Using a technique known as natural language processing (NLP), data scientist Jeff Kao determined more than one million of the public comments regarding the FCC's net neutrality repeal were likely fake.
Of the remaining comments written by unique individuals, Kao estimates 99 percent favored keeping the rules in place.
Kao [scanned] more than 22 million comments submitted to the FCC’s website. He found that more than 17 million were duplicates or close parallels. But many of those were, he writes, “legitimate public mailing campaigns,” which provide boilerplate text for real people to submit.
The system used to generate the fake comments swapped out words in such phrases again and again – for instance, switching “people like me” for “individual citizens” and “products” for “services” – to produce 1.3 million superficially distinct variations on the same basic block of text.
Trump's FCC has declined to look into the matter, and in so doing effectually renders concerned citizens' comments meaningless.
Fortune’s Aaron Pressman has argued that undermining the public comment system would give a tactical edge to industry opponents of net neutrality... [T]he Trump FCC [has] refused to cooperate with an investigation into the fake comments by the New York Attorney General.