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 (#متضامن_مع_أطباء_مصر ) 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.