Identifying Movies Influencers using Social Network Analysis - Sriphromting, Nantana; Luenam, Pramote (Journal of Applied Statistics and Information Technology, 2019)
This research aims to analyze the social networks and identify movie influencers on Twitter by applying graph theory and centrality scores. The researcher collected tweets related to movies that were released in Thailand from February 2018 to May 2018. There were 84,663 tweets, 2,536,270 retweets, 3,402 mentions, 2,870 replies and 369,396 accounts in total. Then the researcher analyzed social networks with directed and un-direct graphs. The researcher calculated the centrality scores to rank influencers by In-degree and Betweenness scores and statistically analyzed by spatial statistics inference using correlation analysis (Pearson Product Moment Correlation and Spearman’s correlation coefficient) and applied Independent Samples T-Test to study the influence of tweets on movie revenues. The result of social network simulation with graph theory shows a correlation between 15,954 groups in network and group size and the number of user tweets that are the center of the group. The result of correlation analysis shows the number of tweets is highly correlated with movie revenues. In addition, the number of tweets created by an individual account has more influences towards movie revenue than the number of tweets created by a corporate account.