Signal Extraction from IMDb Ratings and Metadata
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Authors
McConnell, Jan
Issue Date
2026
Type
Capstone
Language
en
Keywords
audience engagement , IMDb ratings , production metadata , statistical analysis , decision support
Alternative Title
Abstract
The Internet Movie Database (IMDb) provides large, public relational datasets widely used in applied analytics. IMDb ratings and vote counts serve as compressed signals of audience engagement, aggregating individual evaluations into numeric measures. This project examines how production attributes such as genre, release period, and cast and crew roles relate to observed engagement patterns. A relational dataset is constructed using SQL, followed by exploratory and statistical analysis in Python and R. Results indicate a positive but variable relationship between ratings and vote volume, with substantial dispersion across genres and release periods. These findings suggest that ratings reflect evaluative intensity while vote counts capture participation scale, and that both are influenced by structural factors beyond perceived quality. The study highlights limitations in treating IMDb metrics as direct indicators of preference and supports their use as context-dependent signals in analytical and decision-support settings.
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Citation
Publisher
License
Attribution-NonCommercial-NoDerivs 3.0 United States
openAccess
openAccess