Active and passive stakeholders in issue arenas: A communication network approach to the bird flu debate on Twitter

Abstract

Issue arenas, as places for societal discussions, have recently been studied as an important aspect of organizational environments. While a fundamental part of any issue arena is the distinction between active and passive actors, empirical analyses have mainly focused on active stakeholders. We approach issue arenas as communication networks in which active stakeholders discuss topics and involve passive stakeholders. Based on network theory, we introduce an automated method for mapping these issue arenas on Twitter. In particular, we combine manual coding of active stakeholders, and automated semantic network analysis of addressed, passive stakeholders and their topics of discussion. Empirically, we focus on the issue of bird flu affecting poultry farming in the Netherlands from 2015 to 2017 with a sample of 704 Twitter messages. Instead of pre-defining a set of stakeholders for the analysis, our approach to study communication networks in online settings allows for mapping issue arenas based on the stakeholders that communicate about the topic.

Automated analysis of actor–topic networks on twitter: New approaches to the analysis of socio‐semantic networks

“…In conclusion, this approach can be applied to a wide range of theoretical traditions in the communication sciences, such as research into issue arenas (Hellsten, Jacobs & Wonneberger, 2019) as well as stakeholder analysis by focusing on the co-mentioning of actors in news media, social media, and organizational media in general. Although we applied the method to the Twitter messages under study, the approach can also be applied to, for example, scientific publications where subject headings or keywords (meta-data) can be considered as #hashtags and actors cited in the texts as @usernames.…”

“…Such organizations are positioned on the periphery of the map due to their lesser role in the Twitter discussion on bird flu. see also Hellsten, Jacobs and Wonneberger (2019).…”

“…In total, #hashtags were used 2,368 times, and @usernames 1,182 times in the data set of 2,139 tweets. For a more detailed analysis of a sample of 704 tweets using this method, see Hellsten, Jacobs and Wonneberger (2019).…”

“…There were two peaks in the number of tweets during this period, in December 2015 related to new cases of the disease in poultry farms in France, and in November-December 2016, related to cases in The Netherlands (Hellsten, Jacobs, & Wonneberger, 2019). For pragmatic reasons, to limit The number of nodes in the resulting visualizations roughly to 100 nodes, we set the threshold to hashtags and usernames that appear five or more times in the data set.…”

“…The analysis can be further elaborated, for example, by selecting tweet authors who have frequently posted on the issue, and then focusing on the co-occurring usernames and hashtags in the tweets by a specific active Twitter user, or organization, authoring Twitter messages (Hellsten, Jacobs & Wonneberger, 2019). This further refining is particularly useful in the case of large and heterogeneous data sets, such as the Twitter messages during an international meeting.…”