Saturday Night Data Party: Finding Twitter Bots

It is Saturday night data party. Tonight, we are gonna dig into some Twitter data around a popular conspiracy theory around the current pandemic. There's a lot of stupid, stupid stuff being said about uh the COVID-19 pandemic. Uh one of the most stupid things that I've read so far is that this is a uh new world order conspiracy theory to have you know random people take over the world and George Soros and Bill Gates implant mind control and spread the disease with 5G wireless downs. So much stupid stuff.

Um of the questions that people have that I have about what's going on is this being fed at all? Are there bots at work um that are reinforcing this incredible stupidity instead of just letting it fizzle out? So let's dig into this. I have here uh and this is the uh R Tweet Library, which is a fantastic library for uh R Studio and the R program language, and one by Michael Kernney called TweetBotternaut. So our first step, and I've done some of this because it takes a while, um, is to pull in tweets using the hashtag planemic.

We have uh a limit of five thousand of them. Let's just go ahead and dig in real quick. We can see the various bits of data and what's being said. Yeah, yeah, whatever. So we've got the data.

Um the next thing we're gonna want to do is take this Twitter data and we're gonna split it into two pieces. We're gonna split it into in-network graph theories, this idea of nodes and edges. A node is a person, an entity, and an edge is a connection between two or more nodes. And what we want to do is try and identify patterns in the data. Now, the edges are pretty easy.

We've gone through and extracted them. And you'll see, in fact, I'll bring this up here. This is what an edge map looks like, right? You have people, one column, which is a source, and another column, which is a target, the people that are uh are being tagged or referenced or replied to in tweets. The part that's really interesting is actually I should bring up the nodes so you can see what the nodes look like too.

The nodes are, of course, things like the ID, I user ID, the followers count, engagements, engagement rate, things like that. Pretty straight pretty stock stuff. Uh by the way, if you're here watching, comment and say hi, uh, because otherwise I have literally have no idea that you're here. The bot or not library is one of my favorite libraries because it gives us um uses a very basic machine learning model to look at our users, the last 100 tweets by that user, and then based on patterns of things like repetition and stuff, assign that user a score, the probability that they are a bot from zero to one uh and with decimals in between, and we have their user IDs and we have their screen names. So they can only do 180 uh every 15 minutes, I believe is the Twitter rate API rate limit.

So in this case, we have a lot, and um had I thought about doing this prior to today, I probably would have gone through and extracted over time, you know, those three thousand five hundred eighty. So we're just gonna do a sample of them. So we've got the sample table here, which is screen name, user ID, and probability they're bot. Let's take that table. And just filter it down.

We want just the screen name. And probably it's their bot. Okay. Looking good there. Now we want to merge this back into our nodes.

So let's take our full nodes equals. Oh, what do we want to do here? I want to do a left join on nodes. And then our clean bot table. And we're gonna join on nodes is ID.

And our clean bot table is screen name. And we should have, so nodes should have still have the same three thousand five hundred and twenty observations of certain variables, three thousand five hundred and twenty of eight variables. Great. Okay, that looks good. So let's write that out.

Okay, now just quick double check. We have probability of a bot. Okay. Hmm. You know, that's an NA.

Those things are showing up as NA's are um gonna be problematic. Let's clean that up. I want to do that in Probbot. Now replace that with a zero, I believe is the syntax. Let me just double check that.

Data replace. Let's give that a go. See if it spits up. I'm gonna just do this the old fashioned way for this and figure that out later. And the old fashioned way is to take that NA and replace it with a zero.

What we need to do is now switch to uh graphing software. And graphing software specifically you're talking about network graphing software. This is an open source package called Gefi. Gephi. Gefi allows you to bring in those two types of styles of file that we just uh created the nodes and the edges.

And let's go ahead and get our edges in first. So what these are is the again, every interaction between these uh one Twitter account and another. We have 4,500 interactions. Interesting. Okay, and then we will bring in our nodes.

And just double check that our probability bot that's a double, it's come in as a double, okay. Append this here. Okay, great. Now we can start mapping this out. So we see we've got a we've got our accounts there.

Let's get our interface tidied up here. Now we want to start running some network diagnostics to understand this table. The average degree is the essentially degrees of separation. We want to do a modularity run to understand how things cluster together. Whole bunch of nothing there.

And our eigenvector centrality measure. This is our measure of distance between nodes. Now for the colors, I want to partition and I want to partition on probability of bot or not. I'm gonna have do I have a question on ranking? I think I want ranking.

Yeah, let's do ranking. And our color scheme. Let's do green for one end. Yellow in the middle. And fire engine red.

Let's apply this. So there we can see some of those likely bots in our mix. And now let's rank these by importance. And finally, let's go back in and change our ID to label and start appending some of these labels. And label size should be the size of the nodes.

Go to force atlas two, which is my favorite visualization for this. Start with a one, start with stronger gravity to begin with. And then run this and see what happens. And now let's okay, that's gotten some order to it. Let's explode it.

Again, if you are watching and you want to say hi, please leave a comment. Otherwise, I literally have no idea that you're there. What the software is doing now is essentially trying to force these individual nodes to into some kind of order that where we can expose the network underneath. And eigencentrality is a good measure of what I think of as influence in the sense that it measures who does the not who is the most talkative, but who is most talked about in any environment. I do this a lot for like trade shows.

If I want to understand who are the most prominent speakers at a trade show. But the bots are helping amplify um different groups in the network. And so if this was something that I was investigating for, like a uh like criminal research, um, this would be super useful to have. You'd want to identify and score all the original accounts, not just the 180 sample, but all of them, and figure out just how bad is the bot problem in this particular topic. And once you do that, then you get a sense of is this is this real or is this astroturfed?

Meaning that it's fully bot supported. And if that's the case, then who's running the bot networks? Right? Who is behind the bots? Now, with this information, you could actually go and start looking at those accounts and seeing what else do those accounts tweet about and stuff like that.

Um, Twitter is my favorite social network by far because the data is publicly available for the most part, and we can get a look at the interactions between things. Uh, and it is one of the reasons why uh influencer marketing measures are vastly overweight on Twitter, because that's the data source that everyone can see compared to Facebook, which is notorious for not releasing any of its data. But now we're at a point where if you wanted to dismantle this, it would be trivial to go back to that list of essentially bot handles, look at them and report them, right? Because bots are prohibited by Twitter's terms of service, by Twitter's terms of use. So if you went in and picked something that was clearly not in the best interests of the general public, namely people who are foisting this conspiracy theory that the COVID-19 pandemic is is somehow a planned global new world order, whatever, you could go in and take some of the support away from it by reporting those bot accounts.

Oh, though the bot creators, of course, will create new ones. Um that's the ongoing uh struggle. But think about this for any topic where you might have a problem. And if you are a public relations professional, if you are a uh uh commu uh marketing communications professional, you'd better be doing this if you are doing crisis communications because you want to know is my crisis real or is my crisis astroturfed? Is somebody working to sabotage my brand for like we larger brands?

Is someone working to sabotage my brand when I've done nothing wrong? Now, if you have done something wrong, that's pretty self-evident. You need to fix that. Um if you are a political campaign, this would be super helpful to do, right? To understand your supporters and your detractors and be able to take those apart and figure out again how much is artificial.

We're not talking about trying to change minds of anybody else, we're not trying to do uh anything nefarious. We are literally trying to help, in this case, Twitter, enforce the terms of service on its own network. And if you are digging into a specific topic, whatever it is, use tools and technologies like this. So that's tonight's Saturday Night Data Party. There's a lot in here.

There's a lot to helping social networks by reporting obvious violations of the terms of service, right? Again, we're not talking about taking a political stand, we're talking about taking out the garbage. Right. So if you have questions about this sort of thing, this is of interest to you, go ahead and um go over to trust insights.ai/slash analytics for marketers. It's our uh free Slack group, and ask those questions.

Love to chat with you about them and uh and see what you think. Uh but yeah, thanks for watching. I'll talk to you soon. Take care. Want help solving your company's data analytics and digital marketing problems?

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