Data Set and Design flow

Author

Odejimi OLuwabukola Ikhanose

The title.crew.tsv.gz dataset from IMDb contains information about the crew members of movies and TV shows. By leveraging this dataset, we can construct various networks to explore relationships between different entities such as movies, TV shows, directors, writers, and other crew members.

Node Set(s)

Depending on the research question, the node sets can vary. Here are some possible node sets:

  1. Movies/TV Shows: Each node represents a movie or TV show.

  2. Crew Members: Each node represents a crew member (e.g., director, writer).

  3. Mixed Nodes: Nodes represent both movies/TV shows and crew members, allowing us to explore bipartite networks.

Relationships of Interest

  1. Collaboration Network:

    • Movie/TV Show Collaboration: Edges represent movies or TV shows that share common crew members.

    • Crew Member Collaboration: Edges represent crew members who have worked together on the same movie or TV show.

  2. Role-based Network:

    • Director-Writer Collaboration: Edges represent movies or TV shows directed by a director and written by a writer.

    • Multi-role Collaboration: Edges between crew members based on shared roles (e.g., someone who is both a director and a writer).

Potential Research Questions

  1. Collaboration Patterns:

    • How often do directors and writers collaborate on multiple projects?

    • What are the most common collaboration patterns among crew members in the film industry?

  2. Role Analysis:

    • Are there certain directors who frequently also take on the role of a writer? If so, how does this affect the success of the movies/TV shows they work on?

    • Which crew members are the most central in the network, indicating their importance and influence in the industry?

Workflow to Construct Network Objects

  1. Load the Data:

    • Load the title.crew.tsv.gz, title.basics.tsv.gz, and name.basics.tsv.gz datasets into R.

  2. Filter and Clean Data:

    • Filter the title.basics.tsv.gz to focus on movies and TV shows.

    • Clean the title.crew.tsv.gz to extract relevant information about directors and writers.

  3. Create Node Sets:

    • Extract unique titles and crew members.

    • Create data frames for nodes, including attributes such as title type and crew roles.

  4. Create Edges:

    • For collaboration networks, create edges between crew members based on common titles.

    • For role-based networks, create edges between titles and their respective crew members based on their roles.

  5. Construct Network Objects:

    • Use the igraph package to create network objects from the nodes and edges data frames.

    • Assign relevant attributes to the nodes and edges.

  6. Analyze and Visualize Networks:

    • Use network analysis techniques to explore centrality, community detection, and other network metrics.

    • Visualize the networks to identify patterns and relationships.