Over the last several days, a new hashtag has appeared and propagated on Twitter: #VerifiedHate. The concept seems to be to attack verified (blue-check) accounts that are theoretically promoting hate against white people.
We downloaded recent tweets containing the #VerifiedHate hashtag. We found 18092 tweets from 9747 accounts - the first tweet is in the wee hours of the morning on August 5th, and the hashtag takes off on the evening of August 7th.
This hashtag campaign appears to have been planned on other platforms in two stages. First, we have this post on Gab from 8/5, which is within minutes of the first tweet from @Keque_Magus. It's followed by plenty of additional Gab discussion promoting #VerifiedHate.
Next, we have this post on 4chan. Not long after, this tweet from @meme_america appeared. This tweet has of the time of this writing been retweeted over 1800 times, and it's not the only #VerifiedHate tweet from @meme_america to get a sizable amount of attention.
Here's the retweet network for #VerifiedHate. In addition to the accounts already mentioned, @getongab (the official Gab twitter account) also figures prominently.
Let's take a closer look at @meme_america. The account is less than two months old, and has an unusual tweet schedule - looking closely, the gaps for sleep shift forward an hour or so each day.
There are 772 accounts with original #VerifiedHate tweets (most accounts that tweeted the hashtag only did so via retweets.) Checking these accounts yields eight more with a similar pattern in their schedule.
Let's take another look at the #VerifiedHate volume. 2876 of 18092 tweets (15.9%) are tweets from one of these nine accounts or retweets thereof. This group of accounts had a disproportionate impact on the traffic. It's also notable that they all showed up after the 4chan post.
We don't currently have a good explanation for what causes this pattern, although we have seen it before. It seems to correlate with accounts that push messaging about white people being persecuted. If anyone has a hypothesis, we're interested.
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.
Former BNN employee Michael Gordon Douglas aka "Chicago Mike" has been found guilty of CSAM distribution.
In light of this, it's worth revisiting disinformation propagated by BNN and others to make excuses for Mr. Douglas's illegal content-related X/Twitter ban(s).
The disinfo in question originated somewhere seemingly unrelated, with false claims that several people (including me) were using a magic "console" to ban X users on behalf of Ron DeSantis. This hoax was invented by Texas bullshit purveyor Steven Jarvis.
Steven Jarvis peddled his "console" theory to BNN founder Gurbaksh Chahal, and when BNN employee Michael Gordon Douglas's @ChicagoMikeSD X account was suspended in early 2023, BNN published an article falsely attributing the ban to the imaginary "console". web.archive.org/web/2023012507…
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.