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\begin{document}

\title{Visual Analysis of Twitter Data to Support Decision-Making in Law
Enforcement: An Analytical Study of COVID-19}



\author[1]{Mamdooh Abdelmottlep}%
\author[2]{Muhammad Abdul Razzaq}%
\author[2]{Yousra Hassaan}%
\affil[1]{Naïf Arab University for Security Sciences (NAUSS)}%
\affil[2]{Affiliation not available}%


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  \date{June 14, 2024}


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\begin{abstract}
The COVID-19 epidemic constituted a crisis for health facilities in
2020. This was due to less medical staff available, degrading employment
conditions, and higher death rates. These conditions led to tweets
(messages posted on Twitter) launching hashtags titled
\#In\_solidarity\_with\_the\_Egyptian\_doctors (\#mtDmn\selectlanguage{english}\_m'\selectlanguage{english}\_'Tb\selectlanguage{english}\_mSr
\selectlanguage{english}) to urge medical staff in Egypt to strike for better working
conditions. This resulted in less medical care being provided and
threats to public security. This study addresses the visual analysis of
``Twitter platform'' data during the COVID-19 pandemic in Egypt in April
2020 to test documented mechanisms to process mass data and identify
accounts that lead the public opinion-gathering processes on Twitter. It
analyzes the hierarchical structure and their ideological belonging. The
study uses the URL Decoder/Encoder tool to transfer Arabic hashtags into
codec symbols. The study deduced that dialogue clusters on Twitter
formed Community Cluster Networks in the study sample. Findings proved
significant in determining the accounts leading the public
opinion-gathering process. They were recognized through the coordination
and arrangement function, as well as the hierarchical structure of the
group and their intellectual and ideological tendencies. Finally, the
study confirmed the increase of decision makers' opportunities in
gathering accurate information and producing high-quality inferences
when using multiple open-source analytical tools, especially information
visual analysis tools.\\%
\end{abstract}%



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