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A Quantitative Social Network Analysis of Politicians’ Tweets to Explore Political Communication

Authors:
Heidi Schuhbauer
Sebastian Schötteler
Johannes Niu
Bernhard Schiffer
David Wolfarth

Keywords: Cluster Analysis; Microblog; Network Metrics; Sentiment Analysis; Social Network Analysis

Abstract:
This paper illustrates the practical application of cluster analysis, social network analysis and sentiment analysis in a case study. 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. 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. 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.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2022

Publication date: October 16, 2022

Published in: conference

ISSN: 2326-9294

ISBN: 978-1-68558-002-5

Location: Lisbon, Portugal

Dates: from October 16, 2022 to October 20, 2022