We've previously documented that the "Round Year Fun" apps ("My Twitter Family" etc) force you to follow other accounts without your knowledge. Interestingly, the main Round Year Fun website shares an IP address with a website that sells Twitter followers.
The follower sales website in question (realactivefollowers(dot)com) offers a trifecta of shady Twitter-related services: you can buy followers, likes, and even developer accounts (which enables aspiring botmakers to bypass the normal approval process, among other things).
Realactivefollowers(dot)com also offers a free trial of 50 followers. We had @DrunkAlexJones take advantage of this offer with the goal of testing the hypothesis that the followers being sold on this website are unwitting users of the Round Year Fun apps.
Within a few hours, @DrunkAlexJones experienced an infusion of new followers, most of which indeed have tweets posted via one or more of the Round Year Fun apps. (We were unable to check a few of the accounts as they are presently in protected status.)
If you've already used one or more of the Round Year Fun apps, here are instructions on how to revoke their access to your account:
Final opsec note: we regard both of the websites mentioned in this thread as potentially unsafe and used Tor to visit both of them. We recommend anyone researching sites that may be malicious take similar precautions.
Update - both Round Year Fun and the realactivefollowers(dot)com were offline briefly after losing their original domain registration and hosting. Unfortunately, both sites are now back and hosted by @DreamHost.
Here's a table of the accounts with the largest number of recent (and involuntary) Round Year Fun followers, based on the last 48 hours' worth of Round Year Fun tweets. There's a lot of variety, but cryptocurrency does seem to be a repeated theme.
Update: found some posts on Black Hat World forums related to Round Year Fun/realactivefollowers(dot)com. A user named silentwandererr is promoting realactivefollowers(dot)com and claims to "have an unlimited amount of Twitter API keys".
Some thoughts on perennial pitfalls in news coverage of social media manipulation that frequently result in reporting on fake accounts/bots/etc being far less accurate and informative than it ought to be...
The most common problem with news articles about fake accounts: failure to include any examples of fake accounts or evidence of their inauthenticity. Any or all of these headlines might be accurate, but you can't tell from the articles, due to absence of evidence.
A related issue: articles like the "Nearly Half of Biden/Trump's Followers Are Fake" and "Nearly Half Of Accounts Tweeting About Coronavirus Are Bots" pieces base their numbers on closed-source third party tools, which may or may not actually be detecting anything useful.
Does thanking, praising, or insulting an LLM-based chatbot affect the speed or accuracy of its responses to questions involving basic arithmetic? Let's find out!
For this experiment, Meta’s Llama 3.1 model was asked to add and multiply random numbers between 10 and 100, with six different wordings: polite, rude, obsequious, urgent, and short and long neutral forms. Each combination of math operation and wording was tested 1000 times.
Results: asking the questions neutrally yielded a faster response than asking politely, rudely, obsequiously, or urgently, even if the neutral prompt was longer. Overall, obsequious math questions took the longest to process, followed by urgent, rude, and polite questions.
Just for fun, I decided to search Amazon for books about cryptocurrency a couple days ago. The first result that popped up was a sponsored listing for a book series by an "author" with a GAN-generated face, "Scott Jenkins".
cc: @ZellaQuixote
Alleged author "Scott Jenkins" is allegedly published by publishing company Tigress Publishing, which also publishes two other authors with GAN-generated faces, "Morgan Reid" and "Susan Jeffries". (A fourth author uses a photo of unknown origin.)
As is the case with all unmodified StyleGAN-generated faces, the facial feature positioning is extremely consistent between the three alleged author images. This becomes obvious when the images are blended together.
The people in these Facebook posts have been carving intricate wooden sculptures and baking massive loaves of bread shaped like bunnies, but nobody appreciates their work. That's not surprising, since both the "people" and their "work" are AI-generated images.
cc: @ZellaQuixote
In the last several days, Facebook's algorithm has served me posts of this sort from 18 different accounts that recycle many of the same AI-generated images. Six of these accounts have been renamed at least once.
The AI-generated images posted by these accounts include the aforementioned sculptures, sad birthdays, soldiers holding up cardboard signs with spelling errors, and farm scenes.
The common element: some sort of emotional appeal to real humans viewing the content.
As Bluesky approaches 30 million users, people who run spam-for-hire operations are taking note. Here's a look at a network of fake Bluesky accounts associated with a spam operation that provides fake followers for multiple platforms.
cc: @ZellaQuixote
This fake follower network consists of 8070 Bluesky accounts created between Nov 30 and Dec 30, 2024. None has posted, although some have reposted here and there. Almost all of their biographies are in Portuguese, with the exception of a few whose biographies only contain emoji.
The accounts in this fake follower network use a variety of repeated or otherwise formulaic biographies, some of which are repeated dozens or hundred of times. Some of the biographies begin with unnecessary leading commas, and a few consist entirely of punctuation.
It's presently unclear why, but over the past year someone has created a network of fake Facebook accounts pretending to be employees of the Los Angeles Dodgers. Many of the accounts in this network have GAN-generated faces.
cc: @ZellaQuixote
This network consists of (at least) 80 Facebook accounts, 48 of which use StyleGAN-generated faces as profile images. The remaining 32 all use the same image, a real photograph of a random person sitting in an office.
As is the case with all unmodified StyleGAN-generated faces, the main facial features (especially the eyes) are in the same position on all 48 AI-generated faces used by the network. This anomaly becomes obvious when the faces are blended together.