Social Media and Algorithms

Saturday, November 14, 2020

Media

  • Theres no longer any gatekeeping, everyone is their own influencer now.
  • You want to build up your audience steadily with minor hits rather than being a one-shot wonder.

Algorithms

  • Machine learning fairness bias is present in discriminator algorithms for monetization and recommendation.
  • Promotes radicalization by upping the stakes.
  • Platform staying power is maximized and incentivised.
  • Only algorithm for truth is humans, until we create something that can fairly discriminate this.

Parasocial Relationship

  • Emotional connection
  • Maximize the gradient between providing social good and knowledge and making the experience personal and emotional. (Guest time might not be good for retention)
  • Authority and empathy are the key to having a big outreach. (Talk to the viewer rather than the audience)
  • People watch for the work, but more often they like the person and feel that connection. (Nijisanji)
  • People are just hanging out with “friends” in the twitch chat.
  • People dont want to watch the loud person, but rather the down to earth personal guy.
  • Selling friendship, people want personal connection and are willing to become a patreon or subscriber to get friendship perks (onlyfans).

Challenges

  • People gravitate to their own beliefs, how do we change this and also get people to stay on the channel?
  • Will people donate when they want to donate to influencer rather than charity?
  • How to get a large following without using parasocial relationships, or help people from being exploited by this? (healthy balance)
influencingsocialpsychologysociology

Passion vs. Hobby

Fairness Literature