In busy news days, such as today many journalist take to various social media platforms to not only provide information, but collect information as well. Be it from on the ground reporting from other journalists to citizens at the scene who become conduits of said information posting onto social media the goings on in real-time.
This of course is nothing new to most people and today with the London subway bombings, or North Korea‘s latest nuclear missile tests, to the not guilty verdict of St. Louis police officer Jason Stockley who shot and killed an unarmed African-American, Anthony Lamar Smith.
These events have begun trending on various social media platforms and I myself chose to start tracking the Jason Stockley verdict by using hashtags on the social media platform, Twitter. And that is what we will focus on for now.
the deluge of information flows at millisecond speed at times on Twitter and to focus most of that information users of the social media application utilize hashing, or hashtagging to keep their opinions on the matter or reporting in that specific category. The Twitter webpage has a search feature on it where a user can input a search criteria, such as a hashtag, a name of a user account, or even keywords to assist you in tracking what you may find important to you.
So when a user types there search query into Twitters graphical user interface(GUI) and hits send, that request goes to Twitter’s servers, to what in tech speak is known as a Application Programming Interface, or API. Then once the information you requested is found, it is compiled and then sent back to your browser in a graphical, or GUI format.
Simple right, stick with here because all of you do this act daily. As a journalist, my interest today was on the St. Louis “not guilty” verdict in the St. Louis police officer, Jason Stockley shooting case of an unarmed man using the hashtag #JasonStockley.
And Voila! A crisp GUI interface that one can use to track the events unfolding. However, to me this is somewhat time consuming and needs the user to constantly refresh the feed to gathering said information in real-time. Yet, for me this was too much for me, I didn’t want the pictures or to see the Twitter users side profile picture or any of that, I just wanted the raw data.
So, using skills acquired through my training and other means. I decided to write a program in a computer programming language known as Python. After a quick Google search with keywords of what I needed I set my sights on Twitters developer page and its API cache where I could call upon the raw data of the Twitter servers and display what I wanted in my freshly minted program.
And as you see above in the finished product, the program began spitting out just the “textualized” data that I wanted. Now, you may be saying to yourself, “This is too much I don’t know what you are talking about!” Well, fear not, the ‘tech-geek-speak’ is over and the interesting part begins.
So while I was running this program I happened to see a giant string of tweets pop into the display, each one within mere seconds of one another and in rapid succession saying the same thing. About 50 at a time, then a 20 second break designed to not tip off Twitter’s automated server monitors, then another identical string of the same thing.
And I knew almost immediately that I had just stumbled onto a botnet, or more specifically a Twitterbot. Now, you may asking “What the heck is a Twitterbot!?” So a Twitterbot is a type of software that some consider a form of malware, or malicious software. Its designed to perform acts such as tweeting out messages of influence, retweeting said messages or even following some Twitter user accounts for the purposes of harassing that user or tracking and gathering all that users Twitter interactions and postings.
This specific Twitterbot was attempting to get the hashtag “JasonStockley” to trend on Twitter and drum up national if not global awareness to the Stockley verdict and possible outrage that could come from that by way of Twitter comment wars between differing opinions and the like.
Twitterbots and botnets have been in the mainstream media as of late and Twitterbots have been reportedly used to spread disinformation during the 2016 U.S. Presidential Elections to others promoting Russian propaganda along with both far Left and far Right agendas. Yet, many have no clue what one actually looks like or how to spot them, and in many cases some are so well programmed and written that the majority of internet users will never spot them.
So I wanted to provide an example of a small Twitterbot ‘influence campaign’ and impart some words of wisdom I learned as a Green Beret working with foreign nations, “Trust, but verify.”
Feature image courtesy of: Twitter