A social media algorithm is a computer algorithm that determines whether and in what order social media content is displayed. Social media algorithms are designed to maximize user engagement by trying to predict what content a user would prefer to see, as the more time someone spends on the site, the more ad revenue is earned. Algorithms may improve via machine learning or via manual A/B testing.
Social media algorithms are usually closed-source, as they are considered trade secrets that confer a competitive advantage. They are also used to limit the reach of spam, in a sort of arms race that would make it harder to battle spam if the algorithms were open source. The lack of algorithmic transparency has become an issue in the press, with some calling for regulation of algorithms.
There are many social impacts of algorithms, ranging from allegations that social media algorithms are intentionally designed to be addicting, to allegations that they are biased towards various group based on race, gender, and political leaning.
There is also a phenomenon called a filter bubble, in which the social media algorithm is so good at predicting what a user wants to see that their view of things becomes fairly limited. They see little to no contrasting viewpoints, and their own viewpoint is only reinforced. Taken to the limit, it's possible that this leads to algorithmic radicalization, where someone's views become progressively more extreme.
Other AI algorithms
There are other artificial intelligence algorithms that have similar social impacts.
- Algorithms of Oppression (if I decide to expand the scope of this article)
- Algorithmic transparency
- PageRank (if I decide to expand the scope of this article)
- Regulation of algorithms
- Right to explanation
- The Social Dilemma
- Dickey, Megan Rose (30 April 2017). "Algorithmic Accountability". TechCrunch. Retrieved 4 September 2017.
- "Algorithms have gotten out of control. It's time to regulate them". theweek.com. 3 April 2019. Retrieved 22 March 2020.
- "Study of YouTube comments finds evidence of radicalization effect". TechCrunch. Retrieved 2021-03-10.
- Bartlett, Robert; Morse, Adair; Stanton, Richard; Wallace, Nancy (June 2019). "Consumer-Lending Discrimination in the FinTech Era". NBER Working Paper No. 25943. doi:10.3386/w25943.