About this book
Human attention is in the highest demand it has ever been. The drastic increase in available information has compelled individuals to find a way to sift through the media that is literally at their fingertips. Content recommendation systems have emerged as the technological solution to this social and informational problem, but they’ve also created a bigger crisis in confirming our biases by showing us only, and exactly, what it predicts we want to see.
Data versus Democracy investigates and explores how, in the era of social media, human cognition, algorithmic recommendation systems, and human psychology are all working together to reinforce (and exaggerate) human bias. The dangerous confluence of these factors is driving media narratives, influencing opinions, and possibly changing election results.
In this book, algorithmic recommendations, clickbait, familiarity bias, propaganda, and other pivotal concepts are analyzed and then expanded upon via fascinating and timely case studies: the 2016 US presidential election, Ferguson, GamerGate, international political movements, and more events that come to affect every one of us.
What are the implications of how we engage with information in the digital age? Data versus Democracy explores this topic and an abundance of related crucial questions. We live in a culture vastly different from any that has come before. In a society where engagement is currency, we are the product. Understanding the value of our attention, how organizations operate based on this concept, and how engagement can be used against our best interests is essential in responsibly equipping ourselves against the perils of disinformation.
propaganda algorithms social media disinformation misinformation attention economy cognition Cambridge Analytica GamerGate election hacking ad-tech
- DOI https://doi.org/10.1007/978-1-4842-4540-8
- Copyright Information Kris Shaffer 2019
- Publisher Name Apress, Berkeley, CA
- eBook Packages Professional and Applied Computing Professional and Applied Computing (R0)
- Print ISBN 978-1-4842-4539-2
- Online ISBN 978-1-4842-4540-8
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