Internet Censorship Course / Book Workshop
Platforms often use some type of automation to perform content moderation. In the realm of copyright, one way of moderating content is to use some type of matching algorithm, such as matching a hash or fingerprint of the content against a known databased of infringing content. There are different ways of performing these types of matches.
The Perspective API aims to help online communities detect and filter out toxic content. It is a machine learning model that can be used to score the likelihood that a comment is toxic. The model is trained on a variety of data sources, including Wikipedia talk page comments, and is able to distinguish between different types of toxicity, such as threats, obscenity, and identity-based hate.
The Perspective API is just one example of a sentiment analysis tool. There are many other tools and libraries that can be used to perform sentiment analysis. More recently, the advent of OpenAI’s GPT-3 has opened up new possibilities for sentiment analysis. GPT-3 is a large language model that can be used to perform a variety of natural language processing tasks.
Try asking ChatGPT or Claude to perform sentiment analysis on the same set of phrases that you used with the Perspective API.
As a follow-up activity, you could input a platform’s content moderation policy into an LLM, and subsequently input other types of content into the LLM to see how it might be classified.
One approach used for audio is to perform a so-called spectral or frequency-based, which does not match the content bit-by-bit, but rather matches how the audio “sounds”, by matching frequencies and beats through spectral analysis.
In this part of the hands-on assignment, you can download or compile the Echoprint code and perform some spectral hashes on audio files.