AUTHOR=Bermudez Ivan , Cleven Daniel , Gera Ralucca , Kiser Erik T. , Newlin Timothy , Saxena Akrati 

TITLE=Twitter Response to Munich July 2016 Attack: Network Analysis of Influence

JOURNAL=Frontiers in Big Data

VOLUME=Volume 2 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2019.00017

DOI=10.3389/fdata.2019.00017

ISSN=2624-909X

ABSTRACT=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 \textit{\#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 \textit{\#Munich}  beginning one hour after the shooting was reported, and the data collection ends approximately one month later~\footnote{The collected dataset will be posted online for public use once the research work is published.}. 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 \textit{\#Munich} conversation within seven 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.