Using automated digital systems to thoroughly regulate social governance: monitoring and behavior modification through data-driven algorithmic decision-making

Abstract

I inspect the relevant literature on data-driven algorithmic decision-making, providing both quantitative evidence on trends and numerous in-depth empirical examples. Building my argument by drawing on data collected from HubSpot, Pew Research Center, and Statista, I performed analyses and made estimates regarding % of users who say they frequently/sometimes see content on social media that makes them feel amused/angry/ connected/inspired/depressed/lonely, % of users who say they more often see people being mean or bullying/kind or supportive/trying to be deceptive/trying to point out inaccurate info when using social media sites, and % of users who say it would be very/somewhat difficult/easy for social media sites to figure out their race or ethnicity/hobbies and interests/political affiliation/religious beliefs. Data collected from 4,800 respondents are tested against the research model by using structural equation modeling.