Exploring Film Industry Dynamics: A Network Science Approach to Internet Movie Database Analysis

Abstract

Throughout the history of the film industry, many people have been involved in roles like acting, directing, or even writing the storyline of a TV show or movie. A question arises: Who is the most influential person among all those people? The objective of this study is to provide an answer to this inquiry. Firstly, the Internet Online Movie Database (IMDb) was selected as the data source for this study due to its vast data volume. Furthermore, we employed network science methods to study the social networks of the film industry. To be precise, we performed network analysis where we gained valuable information from properties that relate to influence, which is called centrality measures. Three commonly used centrality measures were chosen to provide different perspectives based on the IMDB dataset, namely betweenness, closeness, and degree centrality. Moreover, we want to identify individuals with the highest scores for all centrality measures tested. In addition, the KNIME Analytics Platform tool was used to preprocess the IMDB data by implementing data integration and transformation. Subsequently, the Igraph package available in Python was utilised to obtain the centrality measure scores. The results from these methods pointed to specific nodes, which were then compared with the rating table of the IMDB dataset.

Keywords

• Network Analysis
• Centrality Measure
• IMDB
• Network Science
• Graph Theory
• Igraph