Content-based comparison of communities in social networks: Ex-Yugoslavian reactions to the Russian invasion of Ukraine - Evkoski, Bojan; Kralj Novak, Petra; Ljubešić, Nikola (Applied network science, 2023)
We discuss the added value of various approaches for identifying similarities in social network communities based on the content they produce. We show the limitations of observing communities using topology-only and illustrate the benefits and complementarity of including supplementary data when analyzing social networks. As a case study, we analyze the reactions of the Ex-Yugoslavian retweet communities to the Russian invasion of Ukraine, comparing topological inter-community interaction with their content-based similarity (hashtags, news sources, topics and sentiment). The findings indicate that despite the Ex-Yugoslavian countries having a common macro-language, their retweet communities exhibit diverse responses to the invasion. Certain communities exhibit a notable level of content-based similarity, although their topological similarity remains relatively low. On the other hand, there are communities that display high similarity in specific types of content, but demonstrate less similarity when considering other aspects. For example, we identify a strong echo-chamber community linked to the Serbian government that deliberately avoids the invasion topic, despite showing news source similarities with other communities highly active on the subject. In summary, our study highlights the importance of employing multifaceted approaches to analyzing community similarities, as they enable a more comprehensive understanding of social media discourse. This approach extends beyond the confines of our specific case study, presenting opportunities to gain valuable insights into complex social events across various contexts.