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.

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.

12 crucial Tumblr tricks you probably don’t know

Tumblr has a tips page, but most users don’t know it exists, and it only scratches the surface of useful advice—for example, the fact that you can display your tags in chronological order, or browse your dashboard using keystrokes instead of the scroll bar.

If you want to encourage direct replies on your dashboard, put a question mark at the end of a text post, and you’ll be given the option to activate replies to that post.

2) You can’t post links in askbox messages, but you can in chat Tumblr introduced a chat function in 2016, allowing one-on-one private messages between users.

If someone sends you a link to a video game test, fashion advertising blog, or modeling casting session, it’s probably spam.

Alternatively, you can move your cursor over the little person-shaped icon, and see a dropdown menu including the option to block them, send an askbox message, or subscribe to their posts on the app.

If you’re planning to post spoilers for something new and popular, it’s polite to use a spoiler tag (say, “Star Wars spoilers”) so your spoiler-phobic followers can block any relevant posts using an extension app like Tumblr Savior.

If you want to post or reblog something that runs the risk of triggering someone’s PTSD (for example, a post about rape, eating disorders, or suicide), tag it with some variation of “trigger warning”

It’s simply not possible to cover all the ins and outs of Tumblr in one tips post, but if we’ve left out your favorite tip for better tumblring, let us know on Tumblr!

Protect yourself from Avengers: Infinity War spoilers with these simple tools

Avengers: Infinity War press screenings are done, reviews written, red carpet events over — and that means the internet is about to become a cesspool of spoilers.

It’s not only helpful in quietly banishing trolls who continue to scream into the abyss, but it’s also a decent way to keep spoilers off your timeline.

If, for example, you don’t want to know anything about Hawkeye until you settle in for the movie yourself, your muted terms page could look something like: It also might not be a bad idea to mute specific accounts, like enthusiast press sites, that will be writing about the film in days to come.

Using things like a hammer (Thor), bow and arrow (Hawkeye), tree (Groot), rocket (Rocket Raccoon), the American flag (Captain America) and a spider (Spider-Man) are some ways that trolls might try to bombard people with spoilers.

Having certain emoji blocked until you see the film may seem like the nuclear option, but it’s become a necessary evil.

Here are a list of subreddits that the Marvel community is anticipating will try to ruin the movie: Besides avoiding these subreddits, the best way to ensure they don’t pop up on your front page is by going to r/all, hitting the “filter” button on the side of the page and typing in the names of these subreddits.

Other subreddits that the Marvel fandom is wary of, which you may want to add to your list of filtered subreddits just in case, are as follows: It’s a little disappointing and wholly ironic that Tumblr, a website dedicated to fandom, doesn’t have a very good, proper mute function.

Facebook’s New Snooze Feature Saves Us From Spoilers and Spoiler Culture

Facebook is rolling out a new feature that allows users to snooze keywords they don’t want to see on their timeline.

Simply click the three dots next to a post and select “snooze” to put a friend or group on a 30-day suspension from your personal feed without alerting them.

Soon, users who need a break from news about Trump will be able to snooze certain related keywords for 30 days.

Or maybe you waited years for a movie sequel only to have your favorite blogger reveal the ending?” wrote Facebook News Feed Product Manager Shruthi Muraleedharan in the post.

Whenever I write a movie review, talk about a video game, or mention a television show, one of the first comments will inevitably be about how I spoiled it without fair warning.

It used to be enough to not talk about the ending, but increasingly people don’t want to know any specific story beats or even the cast list for fear of ruining their viewing experience.

This is a good time to mention, by the way, spoilers for Mad Men and Breaking Bad follow – two shows that finished airing in 2015 and 2013, you nerds.

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