, 2025). This concerns both the objective conditions and demands of communication — such as extended accessibility, new modes of expression, or digital representations of embodiment — and the cultural norms structuring relationships like friendships and, consequently, the quality of these ties. Against this background, questions arise about the consequences of specific Internet-based communication spaces (e.g., games, social media, video calls) and of new interaction partners such as AI-driven chatbots for relationships and networks. 

The Internet also provides access to a wealth of new data through APIs, web scraping, and proprietary databases, which can be analyzed using network-analytic methods (Foucault Welles & Gonzáles-Bailón, 2020). Researchers have investigated networks of emails and mailing lists, links between blogs, interactions in online games, the dynamics of online dating, and networks on social networking sites such as Facebook, Instagram, and Twitter/X. These diverse spaces of online communication are based on relational data connecting accounts or profiles, allowing for non-reactive studies of networks — for instance, based on emails, hyperlinks, Facebook friendships, or retweets.

Many of these data points — such as emails or retweets — do not represent stable relationships over time but rather momentary events. They reflect communication, not enduring relational structures. This challenges conventional network analyses but opens new avenues, from process modeling with regression analyses and relational event models to the study of relationship structures in regular communication (Kitts & Quintane, 2020). Researchers draw on such digital traces to infer the dynamics and structures of social interaction.

At the level of relations or edges, network research increasingly moves beyond relationship data derived from sociometric self-reports in surveys, using instead non-reactive measures (e.g., Facebook friendships) — or even dissolving the concept of relationships into interactions and communicative events. This gives temporality a new significance: relational dynamics unfold between momentary interactions and enduring relationships, which themselves are embedded in social situations (Doehne et al., 2024). How do such developments reshape analytical methods and possibly our basic understanding of what networks are? Furthermore, we must explore the possibilities and limits of qualitative methods: How can ethnographic observation, qualitative interviews, and text analysis be used to investigate new socio-cultural networks in and around the Internet?

At the level of actors or nodes, the question arises of how online and offline worlds intertwine. Artificial actors are increasingly common: social media bots that influence discussions, virtual avatars in gaming worlds, chatbots in customer service, or algorithmic agents steering platforms. These hybrid constellations require relationships, trust, and power relations between humans and machines to be continually negotiated. For network research, this means developing new approaches that account for the interplay between human and artificial actors.

Beyond online communication per se, the Internet provides numerous databases — for example, on film productions or scientific publications — that enable innovative network analyses (Lutter, 2015; Burgdorf et al., 2024; Heiberger et al., 2021). Many such studies explore culture as systems of meaning that vary across contexts and evolve over time. Recent work combines network analysis with quantitative text analysis (e.g., topic models or word embeddings). Some approaches even treat text itself as a network of co-occurring words (Diesner et al., 2012). This integration enables a synthesis of social relations and cultural structures — for example, in the analysis of socio-semantic networks (Roth & Cointet, 2010). These non-reactive archival data, composed of events such as letters or publications, provide fundamentally different information than network surveys. This calls for new measurements, analytical methods, and conceptualizations of what networks represent.

Burgdorf, K., Wittek, M., & Lerner, J. (2024). Communities of style: Artistic transformation and social cohesion in Hollywood, 1930 to 1999. Socius, 10, 1–15. https://doi.org/10.1177/23780231241257334 

Crossley, N., & Widdop, P. (Eds.). (2025). Handbook of social networks and culture. Edward Elgar. 

Diesner, J., Carley, K., & Tambayong, L. (2012). Extracting socio-cultural networks of the Sudan from open-source, large-scale text data. Computational and Mathematical Organizational Theory, 18, 328–339. https://doi.org/10.1007/s10588-012-9126-x 

Doehne, M., McFarland, D., & Moody, J. (2024). Network ecology: Tie fitness in social context(s). Social Networks, 77, 180–196. https://doi.org/10.1016/j.socnet.2023.09.005 

Foucault Welles, B., & Gonzáles-Bailón, S. (Eds.). (2020). The handbook of networked communication. Oxford University Press. 

Heiberger, R., Munoz-Najar Galvez, S., & McFarland, D. (2021). Facets of specialization and its relation to career success: An analysis of U.S. sociology, 1980 to 2015. American Sociological Review, 86, 1164–1192. https://doi.org/10.1177/00031224211056267 

Kitts, J., & Quintane, E. (2020). Rethinking social networks in the era of computational social science. In R. Light & J. Moody (Eds.), The Oxford handbook of social networks (pp. 71–97). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190251765.013.24 

Lewis, K. (2024). Digital networks: Elements of a theoretical framework. Social Networks, 77, 31–42. https://doi.org/10.1016/j.socnet.2021.12.002 

Lutter, M. (2015). Do women suffer from network closure? The moderating effect of social capital on gender inequality in a project-based labor market, 1929 to 2010. American Sociological Review, 80, 329–358. https://doi.org/10.1177/0003122414568788 

Roth, C., & Cointet, J.-P. (2010). Social and semantic coevolution in knowledge networks. Social Networks, 32, 16–29. https://doi.org/10.1016/j.socnet.2009.04.005 

Roth, P., & Laut-Berger, C. (2025). Videotelefonie & soziale Nähe. In H. Friese, M. Nolden, & M. Schreiter (Eds.), Handbuch soziale Praktiken und digitale Alltagswelten (2nd ed., pp. 1–16). Springer VS. https://doi.org/10.1007/978-3-658-08460-8_89-1