Name: Tiffany Jiang
Date: 10/17/2023
I received assistance from: NO ONE
I assisted: NO ONE
Algorithms note down that you like something. It then sees who also likes that something. Then, it sees what those people also like. The system then reccomends the things that other people like.
The more you interact with something, the more data the system has on that particular something, thus creating a bias. The system will continue to reccomend things related to that particular something, thus narrowing the scope and range of the content you will be reccomended.
To maintain user interest and interaction, and thus advertisement view rate and revenue, many algorithms inadvertably amplify controversial yet action-inducing media. Conspiracy theories and "hot takes" are such examples.
Refer to the above argument. Because advertisement view rate and revenue is the end goal, platform admins often ignore sensational misinformation.
People with ill intent can deliberately create and promote aforementioned sensational misinformation to a greater extent by taking advantage of the biased algorithm.
One example of misinformation is discourse. I, as an enjoyer of certain characters in certain shows, often see the arguments of others over said characters on my Twitter home page. I also have a few friends who have noticed that no matter how much they attempt to block key words relating to such discourse while continuing to be able to interact with their mutuals positively over said characters, it always lands back onto their home feed. This goes to show the effects of Algorithmic Amplification.