- Over 3100 people block my personal account on Twitter.
- I created an algorithm to crawl Twitter and find people who block me.
- Those people appear to be mainly aspiring authors and members of the anti-Trump "resistance."
When I go on Twitter, I'm often confronted with this view:
Trying to see the unavaible tweet, I click through and see this:
I am blocked by thousands of people on Twitter. When I tell people about this publicly they often react in thinking I must be the worlds most massive troll (but I'm not). But most of these "blockers" are accounts that I've never interacted with, they have essentially blocked me either categorically or because I'm part of a massive block list.
Blocking has become a major part of the user experience on Twitter for many reasons, and that's largely out of the scope of this blog. To understand how someone ends up blocked like me; you should understand two products:
- Block together: A program that gives users the ability to share block lists and otherwise categorically ban accounts.
- Twitter Block Chain: This chrome extension is (poorly named) used to block all users of a specific account. For instance, it could be used to block anyone who follows @realDonaldTrump
After a day when I found a couple of random accounts blocking me. I realized their was a Data Science angle, specifically:
- Can I use an algorithm to scan Twitter and accounts that block me?
- Can I optimize the algorithm with Machine Learning to predict accounts likely to block me and make my initial algorithm find blockers more quickly.
The answer to these questions ended up being "yes" and "yes." Here I'll describe the results, first with a description of results and then a concept of the data science method.
THE DATA: WHO BLOCKS ME
To-date, I've used an algorithm to find about 3100 people who block me on twitter. Releasing the full list would seem to be doxxing-however the public nature of which "verified" accounts block me seems less problematic. Here's a listing with evidence of some "celebrity blocks":
Digging into the data around who blocks me, we can generally describe the nature of people who tend to block me by comparing how the words on their profiles compare to the words on the profiles of people who *don't* block me. Below is a list of those words and their indication of risk.
- People who are part of the liberal "resistance" to Donald Trump (which is a bit bizarre because I'm not a Trump supporter-though I was very anti-Bernie Sanders).
- "Geeky" authors and writers-this is speaks to the Wil Wheaton theory of my epic blocked status (see below).
And because I know everyone loves wordclouds (sarcasm), on a recent long social scan, I created wordclouds of people who block me. Here's what that looks like, first looking at those who block me:
And those who do not:
Apparently, unbeknownst to me, I am loved by dog/pet lovers (potentially an artifact of sampling, rescan method-but few of the dog-lover accounts blocked me).
HOW I ENDED UP BLOCKED
To begin my methodology, I started with a bit of a priori theory-specifically-what are the incidents that led to my blocking? I'm blocked by people across the political spectrum, from conservative politicians in my own state to "resistance" members that are famous nationwide.. and an odd number of science fiction writers (I'm not a fan, don't care). Here were my best theories of my own blocking:
- I was added to Wil Wheaton's block list after telling him to "Shutup Wesley" one too many times.
- I got into a few arguments that led to individual blocks with Bernie bros when I was pointing out some misconceptions they had about tax policy.
Wheaton's block list, as well as the block together App seemed the most likely scenario for wide-spread blocking.
HOW TO DETERMINE IN CODE THAT SOMEONE BLOCKS YOU
Figuring out that someone blocks you via the Twitter API is actually super easy. My initial scanner ran all inside of R, then I ported it to Python using the tweepy package. The basic steps:
- Try to download a user timeline for your target user.
- Catch all errors.
- Check to see if the returned error is code 136.
Here's what that looks like in general Python:
Note: This code is simply for a single user. The entire application I wrote is a full iterative search-for-user, check block status, recursively search for more users, model, repeat application in Python with a Microsoft SQL Server backend that I am not publishing in whole at this time, for various reasons.
HOW TO CHOOSE WHICH USERS TO CHECK-AT FIRST
Knowing that our end goal here is to optimize an algorithm which will tell us which users to check for blocking, we first need to gather an informed set of accounts that will tell us which types of users block us. Using the a priori theory of why users may be blocking me, I turned to two places:
- The followers of the account for the app "BlockTogether"-this may create an artificially high incidence rate-but hey at least I know they follow me.
- Followers of Wil Wheaton-because I know he blocks me, and also shares his block list, so there's a fair chance that his followers would buy into this.
MODELING MY BLOCKERS
Why would I even need to model my blockers? A few reasons:
- I don't have a list of all Twitter accounts, so I need to predict who's followers might block me at high rates.
- These checks rely on the Twitter API, which is rate limited, so it's of great advantage to rank-order accounts in terms of likelihood to block.
- Understanding which people block me gives me insight to the 'why'?
As such I began creating a model against Twitter profile data to determine which accounts are likely to block me. First, what data elements will I have available? The basic elements available are what is pulled from a profile scan of the Twitter API:
- Verified Account?
- # of follows
- # you follow
- date created
- #of favorites
- #of statuses
- Description (text description on your profile)
Most of these are fairly easy to analyze, with the exception of the text description (of course, the most rich sense of actual attitudinal data which is likely to predict who you may block). I can't push a text description directly into a ML model, so I took two strategies in creating numeric data from textual data:
- Compute relative frequency between blockers and non-blockers of high incidence words, and then create binary dummies for all words with a skew (measured by binomial test) <.05
- Use a type of Natural Language Processing (NLP) to compute correlated topic models accross the description data; create a variable for each observed topic, and assign the probabilistic association for each user description to fill in the data for each topic.
I tested several types of models (read: ran and evaluated them using R's caret package). Extreme Gradient Boost trees ended up winning (though there wasn't a huge gain). AUC = .85.
Variable importance was interesting:
As expected, textual data was most predictive with several other items also providing important information to the prediction. Note: many of these variables have complex interactive effects inside our XGBTree, so it's not a simple matter of "more followers==more likely to block."
WHICH ACCOUNTS TO CHECK FIRST
What is the value of modeling this data? Because the Twitter API is rate limited I can only check a certain number of accounts per day, so it's good for me to use my rate limit wisely. By predicting which accounts are most likely to block me, I can simply prioritize those and thus increase my find rate. Using this prioritization at first increased my positive block rate by about 3x, from about 0.1% to 0.3%.
HOW TO GET TO NEW ACCOUNTS TO CHECK
Far above I covered where to start with a large number of account to check, deciding on followers of Wil Wheaton and the BlockTogether follower list. But once I've checke all of these accounts where should I go? Here I implemented two strategies:
- I found a relationship between an individuals propensity to block and their followers propensity to block. A quirk of the twitter API is that while blocked from seeing a user's timeline, I can still pull all of their followers. So, I began iteratively pulling all the followers for each block I found.
- I also found a releationship between the probability that you block me, and the incidence at which your followers will block. As such, I began pulling the followers for all high-probability blocking accounts(as determined by model above), regardless of whether they actually blocked me.
- There's an ability to find users via Twitter API using words on their profile, because I already know which words (Lyme, Geek) are associated with blocking me, I simple search Twitter for those words.
This iterative method can lead to a biased sample effect over time-so it's important that you also include some random accounts in your sample OR some that are intentionally in large deviation to your current pull.
In the end a few points to take away:
- The culture of Twitter has led to a situation where blocking is pervasive-especially for certain people who end up on block lists.
- I am blocked by many people on Twitter, and by gathering data about these individuals-I can certainly create a general profile of my blockers.
- Using Machine Learning, the Twitter API, Natural Language Processing, and other Data Science Technology, I can pull together a list of people who block me on Twitter.
As a personal aside, I don't care a lot that I'm blocked on Twitter, it doesn't appear to effect my user experience in a material way, as I am not blocked by any accounts I would normally follow and it's not an emotional issue for me. I do find it troubling that the pervasive use of block lists is allowing many Americans to insulate themselves from conflicting points of view, including primarily blocking people with whom they have never interacted with in the past.