In one of the more hilariously meta developments in recent Twitter botting history, retweet-to-win tweets from @SeigRobotics offering free access to some sort of Twitter botting tool are being retweeted by a bunch of bots. #MondayMotivation
We found 321 accounts that used one or more custom automation apps to retweet one or more of @SeigRobotics's recent tweets. We then looked at the retweets of other tweets those accounts had retweeted to find members of the same networks that haven't (yet) retweeted @SeigRobotics.
We found 49 groups of automated accounts (536 accounts total), each using a separate set of custom automation apps. (It's quite possible that there are fewer than 49 distinct botnets, as several of the smaller groups were created on the same day.)
We looked at some of the larger networks. First, we have 29 accounts created over two hours on August 8th 2020, each of which has exactly one tweet: a retweet of @SeigRobotics's most recent tweet, sent via an app called "seig_bot".
Next, we have a group of 97 accounts tweeting via a myriad of apps, most of which have names similar to official Twitter products but with extra spaces in them, i.e. "Twitter for iPhone ". These accounts retweet tweets from a variety of proxy and botting software providers.
Moving on, here's a group of 53 accounts tweeting via apps named "Biyon≡( ε:)" and "Biyon≡( ε:) Pro". Most of these accounts were made back in 2013 and originally amplified Japanese tweets, but some have reawakened and are now boosting English retweet-to-win tweets.
The last retweet-to-win botnet we'll look at for now consists of 46 accounts, and has tweeted via 90 different custom apps in 5 months. All but 3 of these apps have been shut down (the "erasedXXXXXXXX" apps on the legend) - whether by the bot operator(s) or by Twitter is unclear.
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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.