Plenty of other newsletters cover the U.S. debate over Big Tech regulation, so I stay away from writing about those hearings and takes. (To be honest, I don’t really care who owns TikTok, but it would be hilarious if it were Oracle, which is like the Russell Crowe of enterprise software.) But I watch them closely because they’re a fantastic way to learn how the public conceives technology and content recommendation algorithms.
Compared with the general public’s knowledge of how journalism works — not great! — our collective understanding of algorithms is exponentially lower. Since Big Tech feels no need to educate users about their complex products in the name of intellectual property, most people instead believe what academics would consider a folk theory of algorithms. Technically, what I discuss in this newsletter are educated folk theories. The SEO industry comprises educated folk theories since no SEO experts actually work at Google and all current and former Googlers who understand the algorithm are under NDAs.
Like all folk theories of tech, these theories of algorithmic censorship are not entirely wrong, but they’re often off-base. They’re on the outer part of the dartboard where the numbers are but technically corked in on the same board as the bulls-eye. (The theories in this newsletter are outer bulls-eye, according to me.)
To understand the reach of these and other folk algorithm theories, as well as to test out a few tools, I pulled some search data to explore the public’s conception of our algorithmic networks.
Why search query data?
Search query data records the exact words that a person types into a search bar. Google and other search engines record and aggregate these terms primarily for advertising, but SEO and content strategists use it as well. The data is completely anonymized — never publicly associated with who typed it. Some search queries are used tens of thousands of times per month, and others are unique.
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