Dataclysm: Who We Are (When We Think No One's Looking)
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Quantified Self movement
“Quantitative Analysis of Culture Using Millions of Digitized Books” (Michel and Aiden), n
race, itr.1, itr.2, itr.3, 7.1, 8.1, 8.2
attractiveness and, 6.1, 6.2
four largest groupings by
Internet use and, itr.1, 6.1, 6.2
jokes about, 8.1n, 8.2, 9.1
quantitative analysis of, 6.1, nts.1
rhetoric about
tokenism and
racism, itr.1, 1.1, 5.1, 8.1, 9.1, 11.1
data on, itr.1, 6.1, nts.1
dating and, 6.1, 6.2
expression of, itr.1, 6.1, 8.1, 9.1, nts.1
Obama on
pervasiveness of, 6.1, 8.1
politics and
stereotypes of, 8.1, 10.1
radio
CB, 9.1, nts.1
ratings
compatibility, 6.1, 6.2, 6.3, 6.4
congressional, itr.1, itr.2
of men and women, itr.1, itr.2, itr.3, itr.4, itr.5, 1.1
pizza, itr.1, itr.2
Reagan, Ronald
Reddit, itr.1, itr.2, itr.3, 2.1, 12.1, 13.1, 14.1n, nts.1, nts.2
community and
subreddit pages on, 2.1n, 12.1
relationships
assimilated
bonds of, 4.1, 4.2, 4.3
breakup of, 1.1, 4.1
common interests in
connectors in, 4.1, 4.2
of couples, 1.1, 4.1, 5.1, bm2.1
courtship, 1.1, 4.1
evaluation of
family
leading separate lives in, 4.1, 4.2
progression of
“real life,”
romantic, 1.1, 2.1, 4.1, 4.2, 5.1, 6.1, 7.1, nts.1
stability in, itr.1, 4.1
see also dating; friends; marriage
Republican National Convention of 2008
Republican Party, 5.1, 8.1, 13.1, 14.1, nts.1
Richter scale, 7.1, 12.1, nts.1
Rieger, Gerulf, 11.1, nts.1
Romans, ancient
Romney, Mitt, Twitter followers of, 13.1, 13.2, nts.1
Rorschach tests
Rove, Karl
Russia, 9.1n, bm1.1
Ruthstrom, Ellyn, 11.1, nts.1
Sacco, Justine, 9.1, 9.2, 12.1, 13.1, nts.1
Salesforce.com, 13.1, 13.2, nts.1
Salk, Jonas
Samsung
Sapolsky, Robert, 7.1, nts.1
SAT
science, itr.1, 1.1, 2.1, 3.1, 6.1, 9.1
computer, 4.1, 13.1, 14.1
data, itr.1, itr.2, 2.1, 8.1n, 12.1, 12.2, 13.1, 14.1, 14.2, bm1.1, bm2.1, bm2.2
genetic
network analysis
political
social, itr.1, itr.2, 5.1, 6.1, 8.1, 9.1, 10.1, bm2.1
Scientific American, 14.1, 14.2, nts.1, nts.2
Scruff, n
Seacrest, Ryan
seismology, 7.1, 12.1
selfies
September 11, 2001, terrorist attacks
sex, itr.1, 1.1, 6.1, 8.1, 10.1, 11.1
attractiveness and, itr.1, 1.1, 2.1, 6.1, 7.1, 7.2, bm2.1
casual, 5.1, 11.1
regret and, itr.1, nts.1
threesome
see also bisexuality; homosexuality; lesbianism; lust
Shakespeare, William
Sharpton, Al
Shazam
Shiftgig, 7.1, 7.2, nts.1
showers, 12.1, 12.2
Silver, Nate, 11.1, 11.2, 14.1, nts.1
Simmons, Gene
“six degrees of separation” theory
Slackers (film)
Slate, itr.1n, 3.1, 13.1, nts.1
smartphones, itr.1, 12.1, 12.2
smell, sense of, 2.1, nts.1
Snapchat
Snowden, Edward, 14.1, 14.2
social desirability bias
social graphs, 4.1, 4.2, 4.3, 4.4
social media, 4.1, 6.1, 7.1, 9.1, 9.2, 13.1, 13.2, 14.1, nts.1
unrest and protest fanned on
social physics
solar eclipse of 1919
Sorell, C. Joseph, 10.1n, nts.1
Sparks, Nicholas
speech
hate, 8.1, 9.1
partisan
Spielberg, Steven
sports, 6.1, 8.1, 10.1, 12.1
Stanford-Binet test
states’ rights
statistics, itr.1, 6.1, 6.2, 10.1, 10.2
Stephens-Davidowitz, Seth, 8.1n, 8.2, 11.1, 11.2, bm2.1, nts.1, nts.2, nts.3
stock market predictions
Street Fighter II
string theory
Strunk, William
Suler, John
Supreme Court, US, 8.1, 13.1
symmetric beta distribution
Taboo (game)
talking points
Target, 13.1, nts.1
tattoos, 2.1, 2.2
taxation, 8.1, 14.1, 14.2
Tea Party, 8.1, 9.1
technology, itr.1, itr.2, 4.1, 5.1, 9.1, 12.1, 13.1, 14.1
cultural effect of, 3.1, 3.2, 9.1
harnessing of
telephones, itr.1, 3.1, 3.2, 3.3, 4.1, 4.2, 9.1
television, 6.1, 6.2, 14.1n
Tennyson, Alfred, Lord
terrorism
Texas, 8.1, 12.1, 12.2
text messages, 3.1, 3.2, 14.1
average length of, 3.1, 3.2, 3.3
copy-and-paste vs. from-scratch
keystrokes used on, 3.1, 3.2, 3.3
response rates to, 3.1, 3.2, 3.3
revision of
time spent on, 3.1, 3.2
Thoreau, Henry David, 11.1, nts.1
thought, 1.1, 8.1, 8.2
time, 3.1, 3.2, 8.1
passage of, 3.1, 3.2
spent on messages, 3.1, 3.2
Tinder, itr.1, 7.1
tribes, 3.1, 7.1, 7.2, 9.1, nts.1
Trump, Donald
Tufte, Edward R., bm1.1, nts.1
Tumblr, itr.1, 7.1, 9.1, 9.2, nts.1, nts.2
Clients from Hell posts on
Twitter, itr.1, itr.2, itr.3, itr.4, itr.5, 3.1, 3.2, 3.3, 4.1, 8.1, 9.1, 12.1, 12.2, 13.1, nts.1
average word length on
black users of, 13.1, nts.1
common hashtags on, 13.1, 13.2, 13.3
followers on, 13.1, 13.2, 13.3
#HasJustineLandedYet topic on, 9.1, 9.2, nts.1
language style and vocabulary on, 3.1, 13.1, nts.1
messaging patterns of subgroups on
most common words on, 3.1, 10.1, 10.2
140-character limit on, 3.1, 3.2
TeamFollowBack on, 13.1, 13.2
Trending Topics list on
tweets and retweets on, itr.1, itr.2, 3.1, 3.2, 3.3, 6.1, 9.1, 9.2, 9.3, 9.4, 12.1, 12.2, 13.1, 13.2, 13.3, 13.4, nts.1
TwitterWind
ugliness, 1.1, 2.1, 6.1, 8.1
race and
social costs of
Ulysses (Joyce)
uniform resource locators (URLs), 2.1, 3.1n
Union of Soviet Socialist Republics (USSR), 12.1, nts.1
United Kingdom (UK), 6.1, 12.1, 13.1, 14.1, nts.1
United States, 6.1, 8.1, 8.2, 12.1
Internet usage in
moving in, 12.1, nts.1
national security apparatus of, itr.1, 14.1
Twitter use in
universal product code (UPC)
Utsunomiya
variance concept
verbs, 3.1, 3.2
Viet-Cong, 8.1, nts.1
Vietnam Memorial, bm1.1, nts.1
Vietnam War, 8.1, bm1.1, nts.1
visual perception, itr.1n, 6.1
Wall Street Journal, 7.1, nts.1
Walmart, 12.1, 12.2, 12.3, nts.1
Warden, Pete
Washington, DC, “Million” marches on, 14.1, nts.1
Washington Post, 14.1, 14.2, 14.3, nts.1
Waters, John, 2.1, 2.2, nts.1
Watson, James
wealth, 6.1, 7.1,
7.2n, 7.3, 11.1, 13.1
One Percent of
websites, itr.1, 4.1, 6.1, 12.1, 12.2
company
dating, itr.1, itr.2, itr.3, 1.1, 1.2, 2.1, 2.2, 3.1, 3.2, 4.1, 4.2, 5.1, 7.1, 12.1
job, itr.1, 7.1, 7.2
person-to-person interaction on, itr.1, itr.2, 2.1, 5.1, 6.1
ratings on, itr.1, itr.2, itr.3, itr.4, itr.5, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.1, 2.2, 6.1
social, itr.1, 4.1, 6.1
see also specific websites
WEIRD research, itr.1, 7.1n, nts.1
Wendy’s, 13.1, nts.1
“What Is Beautiful Is Good,” 7.1, nts.1
WhatsApp
WhoBeefed81
Who Owns the Future? (Lanier)
“Why Do White People Have Thin Lips?” (Baker and Potts), 8.1, nts.1
Wikipedia, 10.1, nts.1, nts.2
Wilson, Lorne John, bm1.1, nts.1
Windows, 4.1, nts.1
Wodehouse, P. G., 3.1, 3.2
Wolf, Naomi
women:
aging of, 1.1, 1.2, 1.3, 1.4
Asian, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 10.1, 10.2, 10.3
attraction of men to, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 2.1, 2.2, 2.3, 5.1, 5.2, 6.1, 6.2, 6.3, 6.4
attractiveness of men to, 1.1, 1.2, 1.3, 5.1, 5.2
black, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 10.1, 10.2
contacting of men by
dating pool of
extra pounds on
Latina, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 10.1, 10.2
men’s contacting of, 1.1, 1.2, 1.3, 1.4, 2.1, 3.1
menstrual cycles of
pregnancies of, 9.1, 13.1, 14.1, nts.1
straight, 11.1
unconventional-looking, 2.1, 2.2, 2.3
white, itr.1, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 10.1, 10.2
Wooderson’s law, 1.1, bm2.1
words:
antithetical, 10.1, 10.2, 10.3
changes in use of, 3.1, 3.2, 3.3
ethnic preferences for, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9
food, 3.1, 3.2
frequency of, 3.1, 3.2, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9
gender preferences for, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 10.10
negative, 8.1, 9.1, 9.2
“netspeak,”
self-descriptive, 10.1, 10.2
shortening and contraction of, 3.1, 3.2, 3.3, 13.1
as social connectors, 3.1, 3.2
of Twitter users, 13.1
typing of, 3.1, 3.2, 3.3, 3.4, 3.5, 8.1
written
World War II
Wortham, Jenna, 13.1, 14.1, nts.1, nts.2
writing
changing culture of, itr.1, 3.1, 3.2, 3.3
Xbox One, 14.1, nts.1
Yahoo, 14.1, nts.1
Youth for Understanding program
YouTube, 3.1, 5.1, 12.1
Zimmerman, George, 8.1, nts.1
Zinn, Howard
Zipf’s law, 10.1, nts.1
Zipf’s Law and Vocabulary (Sorell), 10.1n
Zook, Matthew, 12.1, nts.1