Let’s build an insultbot, Getting ready to welcome trollers and alike.
Course Description
“even the prime minister of Norway herself, reposted the photo to Facebook, only to have it quickly removed.” Custodians of the Internet (Tarleton Gillespie)
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.
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. Recently, we had chatGPT that can make sentiment analysis in an advance approach, this course shows an alternative, which is free. chatGPT has a small chart per API call. Furthermore, chatGPT requires much less coding expertise
On this course, I want to share a trivial case, which was not also easy to solve for a scientists, but now easily implemented as public library: 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 probability, 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.
