Understanding public interest and needs in health policies through the application of social network analysis on a governmental Facebook fan page

Abstract

Background This study analyzed the interactions between agencies, policies, and the interest of the public using a social network analysis. Methods Open data on the 2017 Facebook fan page of the Ministry of Health and Welfare (MoHW) in Taiwan, including 18,193 messages, were analyzed by conducting a social network analysis, NodeXL (Network Overview, Discovery and Exploration for Excel), creating visualized explorations using size volumes to present the degree of strength between agencies and policies to further calculate the network centrality indicators of agencies and policies. Results Agencies of the “Social and Family Affairs Administration” and “Health Promotion Administration” contributed the most policy posts. The policy of “Physical and mental health promotion” entailed the most agencies to be involved. The “Department of Nursing and Health Care” received the largest public response, for which “Long-term care” received the most public interest. Conclusions A social network analysis of fan page of Taiwan’s top level health government agency can reveal the government’s most emphasized core policies, the strength of correlations between agencies and policies, and provide an understanding of public interest toward the policies.

Construction safety knowledge sharing on Twitter: A social network analysis

“…We then calculated the graph metrics to understand the size, connectivity and the attributes of the network, based on in-degree and out-degree centrality (Hansen, Shneiderman & Smith, 2010). In-degree centrality is used to measure the importance of individuals in the network graph and can be used to investigate the information trends of the entire social network graph and the possibility of individuals controlling resources (Huang & Chiu, 2020). Its purpose is to find important individuals in the network (Chong & Kim, 2020).…”

“…If the minimum indegree is 0, it means that a vertex does not receive any replies or mentions. Out-degree centrality represents the number of relations vertices sending to other vertices (Huang & Chiu, 2020). When the minimum out-degree is 0, it means that a vertex does not send out any tweets or replies.…”