Conspirador Norteño Profile picture
Apr 30, 2021 8 tweets 4 min read Read on X
How does one find bot/sock networks? One technique that sometimes bears fruit is to gather a bunch of tweets with some common characteristic (in this example, Turkish tweet sent with TweetDeck), plot the account creation dates, and look for spikes.

cc: @ZellaQuixote
Two of the creation date spikes (Jan 1 and Mar 15, 2021) are batches of accounts that are part of the same botnet: a 32-account porn network whose members tweet at the same times each day via TweetDeck and occasionally via the Twitter Web App. Most were created in 2020 or 2021.
The botnet also contains three accounts created in 2009. It is possible that these were purchased/hacked/otherwise repurposed, as all three have changed their display name significantly and one has changed its @-name as well.
This network posts two types of tweets: original tweets sent mostly via TweetDeck, and retweets sent via the Twitter Web App. The original tweets are repetitive porn tweets with images, while the retweets are mostly of other accounts in the network.
The scheduling features of both TweetDeck and the Twitter website (usually) post scheduled tweets in the first second of the minute for which they are scheduled. Based on this, this pornbot network's TweetDeck tweets appear to be scheduled, while its web tweets do not.
(thread with more background on the timing of scheduled TweetDeck tweets)
Every account in this botnet follows or is followed by every other account in the botnet. (Some follows are mutual, some are not.) The bots follow very few accounts outside the network, mostly large Turkish accounts and porn accounts.
As is popular with pornbot networks, these accounts use stolen profile pics. TinEye proved to be more effective than Google or Yandex at finding other uses of these images on the internet. Most are cropped versions of larger photographs.

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More from @conspirator0

Mar 8
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 sponsored Amazon listing for the "Crypto Essentials" series by "Scott Jenkins", an author with a GAN-generated face>
screenshot of the About the Author page for "Scott Jenkins"
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.) screenshots of the About the Author pages for "Morgan Reid" and "Susan Jeffries"
list of books by "Scott Jenkins" on the Tigress Publishing website
list of books by "Morgan Reid" on the Tigress Publishing website
Image
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. all three GAN-generated faces blended together
Read 5 tweets
Feb 8
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 three Facebook posts featuring AI-generated images of various large sculpted items: a loaf of bread shaped like a rabbit, and wooden sculptures shaped like a deer and an eagle
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. collage of 18 Facebook profiles
Page Transparency info for six of the pages
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. three Facebook posts featuring AI-generated images of various large sculpted items: a loaf of bread shaped like a rabbit, and wooden sculptures shaped like a deer and an eagle
three Facebook posts featuring AI-generated images of people holding birthday cakes
three Facebook posts featuring AI-generated images of soldiers holding cardboard signs with spelling errors
three Facebook posts featuring AI-generated images of people holding freshly picked produce
Read 7 tweets
Jan 24
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 screenshots of 12 Bluesky accounts with the biography "Praticante de esportes radicais"
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. hourly creation volume chart for the 8070 accounts in the spam network
table of 30 example spam accounts
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. table of biographies most frequently used by the network
screenshots of six accounts with malformed biographies
Read 8 tweets
Jan 10
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 Screenshots of the profiles of 12 fake LA Dodger employee Facebook accounts
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. collage of 48 GAN-generated faces used as profile images by the fake accounts
collage of 32 identical photos used as profile images by the fake accounts
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.
Read 6 tweets
Oct 18, 2024
None of these chefs exist, as they're all AI-generated images. This hasn't stopped them from racking up lots of engagement on Facebook by posting AI-generated images of food (and occasional thoughts and prayers), however.

cc: @ZellaQuixote collage of 11 AI-generated images of chefs, presently being used as the avatars of Facebook accounts
These "chefs" are part of a network of 18 Facebook pages with names like "Cook Fastly" and "Emily Recipes" that continually post AI-generated images of food. While many of these pages claim to be US-based, they are have admins in Morocco per Facebook's Page Transparency feature. collage of the profiles of 18 Facebook "chef" pages that post AI-generated images of food
screenshots of Facebook page transparency info showing that the pages are run from Morocco
Between them, these 18 Facebook "chef" pages have posted AI-generated images of food at least 36,000 times in the last five months. Not all of the images are unique; many have been posted repeatedly, sometimes by more than one of the alleged chefs. examples of AI-generated food images posted by the Facebook spam pages
examples of AI-generated food images posted by more than one Facebook spam page
Read 7 tweets
Sep 8, 2024
Can simple text generation bots keep sophisticated LLM chatbots like ChatGPT engaged indefinitely? The answer is yes, which has some potentially interesting implications for distinguishing between conversational chatbots and humans.

cc: @ZellaQuixote
conspirator0.substack.com/p/baiting-the-…
screenshot of Substack post
For this experiment, four simple chatbots were created:

• a bot that asks the same question over and over
• a bot that replies with random fragments of a work of fiction
• a bot that asks randomly generated questions
• a bot that repeatedly asks "what do you mean by <X>?"
The output of these chatbots was used as input to an LLM chatbot based on the 8B version of the Llama 3.1 model. Three of the four bots were successful at engaging the LLM chatbot in a 1000-message exchange; the only one that failed was the repetitive question bot.


screenshots of portions of the conversation between the repetitive cheeseburger bot and the LLM bot
screenshots of portions of the conversation between the Star Trek bot and the LLM bot
screenshots of portions of the conversation between the random question bot and the LLM bot
screenshots of portions of the conversation between the "what do you mean" bot and the LLM bot
Read 5 tweets

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