Journal Article

Exploratory Analysis of Twitter Data to Support Decision-Making in Law Enforcement

Abdel-Razzaq, Mohamed Seif El-Din, Hassaan, Yousra Hossni Abd-Elkhalek, Abdelmottlep, Mamdooh Abdelhameed

2022

Overview

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 #ln_solidarity_with_the_Egyptian_ doctors (#Solidarity_with_doctors_ Egypt) to urge medical staff in Egypt to strike for better working conditions. This re¬sulted in less medical care being provided and threats to public security. This study addresses the exploratory 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 coordina¬tion 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 exploratory analysis tools.

• Publisher: جمعية الاجتماعيين في الشارقة
• Subject: الرعاية الطبية / فيروس كورونا "كوفيد-19" / منصة تويتر / مواقع التواصل الاجتماعي