AI News, Catching Star Wars surprises and other spoilers with Machine Learning

Catching Star Wars surprises and other spoilers with Machine Learning

Like many postdocs and grad students, when I wasn’t trying to discover the basic laws of matter (i.e., debugging my code), I spent a lot of time surfing the Internet.

I chose Tumblr for three reasons: To explain what I mean by 3, here is what a Star Wars: The Force Awakens post might look like on Tumblr, with the spoiler text redacted: Despite posting spoilers, this Tumblr user has followed an important informal community rule: in the hashtags below the post, they’ve added “#star wars spoilers” and “#tfa spoilers”.

This allows other users to avoid spoilers, usually via a Chrome Extension like Tumblr Savior that can block pages containing user-specified phrases (in this case, obviously, “star wars spoilers” and variants).

My goal was to find a set of features which could best describe these spoiler-labeled posts, and then to train a model to pick up the “spoileriness” of unlabeled posts much like the above one.

Using the scikit-learn python library, I made my first set of features by calculating the frequency of each of the 500 most common words in the body of the posts (alsoweighted by the words’ inverse frequencies in the dataset, to reduce the importance of words like “Jedi” appearing in nearly every post in both classes).

To validate and test this performance, I held back a sample of both spoiler and non-spoiler posts, with all spoiler labeling removed — in other words, I made my own unmarked spoilers.

By selecting posts using various cutoff values for this predictor, here’s what the resulting True Positive and False Positive rates look like for the test sample: For a cutoff which picks up 80% of the original spoilers (the true positives), the algorithm mis-identifies 25% of the non-spoiler posts as spoilers (the false positives).

You’ll get back a page of search results, with links to individual blog posts, the date of the posting, a column saying whether or not the post was tagged as a spoiler, and the results of FanGuard, i.e.

In the drop-down menu, you have access to a spoiler filter not just for Star Wars, but for seven other movies, TV shows, and games taken from lists of the most popular reblogged content on Tumblr in 2015.

The “How careful should I be?” buttons give different levels of filtering, catching 60%, 80% (default), and 90% of spoilers, but with increasing false positive rates.

First, the most predictive feature of a spoiler by far is the total number of words in a post, with spoiler posts being on average over twice as long as non-spoiler posts.

Here is a graph of the most important features appearing in at least three of the models (the variable averaged on the y-axis is a numerical measure of the classification power of a feature in the Random Forest): When they talk about spoilers, Tumblr users seem to be employing a common underlying vocabulary and grammar, regardless of the movie, TV show, or game.

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