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Exploring film industry dynamics: a network science approach to Internet movie database analysis / Muhammad Izzat Farid Musaddin

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...

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Main Author: Musaddin, Muhammad Izzat Farid
Format: Article
Language:English
Published: UiTM Cawangan Perlis 2024
Subjects:
Analysis
Online Access:https://ir.uitm.edu.my/id/eprint/103965/1/103965.pdf
https://ir.uitm.edu.my/id/eprint/103965/
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spelling my.uitm.ir.1039652024-10-18T09:45:58Z https://ir.uitm.edu.my/id/eprint/103965/ Exploring film industry dynamics: a network science approach to Internet movie database analysis / Muhammad Izzat Farid Musaddin jcrinn Musaddin, Muhammad Izzat Farid Analysis 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. UiTM Cawangan Perlis 2024-09 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/103965/1/103965.pdf Exploring film industry dynamics: a network science approach to Internet movie database analysis / Muhammad Izzat Farid Musaddin. (2024) Journal of Computing Research and Innovation (JCRINN) <https://ir.uitm.edu.my/view/publication/Journal_of_Computing_Research_and_Innovation_=28JCRINN=29/>, 9 (2): 27. pp. 332-347. ISSN 2600-8793
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Analysis
spellingShingle Analysis
Musaddin, Muhammad Izzat Farid
Exploring film industry dynamics: a network science approach to Internet movie database analysis / Muhammad Izzat Farid Musaddin
description 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.
format Article
author Musaddin, Muhammad Izzat Farid
author_facet Musaddin, Muhammad Izzat Farid
author_sort Musaddin, Muhammad Izzat Farid
title Exploring film industry dynamics: a network science approach to Internet movie database analysis / Muhammad Izzat Farid Musaddin
title_short Exploring film industry dynamics: a network science approach to Internet movie database analysis / Muhammad Izzat Farid Musaddin
title_full Exploring film industry dynamics: a network science approach to Internet movie database analysis / Muhammad Izzat Farid Musaddin
title_fullStr Exploring film industry dynamics: a network science approach to Internet movie database analysis / Muhammad Izzat Farid Musaddin
title_full_unstemmed Exploring film industry dynamics: a network science approach to Internet movie database analysis / Muhammad Izzat Farid Musaddin
title_sort exploring film industry dynamics: a network science approach to internet movie database analysis / muhammad izzat farid musaddin
publisher UiTM Cawangan Perlis
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/103965/1/103965.pdf
https://ir.uitm.edu.my/id/eprint/103965/
_version_ 1814058504401453056
score 13.211869

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