4 Matching Annotations
  1. Last 7 days
  2. social-media-ethics-automation.github.io social-media-ethics-automation.github.io
    1. Star Wars Kid. December 2008. URL: https://knowyourmeme.com/memes/star-wars-kid (visited on 2023-12-08).

      The "Star Wars Kid", aka Ghyslain Raza was a child featured in a viral video of a mock lightsaber battle. The video is estimated to have amassed over a billion views, and the internet was struck by the hilarity of the video. However, Raza was psychologically damaged and suffered emotionally from the video, even finishing school in a psych ward. This example demonstrates the dangers of the internet and how someone can suffer greatly from going viral.

    1. When physical mail was dominant in the 1900s, one type of mail that spread around the US was a chain letter [l7]. Chain letters were letters that instructed the recipient to make their own copies of the letter and send them to people they knew.

      It's interesting to see this as I remember in the early days of the internet when digital chain mail would go around with a negative or scary incentive to repost the image or thread.

  3. Oct 2025
  4. social-media-ethics-automation.github.io social-media-ethics-automation.github.io
    1. Elon Musk [@elonmusk]. Trashing accounts that you hate will cause our algorithm to show you more of those accounts, as it is keying off of your interactions. Basically saying if you love trashing *that* account, then you will probably also love trashing *this* account. Not actually wrong lol. January 2023. URL: https://twitter.com/elonmusk/status/1615194151737520128 (

      It is neat to see how algorithms work and how some times they can have unintentional errors such as this. While it may seem like a good idea to keep showing users content that they don't agree with, would it eventually make the user not want to use the platform as they are bombarded with content they don't like?

    1. Similarly, recommendation algorithms are rules set in place that might produce biased, unfair, or unethical outcomes

      In another class I am taking this quarter, we are learning about biases that make their way, whether intentional or unintentional, into algorithms and social media systems. It is interesting to see the connection between the two.