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Image by Adrien Gilbert

Let's build an insultbot: Getting ready to welcome trollers and alike

Online Content moderation is the process by which one must decide what stays online vs. what should be moderated or even eliminated; no one is happy to be moderated, I was not when I was the target of several online moderations on YouTube, Facebook and Twitter. The “Napalm girl” was a famous case in which Facebook classified as nudity, which is a historical picture.

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Online Content moderation is the process by which one must decide what stays online vs. what should be moderated or even eliminated; no one is happy to be moderated, I was not when I was the target of several online moderations on YouTube, Facebook and Twitter. The “Napalm girl” was a famous case in which Facebook classified as nudity, which is a historical picture.

"even the prime minister of Norway herself, reposted the photo to Facebook, only to have it quickly removed." Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media Book by Tarleton Gillespie

What is the best and most efficient way to safely moderate content online?

Several programming savvies are doing their best. Like in String Theory, they gather the best, but no practical answer came out yet.

On this course, I want to share a trivial case, which was not also easy to solve: direct insult (i.e., calling people names).

The bot is a classical pattern recognition: it gives out a number, and you must decide your tolerance for insults. This number, already standard on deep learning, is a sort of properly, loosely speaking.

We are going to use two MIT license Google public tools: TensorFlow.js and Angular. The former is a deep learning tool for freely creating smart web application, whereas the latter is a free framework for creating frontend application, no need to create servers.

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