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Using Automated Digital Systems to Thoroughly Regulate Social Governance: Monitoring and Behavior Modification through Data-driven Algorithmic Decisio ...

Using Automated Digital Systems to Thoroughly Regulate Social Governance: Monitoring and Behavior... 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. Keywords: automated digital system; data-driven algorithmic decision-making; society http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Contemporary Readings in Law and Social Justice Addleton Academic Publishers

Using Automated Digital Systems to Thoroughly Regulate Social Governance: Monitoring and Behavior Modification through Data-driven Algorithmic Decisio ...

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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
1948-9137
eISSN
2162-2752
Publisher site
See Article on Publisher Site

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. Keywords: automated digital system; data-driven algorithmic decision-making; society

Journal

Contemporary Readings in Law and Social Justice – Addleton Academic Publishers

Published: Jan 1, 2019

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