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

Abstract

Social media data provide increasing opportunities for the automated analysis of large sets of textual documents. So far, automated tools have been developed either to account for the social networks among participants in the debates, or to analyze the content of these debates. Less attention has been paid to mapping cooccurrences of actors (participants) and topics (content) in online debates that can be considered as socio-semantic networks. We propose a new, automated approach that uses the whole matrix of co-addressed topics and actors for understanding and visualizing online debates. We show the advantages of the new approach with the analysis of two data sets: first, a large set of Englishlanguage Twitter messages at the Rio + 20 meeting, in June 2012 (72,077 tweets), and second, a smaller data set of Dutch-language Twitter messages on bird flu related to poultry farming in 2015-2017 (2,139 tweets). We discuss the theoretical, methodological, and substantive implications of our approach, also for the analysis of other social media data.

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

“…Three characteristics of this approach are most important when it comes to the analysis of issue arenas. The main advantage of this network approach is that is automated, and has also successfully been applied to the mapping of a bigger data set of over 70.000 tweets on the Rio+20 meeting (Hellsten & Leydesdorff, 2017). Automated mapping of issue arenas certainly does not substitute but rather complements the more qualitative approaches that have been applied so far.…”

“…For the analysis of the Twitter message content, we applied an automated network analysis to examine the co-occurrences of @ username (passive addressees) and #hashtag (topic) networks. The tool is online available, and free for academic use (Hellsten & Leydesdorff, 2017; https://leydesdorff.github.io/twitter). This new automated tool was adjusted from the automated tool for mapping co-occurring words in text documents (Leydesdorff & Hellsten, 2006).…”

Foundations of Temporal Text Networks

“…Following this reasoning, a recent stream of research focused on combining structural and semantic data simultaneously, which led to the formalization of the socio-semantic network model [29,27,28]. Originally, socio-semantic networks were just bipartite graphs interconnecting agents (also known as actors in Social Network Analysis) with semantic objects called concepts, corresponding for example to terms, n-grams, or lexical tags.…”

“…During the last decade the socio-semantic network model has been extended to extract more valuable knowledge from social media. An illustrative example of such extension can be found in [28] where the authors propose to combine the aforementioned social and socio-semantic networks into a single model. In short, they use a single matrix representation where the diagonal sub-matrices represent the relation between the same type of entities (agents and concepts) and the off-diagonal matrices represent the relation between different ones (agent/concept and concept/agent).…”

How do academic topics shift across altmetric sources? A case study of the research area of Big Data

“…In addition to the role of social media in increasing the visibility of scholars and their work, research around SMM of science have also attempted to trace the public perceptions and opinions from online communities about specific scientific fields or topics, for instance, "climate change" (e.g., An et al 2014;Pearce et al 2014;Haustein et al 2014), "Rio+20 1 " (Hellsten and Leydesdorff 2017), and "migrant crisis" (Nerghes and Lee 2018). In a recent study, Haunschild and his colleagues (2019) explored a novel network approach to compare topics between researchers and Twitter users based on author keywords and Twitter hashtags, offering insights that publications being tweeted can clearly be distinguished from those that are not tweeted.…”