Dataclysm: Who We Are (When We Think No One’s Looking)
Christian Rudder
An irreverent, provocative, and visually fascinating look at what our online lives reveal about who we really are--and how this deluge of data will transform the science of human behavior. Big Data is used to spy on us, hire and fire us, and sell us things we don't need. In Dataclysm, Christian Rudder puts this flood of information to an entirely different use: understanding human nature. Drawing on terabytes of data from Twitter, Facebook, Reddit, OkCupid, and many other sites, Rudder examines the terrain of human experience. He charts the rise and fall of America's most reviled word through Google Search, examines the new dynamics of collaborative rage on Twitter, and traces human migration over time, showing how groups of people move from certain small towns to the same big cities across the globe. And he grapples with the challenge of maintaining privacy in a world where these explorations are possible. Audacious, entertaining, and illuminating, Dataclysm is a portrait of our essential selves--and a first look at a revolution in the making.
Award History
0 wins · 1 total
| Award | Year | Result | Category / Notes |
|---|---|---|---|
| Los Angeles Times Book Prize for Science and TechnologyMajor | 2014 | Finalist | Secondary source |
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies
This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.
01 Central figures
None extracted with sufficient confidence.
02 Central places
None extracted with sufficient confidence.
03 Suggested argument
The vast data from online life can be used to reveal and understand human nature, transforming the science of human behavior despite risks such as spying, hiring/firing, and commercial manipulation.
Model confidence 72%
04 Reading orientation
25 / 100 academicSerious trade
An estimate of intended readership and scholarly apparatus—not quality or importance.
Confidence percentages are the model's own estimates. Profile confidence: 74%.