This botnet consists of 50 Arabic-language accounts, all created between December 18th, 2020 and January 1st, 2021. These accounts tend to all activate simultaneously and all (allegedly) tweet via the Twitter Web App.
The accounts in this botnet follow other members of the botnet, and most of them follow several. The follow relationships are somewhat structured by account creation date, with the December 22nd and 23rd batches having the most follows from other bots in the network.
The bots in this network are grouped into pairs created on the same day that exclusively retweet one another. There are a few cases where a bot retweeted a member of the network other than its "partner", and two (@cleaning_sa2 and @pestclean1) have yet to retweet anything.
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To close out 2020, we had @DrunkAlexJones tweet #HappyNewYear botbait tweets in various time zones, accompanied by music from artists associated with said time zones. (We largely missed the Eastern hemisphere because we started too late in the day.)
Here's a chart showing how much amplification, both from automated and organic accounts. "Astro-Atlantic Hypnotica from the Cape Verde Islands" was the most-retweeted video, with 15 automated and 18 total retweets.
The @DrunkAlexJones#HappyNewYear botbait tweets were retweeted by a total of 48 different automated accounts. The most frequent flier was @BlazedRTs, which retweeted all 6 tweets it was tagged in. (Some of the tag-for-retweet bots were less reliable.)
If you're looking for a "news feed" account whose website consists entirely of news stories harvested from other websites (many of them less than reliable themselves), then @1BUV_News just might be right up your alley.
Where does the content on 1buv(dot)com, the website promoted by @1BUV_News come from? The present lineup includes 20 different websites, the most common being Sputnik, Breitbart, and ZeroHedge. Antivax/conspiracy site Natural News is another interesting inclusion.
The majority of @1BUV_News's content is automated, posted round-the-clock by a custom app with no name. (We've seen a few bots that post via nameless apps before, but without a visible name, it's hard to tell if they're the *same* no-name automation app.)
GAN-generated profile pics (such as those produced by thispersondoesnotexist.com) have become quite popular among botnets promoting cryptocurrency blogs/websites. Here's a look at one such botnet that was mostly made just before Christmas. #HolidayShenaniGANs
We found a group of 12 accounts with GAN-generated profile pics linking cryptocurrency-themed websites and blog posts. All but one of these accounts was created between December 22nd and 25th, 2020.
Here are the profile pics of all 12 bots in the network. When overlaid, one can see that the major facial features (particularly the eyes) are in the same location on each image. The images contain other artifacts too: the metallic droplet on @SwiftAlene's forehead, for example.
This botnet is made up of 241 accounts, created in batches between February 28th and March 3rd, 2011. All have names consisting of a first and last name followed by 2 or 4 digits, follow similar numbers of accounts, and have never liked a tweet.
The accounts in this botnet don't just follow similar numbers of accounts - they follow a lot of the same accounts, with 543 accounts followed by all 241 members of the network. The accounts they follow are mostly promotional accounts, many of which followed the bots back.
It's a day that ends in "Y", and a posse of pornbots is prolifically posting tweets advertising a group of websites, with the novel twist that the websites are included in images rather than linked directly from their tweets. #SundaySpam
These bots were created in batches, and their image tweets contain hashtags and were (allegedly) sent via the Twitter Web App. We found 2147 batch-created accounts that fit this pattern, but how do we eliminate the ones without website names emblazoned on their image tweets?
Answer: we used OCR (optical character recognition), specifically the pytessaract library. It couldn't make much sense of the raw images, which use gray text on colored backgrounds, but tweaking the brightness/contrast on grayscale negatives resulted in machine-readable text.
In the aftermath of the Nashville bombing, a wide variety of rumors and conspiracy theories about motives/affiliation of the bomber(s) began circulating on Twitter. Trump supporters, antifa, and Dominion voting machines were some of the most common themes.
The themes of the rumors varied somewhat over time. Antifa was a common topic shortly after the bombing, tweets about Dominion spiked twice after popular tweets, and Trump supporters were a common theme throughout, increasing slightly after CBS named a person of interest.
Here are some examples of tweets containing terms from each group. Some of the tweets fit into multiple groups - for example, some of the tweets about Dominion voting systems also reference the AT&T building.