Peru's next president will be chosen in a runoff election on June 6th, and a network of recently-created accounts (many with stolen profile pics) is expressing its astroturfed preference for Keiko Fujimori over Pedro Castillo via TweetDeck. #FridayAstroturf
This network consists of 114 Spanish-language accounts created in batches between March 26th and April 30th, 2021. Thus far, these accounts have posted almost all of their content via TweetDeck (6989 of 7101 tweets, 98.4%).
These accounts tweet about the upcoming election, a mix of tweets promoting right-wing candidate Keiko Fujimori and tweets attacking left-wing candidate Pedro Castillo. They retweet each other's political content frequently, along with occasional retweets of large accounts.
The accounts also post repetitive non-political tweets. These are a mix of relatively mundane tweets and follower growth tweets. As always, take the English translations of the tweets with a grain of salt, as machine translation sometimes gets things quite wrong.
The hashtags posted by this botnet reflect these themes, with political hashtags related to the upcoming election as the primary theme and follower growth hashtags turning up prominently as well.
One final note: it may be a coincidence, but we found two similar-looking batch-created TweetDeck-based bot/sock networks tweeting about Ecuador's recent presidential election. Here are threads on those networks:
We did some further exploration of recently-created accounts that tweet in Spanish via TweetDeck, and found a few more groups of batch-created accounts:
8 accounts, location set to Guatemala
23 accounts, location set to República Dominicana
27 accounts, location set to Perú
Many of these batch created accounts have follow relationships (usually multiple) with the Perú election botnet described earlier in this thread, as well as with each other. Eight of the accounts with their location set to República Dominicana form a separate follow network.
Although the Guatemala and República Dominicana batch-created TweetDeck accounts have yet to tweet anything political, they have tweeted verbatim copies of some of the same "filler" tweets as the Perú election botnet, a possible sign that they are part of the same operation.
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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.
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
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.
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.
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.
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.