Case Study 3: Consociality-based Netnography

If we choose to conduct our netnographic inquiry using the notion of consociality, focusing on interactions characterized by fleeting connections in contextual fellowships, passive approaches are of good use to allow for charting unstable social networks and vast amounts of digital data.

One advantage of passive approaches is that they can be employed more quickly and cost-effectively, compared to active approaches, for example, by using tools for automated data collection. The digital settings allow for several methods that can aid Netnography using passive approaches. One method well-suited for quantitative studies of the digital contextual fellowships focused on the concept of consociality is Social Network Analysis (SNA) which can be used to map networks. SNA offers a quantitative understanding of connections between entities, the relations between actors and the structure of these relations (Fenton & Procter, 2019). As social network analysis is discussed in some detail in lesson 2.3 of the dariahTeach-course Introduction to Data Analysis with Python (OER1), network analysis methods and advanced tools will not be reintroduced here. Instead, we will introduce two easy-to-use online tools suitable for developing key insights of particular use for netnographic research and one slightly more advanced tool that can be tested if you feel comfortable downloading and trying a slightly more challenging tool to use.

One accessible way to begin to learn more about SNA using data from Twitter is the "One million tweet map" (http://onemilliontweetmap.com). As a first exercise, visit the page and insert a word of particular interest by keyword, user (@) or hashtag (#). You will then be presented with a map visualizing information provided from geolocalized data. Consider the following questions when you analyse this visualization:
• Key countries - where are they?
• What are the main clusters in the world, if any?
• How can you understand and describe any relationships between countries?

This exercise displays one aspect of connections on Twitter by mainly visualizing "where" rather than networks. As a second exercise, we will now turn to the tool Voyant (http://voyant-tools.org). Voyant focuses on the text-based analysis of correlations. To explore correlations, you simply insert a URL or copy and paste the text. The automatically-generated analysis of the text covers frequency and correlation with various visualizations, like word clouds ("cirrus"), Links and Bubble Lines. Consider the following questions when you analyse the visualizations:
• Key terms - what are they, and why do you think it is so?
• What links and correlations between the words in the text do you find most interesting and why?

The third tool, which is slightly more advanced, is SocioViz (http://socioviz.net/). With this tool, you can conduct SNA using Twitter data by attaching values to the correlations as nodes and edges. This tool requires you to download, register, and do some tutorials. Different venues for doing SNA complement each other, and SocioViz is interesting as an example since it exemplifies social media's detailed analysis of user interactions, hashtags, and emoji co-presence. Furthermore, SocioViz analyzes semantic networks, which adds another layer to the Voyant-tool exercise. When you have acquainted yourself with SocioViz, try to study social media phenomena like #blacklivesmatter (also #blm), which user interactions exist in a certain context, and what kinds of hashtag and emoji co-presence may be identified.

This assignment has introduced you to two easy-to-use tools for doing SNA and one slightly more advanced one. Together they open discussions around central concepts such as nodes, edges, and centrality. Using this exercise, you may be able to discover changes in Twitter networks over time, consequently illustrating aspects of the fluid character of much online sociality. The exercises also open ways to identify communities and their central members, illustrating how SNA can be used in Netnography as a way to inform community-based approaches (see also Kozinets, 2020). To foster reflection and active engagement with the material generated by this passive approach, you are advised to write field notes capturing your insights and reflections while doing this assignment.

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References

• Kozinets, R.V. (2020). Netnography: The essential guide to qualitative social media research (3 ed.). Thousand Oaks, CA: Sage Publications.
• Perren, R. & Kozinets, R. V. (2018). Lateral exchange markets: How social platforms operate in a networked economy. Journal of Marketing, 82(1), 20-36. https://doi.org/10.1509/jm.14.0250