A Social Network Analysis: Identifying Influencers in The COVID-19 Vaccination Discussion on Twitter

Abstract

Social media analytics, especially Twitter, has experienced significant growth over the last few years. The data generated by Twitter provides valuable information to many stakeholders regarding user behavior, preferences, tastes, and characteristics. The presence of influencers on social media can invite interaction with other users. An influencer can affect the speed of spreading information on social media. This study looks at the influence of influencers and information dissemination channels on Twitter data related to COVID-19 vaccination in Indonesia as one of the hot Twitter discussion trends. This study applies Social Network Analysis (SNA) as a theoretical and methodological framework to show that interactions between users have differences on the network when the analyzed tweets are divided into mention and retweet networks. This study found that the key accounts in disseminating information related to Covid-19 vaccination were dominated by official accounts of government organizations and online news portals. The official Twitter account of government organizations turns out to have an essential role in disseminating information related to COVID-19 vaccination, namely the @KemenkesRI account belonging to the Ministry of Health of the Republic of Indonesia and the @Puspen_TNI account belonging to the TNI Information Center.

The Development of Social Media Intelligence System for Citizen Opinion and Perception Analysis over Government Policy

“…Metrik citizen engagement index ini kemudian menjadi landasan untuk penelitian-penelitian yang lebih aplikatif terkait dengan analitik media sosial, khususnya antara pemerintah dengan masyarakat [20]- [22]. Diluar metrik tersebut beberapa penelitian langsung mengkalkulasi tiga aspek eksplisit yakni comment, likes dan share untuk menghitung engagement [23], [24] maupun comment dan share saja [25] untuk mengkalkulasi engagement masyarakat pada akun media sosial instansi pemerintahan.…”

Social Network Analysis of Twitter Users on BTS Topic Using Degree Centrality, Betweenness Centrality, and Closeness Centrality

“…Furthermore, this study applied to identify influencers on Facebook [7], [8] or Twitter [5], [6] and many other social media platforms. Some recent studies allow this method to be applied in a variety of issues such as to analyze coauthorship networks in academic issues [9], to identify opinion leaders in political issues on the Twitter social network [10], [11], to study the influential actors in a network in the dissemination of information related to vaccines of covid 19 on Twitter conversation [12], [13], to analyze a brand marketing network in economical issue [4], [14], etc. So, in this study, degree centrality, betweenness centrality, and closeness centrality were applied in the case of influencer identification.…”

Retweet Predictions Regarding COVID-19 Vaccination Tweets through The Method of Multi Level Stacking

“…According to the previous Adrian research, this research used the Multi-Level Stacking method to predict retweets information related to COVID-19 vaccination tweets in Indonesia on Twitter social media using user-based and timebased features (Habibi & Cahyo, 2021). Through combining the Decision Tree, KNN, and Naive Bayes algorithms in the Multi Level Stacking method, it is expected to produce good performance.…”