Automated Analysis of Actor-Topic Networks on Twitter: New Approaches to the Analysis of Socio-Semantic Networks

摘要

We have proposed a new methodology for analyzing Twitter messages by focusing on the co-occurrences of Twitter-specific #hashtags and @usernames instead of the words used in the content of the Twitter messages. Our approach has the advantage of making it possible to map which users were addressed in connection with which topics. This approach helps to solve the problem of semantic networks that have been criticized for producing "bags-of-words" that remain vague in terms of meaningful interpretations. We have shown the advantages of the whole-matrix approach in providing more complete results than the bipartite 2-mode approach, in particular by also including clusters that consist of either hashtags or usernames. The bipartite 2-mode matrix tends to cut off such clusters. In addition, the whole-matrix approach allows for extending the analysis from two types of nodes into n-mode networks (n ＞ 2). As an example, we extended the analysis to a 3-mode network of authors, actors, and hashtags, and mapped the results in a single visualization (Figure 7). Using ANT, the sending authors can also be considered as attributes of the tweets. This semiotic perspective adds opportunities for researchers to focus on multiple types of nodes depending on their research questions.