Social Network Analysis to Identify Influencer in Twitter Conversation on SMEs in Times of Covid-19 Pandemic

Abstract

Since the coronavirus pandemic spread in Malaysia, there are increase discussions on the issue related to SMEs in Malaysia. These conversations have included social media users’ assistance to SMEs. The government extended assistance to SMEs either in kind or through various programs. Twitter, a popular social network site (SNS) is the leading platform to engage in online conversation related to the SMEs enhancement and related programs. The understanding of who starts the conversation engaged into and responds to the information spread are important so as to enhance the government service and delivery of the assistance to SMEs. Hence, the objective of this study is to examine the social network metrics from Twitter data to find the influencers, URLs, and drivers of the news spreading in a selected SNS i.e., Twitter. A social network analysis and brief content analysis of Twitter data were conducted in August 2021. There are 982 networked users of Twitter collected for the analysis. The influential users were identified using social network analysis (SNA) and graph clusters for visualization. The influencers were ranked by their betweenness centrality score, and the graph’s vertices were grouped by cluster using the Clauset-Newman-Moore algorithm to visualize the network structure. The top words in conversation, top hashtags, and top URLs were also examined. The result identified the 10 most influential Tweeters, most popular hashtags, and URLs in the social network of 982 users. This study implies that SNA and its tools help for improving the information spread and sharing on the issue related to SMEs in Malaysia.