A milestoneread more
The nerdosphere is in a minor tizzy over a putative bias in IMDb ratings for the new (2016) Ghostbusters film. It seems a bit odd to me, since IMDb ratings have always been horribly 'biased': If the question you are trying to answer is "If I am forced to watch this randomly selected movie, will I like it?", then IMDb ratings, and most aggregated movie ratings are difficult to interpret, very likely 'biased'. The typical mechanism by which a rating ends up on IMDb is that a person somehow gains an awareness of the film (this has been the major problem for studios since the end of the studio-theatre model seventy years ago), enough so to view the film; they are then more likely to rate the movie if they liked it, or liked it more than expected it, or really hated it. Those who had low to middling opinions of the film are less likely to rate it, and so you have the problem of missing data, without the simplifying assumption of "missing at random."
The Ghostbusters argy bargy (or one of them) is that reviews are suspected to be coming from people who have not seen the movie. This is possibly a problem for all reviews on IMDb, though less so for reviews appearing in streaming services, who know when you have seen a film. (The other argy bargy is that sexist and racist jerks have been harassing stars of the new film.) The analysis on five thirty eight is informative, but uses information (e.g. age and sex of the reviewers) that is not widely available, and which is volunteered by the reviewers. Given the IMDb mirror at my disposal, I can look for systematic biases for films based on sex, and will do so here.
I …read more
I recently looked at IMDb ratings for Robert De Niro movies, finding slight evidence for a dip in ratings in his third act. I noted then that the data were subject to all kinds of selection biases, and that even in a perfect world would only reflect the ratings of movies that De Niro was in, not of his individual performance. I speculated that older actors might no longer be offered parts in good movies. This is something that can be explored via the IMDb mirror at my disposal, but only very weakly: if actors 'stopped caring' after a certain age, or declined in abilities, or even if IMDb raters simply liked movies with more young people, one might see the same patterns in the data. Despite these caveats, let us press on.
First, I collect all movies which are not marked as
Documentary in the data,
and which have a production year between 1965 and 2015, and have at least 250
votes on IMDb. This does present a selection bias towards better movies in the
earlier period we will have to correct for. I then collect actors and actresses
with a known date of birth who have featured in at least 30 of these films.
I bring them into R via
dplyr, and then subselect to observations where
the actor was between 18 and 90 in the production year of the film. This should
look like a lot of blah blah blah, but you can follow along at home if you
have the mirror, which you can install yourself.
library(RMySQL) library(dplyr) library(knitr) # get the connection and set to UTF-8 (probably not necessary here) dbcon <- src_mysql(host='0.0.0.0',user='moe',password='movies4me',dbname='IMDB',port …