Unmodified GAN-generated face pics have the telltale trait that the major facial features (particularly the eyes) are in the same position on every image, and @Gabby_ucm's profile pic is no exception. There are also anomalies in the teeth, clothing, and hair of @Gabby_ucm's pic.
More on GAN-generated images and their use on Twitter in this set of threads:
Although the use of a GAN-generated face is not necessarily deceptive, it becomes clear when looking at @Gabby_ucm's replies to appearance-based compliments that the operator of the account is quite willing to deceive its followers into believing the image depicts a real person.
In addition to the fake profile pic, @Gabby_ucm also posted this image and encouraged their followers to believe that it depicts the person running the account. However, the image is an altered version of a photo snagged from a Japanese hair salon's Instagram page.
Although @Gabby_ucm claimed in late 2021 that the plagiarized photo was taken "recently", it is in fact from 2019 or earlier - and the allegedly US-based @Gabby_ucm's claim to have never visited Japan would seem to preclude appearing in a photo shoot at a Japanese hair salon.
How did @Gabby_ucm get 20,000 followers so quickly? There are at least two reasons, one of which is follow trains. The @Gabby_ucm account has been listed on at least 56 follow trains since December 1st, 2021, and has posted several trains of its own.
The second reason for @Gabby_ucm's rapid growth: it's the recreation of a banned account with many followers (@gabby_UCMaroon) that used the same GAN-generated face pic. @Gabby_ucm says the old account was banned for platform manipulation, but claims not to know what that means.
The suspension for platform manipulation isn't particularly surprising, however, as the operator of the @Gabby_ucm account was running a second account (@GabbyRedux, now suspended) at the same time as the suspended @gabby_UCMaroon, and that account used a stolen profile pic.
As with the current @Gabby_ucm account, the old @gabby_UCMaroon account falsely presented a stolen photo as an image of the person running the account on at least one occasion.
This account has now been suspended.
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For a change of pace, I'm doing a bit of a YouTube experiment. I'm going to choose six songs of varied genre on a new YouTube account, and then just listen to whatever it creates as the "My Mix" playlist. #FruitOfTheAlgorithm
What do 2022 Congressional candidates @BlakeHarbinGA (R), @DavidGiglioCA (R), and @RajiRab2020 (D) have in common? All three have recently been followed by thousands of newly-created accounts with lowercase display names and zero tweets.
The new followers of the three Congressional candidates are part of a botnet consisting of (at least) 38105 accounts with random-looking names created in December 2021/January 2022. None of the accounts has ever tweeted, though most have liked dozens or hundreds of tweets.
The accounts in this botnet have extremely repetitive biographies, using only 1085 unique biographies across 38105 accounts.
In one of the sillier developments in recent history, the adherents of this flawed "vetting guide" are now issuing "alerts" about my account because (gasp!) a small number of the 41K accounts following me look suspicious.
Since I'm now being targeted, I'm going to be blunt. The principal author of this "vetting guide", @EnseySherwood, blatantly makes shit about about bot detection, such as this false claim that servers have a setting that Twitter could use to eliminate all bots in 10 minutes.
If you have been relying on this person or the "vetting guide" for advice on bots and suspicious accounts, you've been fooled, and you should probably also give some thought as to why certain "vetting experts" constantly lock their accounts and refuse to engage with critics.
It's possible to automate this technique and download most or all of an account's tweets archived on Wayback Machine/Internet Archive. Here's how to write a basic Python program to download the tweets and store them as a CSV file.
Step 1: read the list of all the Wayback Machine archives of the account in question. The URL used is the same call the Wayback Machine website uses, but the maximum number of results has been bumped to 1 million.(Wayback Machine defaults to 10K max results.)
Step 2: filter the results to archives of individual tweets or replies (throwing away things like archives of someone's profile etc). Tweets from before Twitter switched to using Snowflake IDs (November 2011 blog.twitter.com/engineering/en…) are also discarded.
The hashtag #Hero trended in the USA yesterday (January 2nd, 2022) with a truly massive volume of tweets. In an interesting twist, the vast majority of the tweets are in Farsi despite "hero" being an English word.
The #Hero trend appears to be the result of a preplanned tweetstorm commemorating General Qasim Soleimani on the second anniversary of his assassination in January 2020. Tweet activity (mostly in Farsi) using this hashtag has been building since mid-December 2021.
Over 1.6 million tweets containing #Hero were tweeted by 50872 accounts on Jan 2, 2022. Very little of the traffic appears to be automated. An exception is @Ra_Shojaei, which tweets pro-Soleimani tweets 24/7 via a custom app called "TwèétDeck" (not to be confused with TweetDeck).
Here's a proposal for @TwitterSafety: modify the "Account suspended" screen to include a brief description of which rule/rules the banned account violated, and have "Learn more" link to the related section of the Twitter Rules.
(Images are mockups with made-up account names.)
Why make this change? Currently, when an account with any degree of notoriety gets suspended, conspiracy theories about why it was removed quickly take root. In the absence of information about the reason for the suspension, the conspiracy theories are often accepted as fact.
Often, the dominant narrative(s) for why a given account got banned are established by the account operator(s) themselves, either on other platforms or via friends/alts on Twitter. Unsurprisingly, these narratives tend to cast the suspension as unfair, regardless of the facts.