Mike Benz: How the Censorship Industry Insiders Create Linguistic Taxonomies

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Mike Benz: How the Censorship Industry Insiders Create Linguistic Taxonomies

 

Summary

➡ The text discusses growing concerns about privacy invasion, suggesting that agencies might monitor keystrokes on personal devices. This concern is linked to increasingly sophisticated social and computer science fields that analyze and censor language used in politics. Specific words, phrases, hashtags, and images are cataloged to create a vast database, which can potentially be used to detect and suppress certain political views.

Transcript

How the hell do they know that I am saying this to you in our private text? Is it because the NSA captures every keystroke that I touch on my desktop or my mobile phone? In a way, it’s dirtier than that. A whole new field has been created around misdes and mal information that joins together the social sciences and the computer sciences. It’s almost like the same way Apple started as being a sort of Steve Jobs and a Steve Wozniak, where you have on the job side, if you will, the vision people, the political scientists who map the diction of the political groups, movements and parties they want to censor.

So, for example, for mail in ballots when they did the mass censorship of 22 million tweets just in the 2020 election cycle, they cataloged all the different words, terms, phrases, hashtags, memes, images, everything that goes into the language. They call it creating a taxonomy or a narrative network, a sort of linguistic lexicon of all the things that are proxies for support for they would they would database terms around election fraud or ballot harvesting or delegitimization proxies.

How do they know that I have used any of those phrases at the moment? I’ve used them well because the Pentagon developed a technique in artificial intelligence called natural language processing using these machine learning methods to be able to dissect every word you say on the internet. Because every word you say on YouTube, for example, goes into in 2009, YouTube created closed captioning and every word you say, there’s a transliteration for it.

It’s run into the back end so they can parse every word you say. They know, for example, that I just used the phrase election fraud. That is going to go into the trust and safety filter layers and it will use the other sort of contextualizing information. Are there a certain number of other cluster terms or phrases used in close proximity? There’s sentiment analysis to run to judge whether or not you are expressing support or opposition.

And they’ve done this for countless issues now. Elections, COVID, immigration, energy, Ukraine policy, all of this. By the way, these were techniques developed by the Department of Defense. DARPA started funding this to take on ISIS when they wanted to learn how ISIS was recruiting. After the Garland, Texas attack in 2015, there were tens of millions of dollars poured into this, but it’s been turned once again from foreign wars to domestic politics.

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