TY - JOUR A1 - Schuhbauer, Heidi A1 - Schötteler, Sebastian A1 - Niu, Johannes A1 - Schiffer, Bernhard A1 - Wolfharth, David A1 - Groß, Tim A1 - Kreidl, Thomas A1 - Model, Fabian T1 - Analysis of Politicians’ Tweets to Explore Political Communication with Social Network Analysis JF - International Journal On Advances in Networks and Services N2 - This paper illustrates the practical application of cluster analysis, social network analysis, sentiment analysis, and topic analysis in a case study on Twitter data. These techniques provide insights into the public communication patterns between German Members of Parliament (MPs) on Twitter around the time of the 2021 federal election. The question of this work was to determine whether a potential shift in communication towards the inaugurated “Ampel” coalition, made up of the parties SPD, Greens, and FDP, can be derived from Twitter interactions. Twitter data were collected and separated into two time slots: before and after the election. In distinct scenarios, mention, retweet, and reply interactions are first considered together and then separately. In these scenarios,the Girvan-Newman Algorithm detects clusters of MPs dependent on the interactions observed. Then, the average inbreeding homophily and other network metrics of the pre- and post-election area are compared. An additional scenario focuses on intra- and inter-party sentiments conveyed within tweet texts. In a fourth scenario, MPs are grouped according to their party affiliation, the average inbreeding homophily values of parties, and potential coalitions. A topic analysis handled relevant discussion topics between the successful coalition parties. Changes in communication behavior at these two different time slots are visible. The communication clusters of those MPs differ mostly before and after the election. The average sentiment of the parties towards each other changed positively, although no significant tendency could be derived regarding later coalition formations. A determination of the density of the network for the topics also indicates that the importance of the individual topics before and after the formation of the government has a consistent relevance. KW - Cluster Analysis; Microblog; Network Metrics; Sentiment Analysis; Social Network Analysis; Topic Analysis Y1 - 2023 SN - 1942-2644 VL - 16 IS - 1/2 ER -