fdata-02-00017_Twitter Response to Munich July 2016 Attack: Network Analysis of Influence.pdf
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Social Media platforms in Cyberspace provide communication channels for individuals, businesses, as well as state and non-state actors (i.e., individuals and groups) to conduct messaging campaigns. What are the spheres of influence that arose around the keyword #Munich on Twitter following an active shooter event at a Munich shopping mall in July 2016? To answer that question in this work, we capture tweets utilizing #Munich beginning 1 h after the shooting was reported, and the data collection ends approximately 1 month later1. We construct both daily networks and a cumulative network from this data. We analyze community evolution using the standard Louvain algorithm, and how the communities change over time to study how they both encourage and discourage the effectiveness of an information messaging campaign. We conclude that the large communities observed in the early stage of the data disappear from the #Munich conversation within 7 days. The politically charged nature of many of these communities suggests their activity is migrated to other Twitter hashtags (i.e., conversation topics). Future analysis of Twitter activity might focus on tracking communities across topics and time.
Read the peer-reviewed publication