A Study On The Centrality Measures To Determine Social Media Influencers Of Food-Beverage Products In Twitter
Keywords:Social Media Influencer, Social Network Theory, Centrality Measures, Food and Beverage
In these recent years, many people have been influenced by stars, celebrities, and influencers of social media platforms in promoting their online products. Many celebrities who have many followers would start to promote certain products, especially food and beverage to all their audiences, fans and followers. The influenced audiences, fans and followers have to share and show with their close friends and family members to gain more popularity among other audiences. Celebrities have played a significant role in corporate brands as they manage to promote the brand products and also attracted many people who are interested in purchasing the products. Therefore, the group of celebrities can be called social media influencers (SMI).
The previous study has reported that food products are top interest for Gen Z and millennials (Hanifawati, Dewanti, & Saputri, 2019). Many of Gen Z prefer to spend their money to buy food and beverage (Cheung, Davis, & Heukaeufer, 2017). Pricing strategy and providing variety are effective approaches for food and beverage. Gen Z has higher engagement with brands on social media compared to millennials. Either Gen Z or millennials are generally used multichannel to engage with the brands (Hanifawati et al., 2019).
In social network analysis (SNA), a node value influential a network called centrality. Centrality is defined as a value that represents how many connections are from nodes to other nodes (Wasserman & Faust, 1994). There are many methods to define centrality to identify the effect of each node in a social network such as Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), and Eigenvector Centrality (EC). Among these, the eigenvector centrality will give the most influential node in a network. A node with the highest eigenvector value among the other nodes is the most influential/important node in a network.
Data was collected from Twitter using the Twitter API with the hashtag #pizzzahut. The goal of this research is to identify the main influencer in the Twitter community. It applied the eigenvector centrality to observe the effect of the centrality value for Twitter data. The result shows that there is a significant difference among the 3 most influential users. This result will be used for future research that will be focused on small and medium enterprise (SME) Twitter data.
This research is held a comparison analysis between the 4 centrality measurements approach for determining the most influential user with social network Twitter as its case study.