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Lecture Three

Lecture Three
Stories of Complex Sociotechnical Systems:
Measurement, Mechanisms, and Meaning
Lipari Summer School, Summer, 2012
Complex
Sociotechnical
Systems
Complex
Sociotechnical
Systems
Press...
Measuring
Happiness
Measuring
Happiness
Some motivation
Some motivation
Measuring emotional
content
Measuring emotional
content
Data sets
Data sets
I
Analysis
Songs
Prof. Peter Dodds
Department of Mathematics & Statistics | Center for Complex Systems |
Vermont Advanced Computing Center | University of Vermont
Blogs
Tweets
“Social Scientists wade into the Tweet
stream” by Greg Miller,
Science, 333, 1814–1815, 2011 [15]
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
Positivity Bias
I
I
“Does a Nation’s Mood Lurk in Its Songs and
Blogs?” by Benedict Carey
New York Times, August 2009. ()
References
More here: http://www.uvm.edu/∼pdodds/research/ ()
Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.
1 of 83
Complex
Sociotechnical
Systems
Outline
Measuring Happiness
Some motivation
Measuring emotional content
Data sets
4 of 83
Complex
Sociotechnical
Systems
Happiness:
Measuring
Happiness
Measuring
Happiness
Some motivation
Some motivation
Measuring emotional
content
Measuring emotional
content
Data sets
Data sets
Analysis
Analysis
Songs
Songs
Blogs
Blogs
Tweets
Analysis
Songs
Blogs
Tweets
Tweets
Positivity Bias
Positivity Bias
References
References
Positivity Bias
Socrates et al.:
eudaimonia [8]
References
Bentham:
hedonistic
calculus
Jefferson:
. . . the pursuit of
happiness
2 of 83
Papers and so on:
“Temporal patterns of happiness and
information in a global social network:
Hedonometrics and Twitter”
Dodds et al., PLoS ONE, 2011 [7]
Much better version here:
http://arxiv.org/abs/1101.5120 ()
Complex
Sociotechnical
Systems
Measuring
Happiness
“Positivity of the English Language”
Kloumann et al., PLoS ONE, 2012 [11]
I
“Measuring the Happiness of Large-Scale Written
Expression: Songs, Blogs, and Presidents”
Dodds and Danforth, Journal of Happiness Studies,
2009 [6]
I
language assessment by Mechanical Turk
(labMT 1.0)
I
http://www.onehappybird.com ()
Early drafts:
Complex
Sociotechnical
Systems
Measuring
Happiness
Some motivation
Some motivation
Measuring emotional
content
Measuring emotional
content
Data sets
Data sets
Analysis
Analysis
Songs
Songs
Blogs
Blogs
Tweets
I
6 of 83
Tweets
Positivity Bias
Positivity Bias
References
References
3 of 83
7 of 83
Desiring happiness—not just for boffins:
I
I
Average people routinely report being happy is what
they want most in life [12, 13, 5]
And it matters: “Happy people live longer:. . . ”
Survey by Diener and Chan. [5]
Complex
Sociotechnical
Systems
Measuring
Happiness
Some motivation
Measuring emotional
content
Data sets
Analysis
BREVIA
Emotional content
A Wandering Mind Is an
Unhappy Mind
Matthew A. Killingsworth* and Daniel T. Gilbert
So how does
one measure
U
2. levels of other emotional states?
nlike other animals, human beings spend
lot of time thinking about what is not
1. happiness?agoing
on around them, contemplating
Songs
Just ask people how happy they are.
Blogs
Tweets
Positivity Bias
I
Experience sampling
I
Day reconstruction
References
National indices of
well-being:
I
Bhutan
I
France
I
Australia
(Csikszentmihalyi et al.)
(Kahneman et al.)
But self-reporting has some drawbacks:
I
relies on memory and self-perception
I
induces misreporting
I
costly
8 of 83
Complex
Sociotechnical
Systems
An easy knock:
events that happened in the past, might happen
in the future, or will never happen at all. Indeed,
“stimulus-independent thought” or “mind wandering” appears to be the brain’s default mode
of operation (1–3). Although this ability is a remarkable evolutionary achievement that allows
people to learn, reason, and plan, it may have an
[2, 4,
emotional cost. Many philosophical
and3]
religious
traditions teach that happiness is to be found by
living in the moment, and[9]
practitioners are trained
to resist mind wandering and “to be here now.”
These traditions suggest that a wandering mind is
an unhappy mind. Are they right?
Laboratory experiments have revealed a great
deal about the cognitive and neural bases of mind
wandering (3–7), but little about its emotional
consequences in everyday life. The most reliable
method for investigating real-world emotion is experience sampling, which involves
contacting peo[14]
ple as they engage in their everyday activities and
asking them to report their thoughts, feelings, and
actions at that moment. Unfortunately, collecting
real-time reports from large numbers of people as
they go about their daily lives is so cumbersome
and expensive that experience sampling has rarely
been used to investigate the relationship between
mind wandering and happiness and has always
been limited to very small samples (8, 9).
We solved this problem by developing a Web
application for the iPhone (Apple Incorporated,
Cupertino, California), which we used to create
an unusually large database of real-time reports
of thoughts, feelings, and actions of a broad range
of people as they went about their daily activities. The application contacts participants through
their iPhones at random moments during their
waking hours, presents them with questions,
and records their answers to a database at www.
trackyourhappiness.org. The database currently
contains nearly a quarter of a million samples
from about 5000 people from 83 different countries who range in age from 18 to 88 and who
collectively represent every one of 86 major occupational categories.
To find out how often people’s minds wander,
what topics they wander to, and how those wanderings affect their happiness, we analyzed samples
from 2250 adults (58.8% male, 73.9% residing in
the United States, mean age of 34 years) who were
randomly assigned to answer a happiness question
(“How are you feeling right now?”) answered on a
continuous sliding scale from very bad (0) to very
good (100), an activity question (“What are you
doing right now?”) answered by endorsing one or
References and Notes
Happiness, attention, and doing:
Measuring
Happiness
Some motivation
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
Fig. 1. Mean happiness reported during each activity (top) and while mind wandering to unpleasant topics, neutral topics, pleasant topics or not
mind wandering (bottom). Dashed line indicates
mean of happiness across all samples. Bubble area
indicates the frequency of occurrence. The largest
bubble (“not mind wandering”) corresponds to
53.1% of the samples, and the smallest bubble
(“praying/worshipping/meditating”) corresponds to
0.1% of the samples.
1. M. E. Raichle et al., Proc. Natl. Acad. Sci. U.S.A
(2001).
2. K. Christoff, A. M. Gordon, J. Smallwood, R. Smith
J. W. Schooler, Proc. Natl. Acad. Sci. U.S.A. 10
12 of 83
(2009).
3. R. L. Buckner, J. R. Andrews-Hanna, D. L. Schac
Ann. N. Y. Acad. Sci. 1124, 1 (2008).
4. J. Smallwood, J. W. Schooler, Psychol. Bull. 132, 94
5. M. F. Mason et al., Science 315, 393 (2007).
6. J. Smallwood, E. Beach, J. W. Schooler, T. C. Ha
Complex
J. Cogn. Neurosci. 20, 458 (2008).
7.Sociotechnical
R. L. Buckner, D. C. Carroll, Trends Cogn. Sci. 11, 4
8. J. C. McVay, M. J. Kane, T. R. Kwapil, Psychon.
Systems
16, 857 (2009).
9. M. J. Kane et al., Psychol. Sci. 18, 614 (2007).
10. D. Kahneman, A. B. Krueger, D. A. Schkade, N.
A. A. Stone, Science 306, 1776 (2004).
11. A. B. Krueger, D. A. Schkade, J. Public Econ. 92, 183
Measuring
12. Materials and methods are available as support
Happiness
material on Science Online.
13. Some
J. Smallwood,
A. Fitzgerald, L. K. Miles, L. H. Ph
motivation
Emotion 9,emotional
271 (2009).
Measuring
14. content
We thank V. Pitiyanuvath for engineering www.
trackyourhappiness.org and R. Hackman, A. Jenkin
Data sets
W. Mendes, A. Oswald, and T. Wilson for helpful co
Supporting
Online Material
Analysis
www.sciencemag.org/cgi/content/full/330/6006/932/D
Songs
Materials and Methods
TableBlogs
S1
References
Tweets
18 May 2010; accepted 29 September 2010
Positivity Bias
10.1126/science.1192439
References
Harvard University, Cambridge, MA 02138, USA.
*To whom correspondence should be addressed
[email protected]
Killingsworth
and Gilbert,
VOL 330 SCIENCE
www.sciencemag.org
Science, 2010 [10]
932
Science = Orwell
more of 22 activities adapted from the day reconstruction method (10, 11), and a mind-wandering
question (“Are you thinking about something
other than what you’re currently doing?”) answered
with one of four options: no; yes, something pleasant; yes, something neutral; or yes, something unpleasant. Our analyses revealed three facts.
First, people’s minds wandered frequently, regardless of what they were doing. Mind wandering
occurred in 46.9% of the samples and in at least
30% of the samples taken during every activity
except making love. The frequency of mind wandering in our real-world sample was considerably
higher than is typically seen in laboratory experiments. Surprisingly, the nature of people’s activities had only a modest impact on whether their
minds wandered and had almost no impact on the
pleasantness of the topics to which their minds
wandered (12).
Second, multilevel regression revealed that people were less happy when their minds were wandering than when they were not [slope (b) = –8.79,
P < 0.001], and this was true during all activities,
Complex
Sociotechnical
including
the least enjoyable. Although p
minds
were more likely to wander to pleasan
Systems
(42.5% of samples) than to unpleasant
(26.5% of samples) or neutral topics (31%
ples), people were no happier when thinkin
Measuring
pleasant
topics than about their current activ
Happiness
–0.52,
not significant) and were considera
happier
thinking about neutral topi
Somewhen
motivation
–7.2,Measuring
P < 0.001)
or unpleasant topics (b =
emotional
P < content
0.001) than about their current activity
Data sets
bottom).
Although negative moods are
to cause mind wandering (13), time-lag a
Analysis
strongly
suggested that mind wandering
Songs
sample
was generally the cause, and not
the Blogs
consequence, of unhappiness (12).
Tweets
Third,
what people were thinking was
predictor of their happiness than was wh
Positivity Bias
were doing. The nature of people’s activi
plained
4.6% of the within-person variance
References
piness and 3.2% of the between-person vari
happiness, but mind wandering explained
of within-person variance in happiness and
of between-person variance in happiness. T
iance explained by mind wandering was
independent of the variance explained by
ture of activities, suggesting that the two w
dependent influences on happiness.
In conclusion, a human mind is a wan
mind, and a wandering mind is an unhappy
The ability to think about what is not hap
is a cognitive achievement that comes at a
tional cost.
12 NOVEMBER 2010
Policy = Brave New World
10 of 83
Complex
Sociotechnical
Systems
13 of 83
We’d like to build an ‘hedonometer’:
I
Measuring
Happiness
Some motivation
Measuring emotional
content
An instrument to ‘remotely-sense’
emotional states and levels, in real
time or post hoc.
Data sets
Complex
Sociotechnical
Systems
Measuring
Happiness
Some motivation
Measuring emotional
content
Data sets
Analysis
Analysis
Songs
Songs
Ideally:
Blogs
Tweets
Blogs
Tweets
Positivity Bias
References
Positivity Bias
I
Transparent
I
Fast
I
Based on written
expression
I
Uses human evaluation
I
Non-reactive
I
Complementary to
self-reported measures
I
Improvable
References
Some possibilities:
I
See story here () for example [slate].
11 of 83
I
Natural language processing (e.g., OpinionFinder)
I
Declared mood levels in blogs (e.g., Livejournal) [16]
14 of 83
Complex
Sociotechnical
Systems
ANEW study
I
I
ANEW = “Affective Norms for English Words”
Study: participants shown lists of isolated words
Complex
Sociotechnical
Systems
Analysing text:
Measuring
Happiness
Measuring
Happiness
Some motivation
Some motivation
Measuring emotional
content
Measuring emotional
content
Data sets
Data sets
Analysis
Analysis
Songs
I
I
Asked to grade each word’s valence, arousal, and
dominance level
Integer scale of 1–9
I
N =1034 words—previously identified as bearing
emotional weight
I
Participants = College students (*cough*)
I
Results published by Bradley and Lang (1999) [1]
Blogs
Tweets
Positivity Bias
References
ANEW
words
Lyrics for
Michael Jackson’s Billie Jean
“She was more like a beauty queen
from a movie scene.
And mother always told me,
be careful who you love.
And be careful of what you do
’cause the lie becomes the truth.
Billie Jean is not my lover,
She’s just a girl who claims
that I am the one.
k=1. love
2. mother
3. baby
4. beauty
5. truth
6. people
7. strong
8. young
9. girl
10. movie
11. perfume
12. queen
13. name
14. lie
Songs
vk
8.72
8.39
8.22
7.82
7.80
7.33
7.11
6.89
6.87
6.86
6.76
6.44
5.55
2.79
fk
1
1
3
1
1
2
1
2
4
1
1
1
1
1
X
vtext = k
Blogs
vk fk
X
k
Tweets
Positivity Bias
fk
References
vBillie Jean = 7.1
vThriller = 6.3
vMichael = 6.4
Jackson
15 of 83
ANEW study—three 1–9 scales:
valence:
Complex
Sociotechnical
Systems
18 of 83
Complex
Sociotechnical
Systems
Data sets:
Measuring
Happiness
Some motivation
Measuring emotional
content
Data sets
Analysis
Measuring
Happiness
Texts:
Some motivation
Measuring emotional
content
1. Song lyrics (1960–2007)
Data sets
Analysis
2. Song titles (1960–2008)
Songs
Songs
Blogs
arousal:
Blogs
3. State of the Union (SOTU) Addresses (1790–2008)
Tweets
Positivity Bias
References
dominance:
Tweets
Positivity Bias
References
Sources:
I
hotlyrics.com ()
I
freedb.com ()
I
American Presidency Project:
www.presidency.ucsb.edu ().
16 of 83
ANEW study words—examples
9
8
valence v
7
6
5
4
3
2
1
0
love/paradise/triumphant
glory/luxury/trophy
optimism/pancakes/church
Complex
Sociotechnical
Systems
Measuring
Happiness
derelict/neurotic/vanity
Complex
Sociotechnical
Systems
Data sets:
4. Blog phrases containing “I feel...”, “I am feeling”, etc.,
taken from wefeelfine.org () (API, 2005–2010)
Measuring
Happiness
Some motivation
Some motivation
Measuring emotional
content
Measuring emotional
content
Data sets
Data sets
Analysis
Analysis
Thanks to ...
Songs
engine/paper/street
20 of 83
Blogs
Tweets
Positivity Bias
Kameron Harris
Songs
Blogs
Tweets
Isabel Kloumann
Catherine Bliss
References
Positivity Bias
References
fault/corrupt/lawsuit
trauma/hostage/disgusted
funeral/rape/suicide
50 100 150 200
frequency
I
ANEW = “Affective Norms for English Words” [1]
17 of 83
Jonathan Harris & Sep Kamvar
wefeelfine.org
Created by
Jonathan Harris
& Sep Kamvar
21 of 83
Complex
Sociotechnical
Systems
wefeelfine.org:
Song Lyrics—average happiness (valence)
Measuring
Happiness
Measuring
Happiness
6.8
Some motivation
Some motivation
6.7
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
mean valence vavg
Measuring emotional
content
Data sets
Complex
Sociotechnical
Systems
Measuring emotional
content
Data sets
6.6
Analysis
Songs
6.5
Blogs
Tweets
6.4
Positivity Bias
6.3
References
6.2
6.1
6
5.9
1960
1970
1980
1990
2000
2010
year
22 of 83
Complex
Sociotechnical
Systems
More data sets:
26 of 83
Song Lyrics—average happiness of genres:
Measuring
Happiness
Measuring
Happiness
Some motivation
Some motivation
7
Measuring emotional
content
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
6. New York Times (20 years)
7. Gutenberg.org
8. Google Books: http://ngrams.googlelabs.com/ ()
mean valence vavg
Data sets
5.
Complex
Sociotechnical
Systems
Analysis
6.5
Songs
Blogs
Tweets
6
Positivity Bias
Gospel/Soul (6.91)
Pop (6.69)
Reggae (6.40)
Rock (6.27)
Rap/Hip−Hop (6.01)
Punk (5.61)
Metal/Industrial (5.10)
5.5
5
9. . . .
4.5
1960
1970
References
1980
1990
2000
2010
year
23 of 83
Complex
Sociotechnical
Systems
Some numbers:
Counts
All words
ANEW words
Individuals
Song lyrics
58,610,849
3,477,575 (5.9%)
∼ 20,000
Song titles
60,867,223
5,612,708 (9.2%)
∼ 632,000
27 of 83
Measuring
Happiness
Per word drop in valence of lyrics from 1980−2007 relative to valence of lyrics from 1960−1979:Measuring
Happiness
Some motivation
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
Counts
All words
Tweets
Individuals
blogs
155,667,394
8,581,226 (5.5%)
∼ 2,335,000
SOTU
1,796,763
61,926 (3.5%)
43
Tweets
Positivity Bias
References
Twitter
∼ 100 billion
∼ 10 billion
∼ 100 million
Word number i
Counts
All words
ANEW words
Individuals
Complex
Sociotechnical
Systems
Happiness Word Shift Graph:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
hate ↑
pain ↑
baby ↓
death ↑
dead ↑
home ↓
sick ↑
fear ↑
hit ↑
hell ↑
fall ↑
sin ↑
lost ↑
sad ↓
burn ↑
lie ↑
scared ↑
afraid ↑
music ↓
life ↑
god ↑
trouble ↓
loneliness ↓
−20
−10
Some motivation
love ↓
lonely ↓
0
Key:
Measuring emotional
content
Data sets
love ↓
baby ↓
home ↓
music ↓
good ↓
Decreases in relatively
high valence words
contribute to drop
in average valence
hate ↑
pain ↑
death ↑
dead ↑
sick ↑
Increases in relatively
low valence words
contribute to drop
in average valence
life ↑
god ↑
truth ↑
party ↑
sex ↑
Increases in relatively
high valence words
contribute to increase
in average valence
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
lonely ↓
sad ↓
trouble ↓
loneliness ↓
devil ↓
Decreases in relatively
low valence words
contribute to increase
in average valence
10
Per word valence shift ∆i
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28 of 83
Complex
Sociotechnical
Systems
Top 16 of ' 20,000 artists:
Artist
All-4-One
Luther Vandross
S Club 7
K Ci & JoJo
Perry Como
Diana Ross & The Supremes
Buddy Holly
Faith Evans
The Beach Boys
Jon B
Dru Hill
Earth Wind & Fire
Ashanti
Otis Redding
Faith Hill
NSync
Valence
7.15
7.12
7.05
7.04
7.04
7.03
7.02
7.01
7.01
6.98
6.96
6.95
6.95
6.93
6.93
6.93
Measuring
Happiness
Some motivation
Measuring emotional
content
Songs
Blogs
Tweets
Positivity Bias
6.4
State of the Union
Messages [6]
New York Times
(1987–2007) [17]
Blogs [6]
Dante’s Inferno
6.1
church (6.28), tree (6.32), air (6.34)
clouds (6.18), alert (6.20), computer
(6.24)
grass (6.12), idol (6.12), bottle (6.15)
6.0
hotel (6.00), tennis (6.02), wonder (6.03)
5.8
5.5
Heavy Metal
lyrics [6]
5.4
owl (5.80), whistle (5.81), humble (5.86)
glacier (5.50), repentant (5.53), mischief
(5.57)
lamp (5.41), elevator (5.44), truck (5.47)
6.3
6.2
References
29 of 83
Complex
Sociotechnical
Systems
Bottom 16 of ' 20,000 artists:
Artist
Slayer
Misfits
Staind
Slipknot
Darkthrone
Death
Black Label Society
Pig
Voivod
Fear Factory
Iced Earth
Simple Plan
Machine Head
Metallica
Dimmu Borgir
Mudvayne
Tweets, 9/9/2008
to 12/31/2010
Rock lyrics [6]
Enron Emails ()
6.7
6.5
Data sets
Analysis
(criteria: ≥ 50 songs and ≥ 1000 ANEW words)
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
havg
6.9
Valence
4.80
4.88
4.93
4.98
4.98
5.02
5.05
5.08
5.14
5.15
5.16
5.16
5.17
5.19
5.20
5.21
Words with a similar score:
chocolate (6.88), leisurely (6.88),
penthouse (6.81)
dream (6.73), honey (6.73), sugar (6.74)
muffin (6.57), rabbit (6.57), smooth
(6.58)
thought (6.39), face (6.39), blond (6.42)
Complex
Sociotechnical
Systems
Blogs
Measuring
Happiness
Measuring
Happiness
6.1
Some motivation
valence (v)
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Text:
Soul/Gospel
lyrics [6]
Pop lyrics [6]
Dante’s Paradise
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
Some motivation
6
Measuring emotional
content
Data sets
5.9
Analysis
5.8
Songs
Blogs
Tweets
5.7
Positivity Bias
5.6
5.5
References
13 20 30 40 50 60 70 80
blogger age
I
Average happiness as a function of the age bloggers
report they will turn in the year of their posting.
(criteria: ≥ 50 songs and ≥ 1000 ANEW words)
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34 of 83
Tref : born in 1960-1969 (havg =5.96)
Tcomp: 14 years old (havg =5.55)
Blogs—Overall trend
1 −↑sick
−↑hate
−↑stupid
−↑sad
5
6.4
US Inauguration 1/20
♥
♥
6.1
♥
6
5.9
♥
5.8
5.7
♥
10
0
10
9/10
9/11
Data sets
Songs
Blogs
Text size:
Tref Tcomp
loved +↑
1
10
fun +↑
20
−↑dead
−↑scared
−↑terrible
2
10
2008
2009
Tweets
Positivity Bias
References
Balance:
−169 : +69
+↓
+↑
friend +↑
people +↑
3
2007
Measuring emotional
content
Analysis
A S OND J FMAM J J A S OND J FMAM J J A S OND J FMAM J J A S OND J FMAM J J A S OND J FMA
2005 2006
Some motivation
happy +↑
love +↑
−↑upset
−↑fat
15
Michael
Jackson
9/10
9/10
Word rank r
average happiness havg
6.3
6.2
Measuring
Happiness
−↑depressed
−↑bored
−↑lonely
−↑alone
−↑mad
−↑pain
+↓life
US Election 11/4
Complex
Sociotechnical
Systems
10
−100
2010
25
Pr
i=1
−20
−↑confused
time −↓
0
δhavg,i
−↑hurt
−10
0
10
−↑
−↓
20
Per word average happiness shift δhavg,r (%)
35 of 83
Tref : Male (havg =5.91)
Tcomp: Female (havg =5.89)
Complex
Sociotechnical
Systems
love +↑
1
−↑hurt
−↑hate
−↑sad
+↓good
−↑alone
5
Measuring
Happiness
Some motivation
Measuring emotional
content
Word rank r
baby +↑
loved +↑
happy +↑
−↑stupid
−↑guilty
−↑sick
10
0
heart +↑
−↑scared
−↑lost
+↓music
+↓free
1
10
20
Analysis
Songs
Blogs
10
15
Data sets
Text size:
Tref Tcomp
Tweets
Positivity Bias
References
Balance:
death −↓ −607 : +507
life +↑
+↓
+↑
family +↑
2
10
+↓christmas
cold −↓
−↑upset
3
10
25
−100
P
r
i=1
friend +↑
dead −↓
0
δhavg,i
−100
−50
0
50
−↑
−↓
100
Per word average happiness shift δhavg,r (%)
Complex
Sociotechnical
Systems
Twitter—living in the now:
Measuring
Happiness
0.16
count fraction
36 of 83
breakfast
0.14
lunch
0.12
dinner
Some motivation
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
0.1
Tweets
Positivity Bias
0.08
References
0.06
0.04
0.02
0
0
2
4
6
8
10 12 14 16 18 20 22 24
hour of day (local time)
38 of 83
Twitter—living in the now:
Complex
Sociotechnical
Systems
Measuring
Happiness
0.07
Some motivation
Word
stomach
mcdonalds
hungry
wings
ham
starving
spaghetti
ihop
noodles
ketchup
fat
sprite
cookin
heartburn
sugar
kool-aid
miller
honey
candy
havg
5.40
5.98
3.38
6.52
5.66
2.58
0.00
0.00
0.00
0.00
3.24
0.00
0.00
0.00
6.74
0.00
5.36
7.44
7.52
rs
0.37
0.30
0.27
0.25
0.24
0.22
0.20
0.19
0.18
0.18
0.18
0.17
0.17
0.17
0.15
0.15
0.15
0.15
0.15
p-value
1.98894e-07
2.60824e-05
0.000206297
0.000388915
0.000763101
0.00272286
0.00689403
0.0100034
0.0106139
0.0145088
0.0148845
0.0175705
0.0182976
0.0200551
0.0329359
0.0354226
0.036325
0.0395531
0.0398618
Words most anti-correlated with obesity
levels in cities:
brunch
bar
banana
barista
delicious
dinner
coffee
espresso
cocktails
booze
mimosa
spiced
veggie
sushi
wines
tofu
panini
gnocchi
clams
caffeine
cocktailin
Twitter—living
bento
huevos
mojitos
vegan
6.32 -0.41
5.82 -0.35
6.86 -0.35
0.00 -0.35
7.92 -0.34
7.40 -0.34
7.18 -0.34
0.00 -0.33
0.00 -0.32
0.00 -0.32
0.00 -0.31
0.00 -0.31
0.00 -0.31
5.40 -0.31
6.28 -0.31
0.00 -0.31
0.00 -0.31
0.00 -0.30
0.00 -0.30
5.80 -0.30
-0.30
0.00 now:
the
0.00 -0.30
0.00 -0.30
0.00 -0.30
4.82 -0.30
6.37431e-09
5.54374e-07
5.67492e-07
7.29324e-07
1.09807e-06
1.35413e-06
2.04145e-06
4.45903e-06
4.96518e-06
6.38461e-06
1.24472e-05
1.52074e-05
1.60439e-05
1.71997e-05
1.7432e-05
1.86278e-05
1.86719e-05
2.51419e-05
2.52124e-05
2.6263e-05
2.63227e-05
2.6349e-05
3.19903e-05
3.52542e-05
3.5502e-05
Measuring emotional
content
0.06
count fraction
Words most correlated with obesity levels in
cities:
0.05
0.03
Measuring
Happiness
Some motivation
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
40 of 83
Complex
Sociotechnical
Systems
Measuring
Happiness
Some motivation
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
41 of 83
Complex
Sociotechnical
Systems
Measuring
Happiness
Some motivation
Measuring emotional
content
Data sets
Data sets
Analysis
Analysis
Songs
Songs
Blogs
Blogs
Tweets
0.04
Complex
Sociotechnical
Systems
Tweets
Positivity Bias
Positivity Bias
References
References
hungry
starving
0.02
food
0.01
0
0
eat
2
4
6
8
10 12 14 16 18 20 22 24
Tweeting the Superbowl () [NY Times]
hour of day (local time)
39 of 83
42 of 83
a
hah
6
aha
hah
ha
10
a
hah
aha
Frequency
hah
hah
aha
aha
4
10
ah
hah
ha
aha
hah
a
hah
2
10
0
10
2
4
6
10
26
hahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahahaha
The happiest distribution:
100 140
Number of Letters (String Length)
labMT 1.0:
language assessment by Mechanical Turk
http:/www.onehappybird.com ()
http://flowingdata.com/2011/10/27/language-communities-of-twitter/ ()
valence
rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
.
.
.
word
valence
std dev
twitter
rank
g-books
rank
nyt
rank
lyrics
rank
laughter
happiness
love
happy
laughed
laugh
laughing
excellent
laughs
joy
successful
win
rainbow
smile
won
pleasure
smiled
rainbows
winning
celebration
enjoyed
healthy
music
celebrating
congratulations
weekend
celebrate
comedy
jokes
rich
.
.
.
8.50
8.44
8.42
8.30
8.26
8.22
8.20
8.18
8.18
8.16
8.16
8.12
8.10
8.10
8.10
8.08
8.08
8.06
8.04
8.02
8.02
8.02
8.02
8.00
8.00
8.00
7.98
7.98
7.98
7.98
.
.
.
0.93
0.97
1.11
0.99
1.16
1.37
1.11
1.10
1.16
1.06
1.08
1.08
0.99
1.02
1.22
0.97
1.07
1.36
1.05
1.53
1.53
1.06
1.12
1.14
1.63
1.29
1.15
1.15
0.98
1.32
.
.
.
3600
1853
25
65
3334
1002
1579
1496
3554
988
2176
154
2726
925
810
1497
–
–
1876
3306
1530
1393
132
2550
2246
317
1606
1444
2812
1625
.
.
.
–
2458
317
1372
3542
3998
–
1756
–
2336
1198
3031
–
2666
1167
1526
3537
–
–
–
2908
3200
875
–
–
–
–
–
–
1221
.
.
.
–
–
328
1313
–
4488
–
3155
–
2723
1565
776
–
2898
439
4253
–
–
1426
2762
3502
3292
167
–
–
833
3574
2566
–
1469
.
.
.
1728
1230
23
375
2332
647
1122
–
2856
809
–
694
1723
349
1493
1398
2248
4216
3646
4070
–
4619
374
–
–
2256
2108
–
3808
890
.
.
.
Complex
Sociotechnical
Systems
Measuring
Happiness
Some motivation
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
48 of 83
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
.
.
.
g-books
rank
nyt
rank
lyrics
rank
.
.
.
violence
cruel
cry
failed
sickness
abused
tortured
fatal
killings
murdered
war
kills
jail
terror
die
killing
arrested
deaths
raped
torture
died
kill
killed
cancer
death
murder
terrorism
rape
suicide
terrorist
.
.
.
1.86
1.84
1.84
1.84
1.84
1.83
1.82
1.80
1.80
1.80
1.80
1.78
1.76
1.76
1.74
1.70
1.64
1.64
1.64
1.58
1.56
1.56
1.56
1.54
1.54
1.48
1.48
1.44
1.30
1.30
.
.
.
1.05
1.15
1.28
1.00
1.18
1.31
1.42
1.53
1.54
1.63
1.41
1.23
1.02
1.00
1.19
1.36
1.01
1.14
1.43
1.05
1.20
1.05
1.23
1.07
1.28
1.01
0.91
0.79
0.84
0.91
.
.
.
4299
2963
1028
2645
4735
–
–
–
–
–
468
2459
1642
4625
418
1507
2435
–
–
3175
1223
798
1137
946
509
2762
–
3133
2124
3576
.
.
.
1724
–
3075
1618
–
–
–
4089
–
–
175
–
–
4117
730
4428
4474
–
–
–
866
2727
1603
1884
307
3110
–
–
4707
–
.
.
.
1238
–
–
1276
–
–
–
–
4914
–
291
–
2573
4048
2605
1672
1435
2974
–
–
208
2572
814
796
373
1541
3192
4115
3319
3026
.
.
.
2016
1447
226
2920
3782
4589
4693
3724
–
4796
462
2857
1619
2370
143
998
–
–
4528
3126
826
430
1273
3802
433
1059
–
2977
2107
–
Complex
Sociotechnical
Systems
Twitter—overall time series:
6.4
Measuring
Happiness
Some motivation
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
average happiness havg
twitter
rank
2008—12/25 2009—
A
References
valence
std dev
twitter
rank
g-books
rank
nyt
rank
lyrics
rank
fE@king
fKKkin
fKKked
pussy
whiskey
slut
cigarettes
fKKk
mortality
cigarette
motherfKKkers
churches
motherfKKking
capitalism
porn
summer
beer
execution
wines
zombies
aids
capitalist
revenge
mcdonalds
beatles
islam
pay
alcohol
muthafKKkin
christ
.
.
.
4.64
3.86
3.56
4.80
5.72
3.57
3.31
4.14
4.38
3.09
2.51
5.70
2.64
5.16
4.18
6.40
5.92
3.10
6.28
4.00
4.28
4.84
3.71
5.98
6.44
4.68
5.30
5.20
3.00
6.16
.
.
.
2.93
2.74
2.71
2.66
2.64
2.63
2.60
2.58
2.55
2.52
2.47
2.46
2.46
2.45
2.43
2.39
2.39
2.39
2.37
2.37
2.35
2.34
2.34
2.33
2.33
2.33
2.32
2.32
2.31
2.31
.
.
.
448
1077
1840
2019
–
–
–
322
–
–
–
–
–
–
1801
896
839
–
–
4708
2983
–
–
3831
3797
–
627
2787
–
2509
.
.
.
–
–
–
–
–
–
–
–
3960
–
–
2281
–
4648
–
1226
4924
2975
–
–
3996
4694
–
–
–
4514
769
2617
–
909
.
.
.
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
721
3960
–
3316
–
1197
–
–
–
–
–
460
3752
–
4238
.
.
.
620
688
904
949
2208
4071
3279
185
–
2678
1466
–
2910
–
–
590
1413
–
–
–
–
–
2766
–
–
–
499
3600
4107
1526
.
.
.
Complex
Sociotechnical
Systems
Data sets
Analysis
Songs
Blogs
Tweets
09/14
02/27
08/06
Positivity Bias
03/11
09/1109/21
07/23 08/0808/23
700
B
600
500
400
300
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
4
C
3
2
1
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Measuring societal happiness through tweets:
6.4
2008—
2009—
12/25
12/25
12/24
01/01
04/12
11/27
C
0.8
1000
0.6
2
600
0.4
0.2
1
400
0
0.5
200
2
3
4
5
havg
6
7
8
9
0
0
D
1
2
3
∆havg
1.5
−0.2
1
2
∆havg
3
% coverage
∆havg
800
12/25
Saturday
11/25
12/24
Sunday
04/29 05/08
11/24
06/19
04/24
12/31
10/31
06/25
04/27
09/29
09/14
02/27
08/06
05/24
06/27
03/11
07/05
07/23
5.8
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
08/08
Jul
08/23 09/11
Aug
Sep
09/21
Oct
Nov
Dec
date
I
Global happiness spikes = predictable rituals.
I
Global sadness spikes = unpredictable, exogeneous
shocks.
I
No accidental happiness outbreaks.
A
Bailout of the U.S. financial system:
Tref : 7 days before and after (havg =6.00)
Tcomp: Monday, 2008/09/29 (havg =5.95)
Royal Wedding of Prince William & Catherine Middleton:
T : 7 days before and after (havg =5.98)
B Tref
comp: Friday, 2011/04/29 (havg =6.04)
−↑no
−↑fail
−↑fails
−↑blame
+↓love
−↑failure
−↑bad
−↑don’t
−↑against
−↑die
−↑rejected
−↑depression
−↑crisis
15
money +↑
weekend +↑
25
+↓won
old −↓
−↑worst
+↓great
−↑panic
+↓awesome
−↑didn’t
+↓google
+↓saturday
2
billion +↑
∆havg
cancer −↓
10
40
−↑sick
−↑problem
−↑crash
+↓friday
−↑falling
1
10
2
10
45
house +↑
10
15000
20000
50
Pr
i=1
−20
married +↑
+↓game
35
Text size:
Tref Tcomp
0
10
Balance:
−165 : +65
+↓
+↑
0
δhavg,i
−10
10
2
+↓lol
10
−↓
20
Per word average happiness shift δhavg,r (%)
50
live +↑
bad −↓
congrats +↑
amazing +↑
kill −↓
ill −↓
nigga −↓
Text size:
wow +↑
Tref Tcomp
died −↓
30
ass −↓
hell −↓
gorgeous +↑ Balance:
congratulations
+↑
−68 : +168
40
couple +↑
3
10
celebrating +↑
ill −↓
−↑hell
celebrate +↑
+↓mom
−↑loss
−↑mourn
35
america +↑
+↓you
−↑deaths
0
10
+↓
+↑
0
P
r
δhavg,i
−30
−20
i=1
kissed +↑
she +↑
killing −↓
100
+↓haha
−10
0
10
10
+↓beautiful
peace +↑
2
+↓fun
−↑evil
+↓good
−↑attack
3
10
20
−↓
30
Per word average happiness shift δhavg,r (%)
10
−100
50
Pr
i=1
−20
Balance:
−143 : +43
+↓
+↑
we +↑
4
−↑
Text size:
Tref Tcomp
1st +↑
sea +↑
usa +↑
1
10
45
+↓friends
4
−↑
0
1
10
−↑gossip
−↑poor
25
+↓life
10
45
miss −↓
4
10
−100
10000
word rank
+↓chocolate
+↓love
+↓win
25
40
+↓home
−↑killed
−↑fear
3
no −↓
−↑hussein
+↓hahaha
−↑terror
+↓like
+↓wedding
−↑terrorism
−↑enemy
+↓lol
−↑shot
20
weekend +↑
friday +↑
party +↑
30
0
3
15
+↓me
+↓good
+↓you
20
+↓sunday
+↓fun
+↓party
+↓game
−↑hurt
+↓win
1
10
no −↓
princess +↑
never −↓
shit −↓
killed −↓
not −↓
dress +↑
real +↑
15
20
−↑killed
+↓love
−↑not
−↑kill
−↑died
−↑killing
+↓happy
−↑saddam
+↓haha
+↓me
−↑war
+↓mothers
−↑terrorist
−↑bad
−↑pakistan
−↑buried
5
+↓easter
+↓happy
10
35
0
dead −↓
dont −↓
death −↓
beautiful +↑
hate −↓
kiss +↑
prince +↑
5
Death of Osama Bin Laden:
Tref : 7 days before and after (havg =5.98)
Tcomp: Monday, 2011/05/02 (havg =5.89)
C
1 −↑dead
−↑death
wedding +↑
1
−↑bill
−↑down
−↑failed
−↑not
10
F
5000
02/14
01/01
06/20
6
5.9
30
E
100
80
60
40
20
0
0
Wednesday
Thursday
07/04
04/04
10/31
Word rank r
01/01/11
100
80
60
40
20
0
% coverage
2.5
Monday
Tuesday
12/31
Friday
05/09
01/01
10/31
10/31
6.1
Word rank r
10/01/10
# words
2∆havg
2011—
12/25
12/24
12/31
02/14
07/04
06/21
12/31
last −↓
Date
B
12/24
11/26
02/14
6.2
5
5.4
07/01/10
2010—
6.3
50 of 83
6
10000
8000
6000
4000
2000
0
0
07/05
References
5.8
1
05/24 06/27
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1
Average happiness havg
06/25
04/27
09/29
05/02
6.2
04/01/10
11/24
06/19
Twitter—overall time series:
Measuring emotional
content
6.4
01/01/10
12/31
12/24
04/2905/08
Some motivation
6.6
10/01/09
02/14
01/01
11/25
6
5.9
1−↑bailout
3
07/04
06/20
Measuring
Happiness
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0
07/01/09
05/09
04/04
04/24
∆havg :
04/01/09
02/14
01/01
10/31
49 of 83
5.6
Frequency
06/21 07/04
12/31
12/31
date
6.8
0
1
02/14
11/27
0
word
01/01/09
Monday
Tuesday
Wednesday
Thursday
12/25
Friday
Saturday
Sunday
2011—
12/25
12/24
05/02
Positivity Bias
A
10/01/08
2010—
12/24 12/31
11/26
04/12
10/31
6.1
5.8
7.2
1200
12/24 01/01
6.2
The very surprising tunable hedonometer:
7
12/25
6.3
Tweets
Simpson lexical size N S
std dev
Word rank r
std dev
rank
valence
word count (x10 7 )
.
.
.
10193
10194
10195
10196
10197
10198
10199
10200
10201
10202
10203
10204
10205
10206
10207
10208
10209
10210
10211
10212
10213
10214
10215
10216
10217
10218
10219
10220
10221
10222
word
average happiness havg
valence
rank
0
δhavg,i
−↑breaking
−↑burial
−10
−↑
0
10
−↓
20
Per word average happiness shift δhavg,r (%)
Some motivation
Measuring emotional
content
havg
Data sets
Analysis
6.35
Songs
Tweets
F
S
S
M
T
W
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F
S
S
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M
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promise
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collection
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asleep
cards
events
sunny
mate
congratulation
estate
september
updates
present
rich
drinking
christian
wii
sony
land
nt
orange
educati
famous
window
dancing
gain
pet
tree
theme
guitar
restaurant
laughing
moms
garden
evening
creativehumor
teach
starbucks
helping
cars
opportun
on
glee
treat
boring
missing
slowbang
fake suck
lie
painpoor
worst
cryfail
traffic
shot
nobody
blood rip
bitches
shoot
stuck
cannot
loss
worry
hospital
−5
6
ymm
oyue
up lol
no
donot
−2
−3
Blogs
T
log10 normalized word frequency f (∆havg )
Measuring
Happiness
6.4
W
∆havg = 1.00
−1
2009−05−21 to 2010−12−31:
6.45
6.3
T
0
Complex
Sociotechnical
Systems
Twitter—weekly time series:
assembled
considerable
equilibrium
cherished
attained
slugs
dividends
granddaughter
trembling
servings
teardrops
ceased
sailed
arises
preserved
adoring
satisfactory
tenderness
comforted
unkind
honky
indictment
−6
tremble
darkened
4
T
W
T
F
S
S
M
T
W
T
F
S
S
M
day of week
Hour 13, 2010
−7
−4
−2
0
2
Average happiness havg - 5
55 of 83
Complex
Sociotechnical
Systems
Tref : Tuesdays (havg =6.03)
Tcomp: Saturdays (havg =6.06)
love +↑
no −↓
haha +↑
party +↑
fun +↑
saturday +↑
0.2
Measuring
Happiness
+↓new
weekend +↑
not −↓
happy +↑
dont −↓
10
−↑last
Word rank r
Analysis
live +↑
die −↓
friends +↑
game +↑
con −↓
movie +↑
cant −↓
:)
!
Blogs
Tweets
−0.8
References
Positivity Bias
References
−1
−1.2
09/09/08
12/01/08
03/01/09
sick −↓
shopping +↑
playing +↑
B
−↑don’t
Text size:
Tcomp
amazing +↑T
ref
bad −↓
awesome +↑
homework −↓
wedding +↑
−↑hangover
−↑miss
+↓free
2
10
45
3
10
4
10
50
Balance:
−87 : +187
+↓
+↑
shit −↓
court −↓
nice +↑
won +↑
0
P
r
i=1
100
−10
movies +↑
−5
0
06/01/09
09/01/09
−1
−2
−3
−4
−5
09/09/08
+↓school
δhavg,i
09/01/09
10
1
10
log rel freq
40
06/01/09
date
+↓lunch
0
Analysis
Songs
Positivity Bias
birthday +↑
10
Data sets
Sad
Tweets
great +↑
sunday +↑
family +↑
beautiful +↑
beach +↑
home +↑
35
:(
Afghanistan
−0.6
Blogs
+↓google
30
Happy
Tea Party
−0.4
Songs
−↑fight
25
Measuring emotional
content
−0.2
Data sets
hahaha +↑
20
Some motivation
Measuring emotional
content
−↑bored
−↑drunk
15
Measuring
Happiness
0
Some motivation
avg
5
Complex
Sociotechnical
Systems
0.4
A
h(amb)
1
4
−↑
12/01/08
03/01/09
date
−↓
5
56 of 83
10
59 of 83
Per word average happiness shift δhavg,r (%)
The daily unravelling of the human mind:
A
B
Tiger Woods
0.2
0
(amb)
avg
h(amb)
avg
0
−0.2
h
−0.4
−1
8
12 16 20 24
4
8
0.03
0
0
2
4
6
8
10 12 14 16 18 20 22 24
C
hour of day (local time)
12/01/09
6
5.95
noon
Wednesday
midnight
noon
Thursday
midnight
noon
05/01/10
07/01/10
Some motivation
date
03/01/10
Data sets
−4
Analysis
−5
05/31/10
Measuring emotional
content
−3
12/01/09
03/01/10
Songs
05/31/10
Blogs
date
T re f: All Tweets (h avg=6.06)
T co m p: Tiger Woods (h avg=5.74)
T ref: All Tweets (h avg=6.04)
T c o m p: BP (h avg=5.57)
Friday
midnight
noon
Saturday
midnight
noon
Sunday
midnight
noon
Monday
midnight
noon
5
wife +↑
car +↑
+↓me
0
santa +↑
10
−↑cheated
15
−↑cheat
2
sex +↑
+↓haha
−↑alone
−↑not
+↓happy
−↑breaking
−↑divorce
10
20
3
10
4
10
−100
25
Pr
movie +↑
0
i =1 δ h av g, i
−20
Text size:
T r e f T c om p
ill −↓
1
10
midnight
Local time
−↑disaster
1
−↑alleged
10
D
no −↓
−↑scandal
+↓love
−↑hospital
−↑cheating
5
6.05
Tuesday
03/01/10
date
−↑accident
−↑crash
−↑injured
1
2010
midnight
01/01/10
Tweets
12 16 20 24
Word rank r
Average happiness havg
09/01/09
6.1
noon
Measuring
Happiness
−1
06/01/10
−4
−5
0.02
6.15
Monday
04/01/10
−3
0.01
4
02/01/10
log10rel freq
10
0.04
hour of day (local time)
5.9
midnight
12/01/09
+↓good
0
Ba la nce:
−188 : +88
+↓
+↑
−↑
−↓
20
Per word average happi ness shi ft δ h av g, r (%)
Word rank r
6
log rel freq
count (%)
havg
6.05
0
10/01/09
date
0.05
6.1
−0.4
−0.8
−0.8
0.06
2009−05−21 to 2010−12−31:
−0.2
−0.6
−0.6
6.15
Complex
Sociotechnical
Systems
BP
0.2
10
0
dont −↓
1
−↑died
billion +↑
con −↓
2
10
−↑blame
−↑criminal
20 3
10
+↓happy
−↑damage
4
−↑costs
10
−100
0
−↑fails
Pr
−↑crisis
25
i =1 δ h av g, i
−20
−10
Text size:
T r e f T c om p
ill −↓
+↓hahaha
10
References
no −↓
−↑down
−↑shut
−↑kill
+↓love
−↑fake
−↑stop
+↓me
+↓haha
−↑not
+↓lol
10
15
Positivity Bias
Ba la nce:
−167 : +67
+↓
+↑
−↑
0
10
−↓
20
Per word average happi ness shi ft δ h av g, r (%)
60 of 83
A
B
Pope
0.2
0
(amb)
avg
h(amb)
avg
−0.2
h
−0.4
−1
02/01/10
04/01/10
06/01/10
Measuring
Happiness
−1
08/01/10
11/01/08
01/01/09
log10rel freq
10
−4
−5
03/01/10
03/01/09
05/01/09
date
date
−3
−3
−4
−5
09/09/08
05/31/10
12/01/08
Measuring
Happiness
Some motivation
Some motivation
Measuring emotional
content
Measuring emotional
content
Data sets
Data sets
Analysis
Analysis
Songs
03/01/09
Songs
Blogs
date
date
Blogs
Tweets
T ref: All Tweets (h avg=6.04)
T c o m p: Pope (h avg=5.40)
−↑abuse
1
Word rank r
sex +↑
15
child +↑
−↑not
−↑resignation
+↓me
+↓haha
−↑crisis
+↓lol
0
10
1
10
Text size:
T r e f T c om p
dont −↓
2
−↑crimes
−↑attacks
−↑accused
−↑deaf
−↑down
4
+↓hahaha
10
−100
0
−↑evil
Pr
−↑rape
25
i =1 δ h av g, i
10
20
5
no −↓
−↑against
+↓love
−↑arrested
10
Ba la nce:
−157 : +57
+↓
+↑
3
10
−50
−↑war
1
−↑scandal
−↑victims
−↑arrest
5
T ref: All Tweets (h avg=6.05)
T c o m p: Israel (h avg=5.30)
D
−↑
0
−↓
50
Per word average happi ness shi ft δ h av g, r (%)
Word rank r
C
Complex
Sociotechnical
Systems
−0.4
−0.8
−0.8
Happiness in Manhattan (just for fun):
−0.2
−0.6
−0.6
log rel freq
Complex
Sociotechnical
Systems
Israel
0.2
0
−↑fired
−↑against
−↑killed
−↑attacks
−↑attack
−↑offensive
−↑fire
−↑crimes
Positivity Bias
References
References
peace +↑
10
−↑no
−↑not
−↑die
−↑killing
−↑stop
15 101
+↓love
−↑bombs
2
−↑fighting
10
−↑kill
−↑kills
20 3
10
−↑conflict
−↑death
4
−↑terrorist
10
−100
0−↑weapons
Pr
25
i
i =1 δ h av g,−↑bombing
Text size:
T r e f T c om p
0
10
−10
Tweets
Positivity Bias
Ba la nce:
−131 : +31
+↓
+↑
−↑
0
See Blog post on onehappybird ()
−↓
61 of 83
10
64 of 83
Per word average happi ness shi ft δ h av g, r (%)
13
Word
1. love
2. happy
3. win
4. kiss
5. cash
6. vacation
7. Christmas
8. God
9. party
10. sex
11. Valentine
12. family
13. sun
14. life
15. hope
16. heaven
17. :)
18. income
19. friends
20. snow
21. :-)
22. night
23. vegan
24. Jesus
25. girl
26. USA
27. you
28. our
29. ;)
30. health
31. tomorrow
32. !
33. summer
34. we
35. today
36. man
37. woman
38. Stephen Colbert
39. ;-)
40. RT
41. coﬀee
42. church
43. work
44. I
45. yes
46. them
47. hot
48. boy
49. yesterday
50. Michael Jackson
(amb)
havg
+1.42
+1.32
+1.26
+1.21
+1.21
+1.11
+1.03
+0.95
+0.93
+0.89
+0.85
+0.79
+0.65
+0.50
+0.48
+0.43
+0.42
+0.36
+0.33
+0.32
+0.32
+0.29
+0.28
+0.27
+0.25
+0.23
+0.22
+0.21
+0.20
+0.20
+0.20
+0.16
+0.13
+0.13
+0.13
+0.12
+0.10
+0.10
+0.10
+0.06
+0.04
+0.03
+0.02
+0.02
+0.02
0.00
-0.01
-0.01
-0.01
-0.02
Total Tweets
46,687,476 (6)
16,541,968 (13)
7,981,856 (26)
1,697,405 (59)
1,279,236 (63)
934,501 (67)
4,887,968 (35)
8,576,364 (25)
6,438,886 (29)
3,551,767 (39)
247,288 (84)
5,014,816 (32)
2,385,348 (52)
14,006,454 (17)
11,833,337 (18)
741,878 (71)
10,470,483 (20)
510,425 (76)
7,669,719 (27)
2,596,165 (49)
1,680,165 (60)
17,089,505 (12)
183,889 (90)
2,027,720 (56)
10,070,132 (22)
2,157,172 (54)
173,276,993 (3)
14,062,465 (16)
2,618,940 (48)
2,575,543 (50)
10,379,637 (21)
3,463,257 (40)
2,998,785 (43)
39,132,934 (7)
25,588,506 (9)
15,856,341 (14)
2,543,036 (51)
23,778 (99)
943,413 (66)
339,055,724 (1)
2,800,972 (46)
1,812,251 (58)
18,415,618 (11)
307,960,343 (2)
11,593,356 (19)
15,352,295 (15)
7,122,144 (28)
4,933,333 (33)
3,077,761 (42)
825,979 (70)
Total ANEW
85,269,499 (5)
32,442,529 (8)
14,640,728 (20)
3,162,330 (48)
2,468,496 (51)
1,783,270 (56)
10,645,630 (25)
17,867,768 (16)
12,090,597 (23)
7,087,972 (31)
464,914 (75)
10,629,361 (26)
4,602,627 (44)
27,770,768 (10)
22,952,366 (13)
1,485,702 (59)
6,787,678 (35)
418,161 (77)
7,541,106 (29)
5,011,785 (40)
1,102,512 (67)
17,606,796 (17)
178,676 (86)
1,673,992 (58)
19,886,691 (14)
1,204,585 (65)
145,464,084 (2)
14,437,899 (21)
1,475,221 (60)
4,950,202 (41)
8,899,406 (28)
1,385,072 (62)
2,554,459 (50)
34,513,587 (7)
23,619,518 (12)
29,558,118 (9)
5,603,347 (39)
14,697 (99)
516,171 (73)
142,219,359 (3)
2,399,867 (52)
3,452,171 (45)
16,191,802 (18)
282,865,043 (1)
7,499,840 (30)
14,398,889 (22)
6,286,163 (37)
9,670,512 (27)
2,852,623 (49)
571,442 (71)
Complex
Sociotechnical
Systems
Twitter—location:
(amb)
Total Tweets Total ANEW
Word
havg
51. me
-0.06 144,342,098 (4) 88,088,051 (4)
52. ?
-0.07 2,333,283 (53)
674,679 (69)
53. commute
-0.09
90,126 (94)
90,092 (92)
54. gay
-0.09 2,727,309 (47) 1,697,177 (57)
55. right
-0.10 19,166,480 (10) 15,850,283 (19)
56. school
-0.11 9,264,217 (24) 6,924,193 (34)
-0.13
229,773 (86)
188,338 (85)
57. Republican
58. they
-0.16 27,442,360 (8) 27,150,189 (11)
59. winter
-0.19 1,255,945 (64) 1,217,225 (64)
60. lose
-0.19 2,056,468 (55) 2,091,540 (53)
61. Jon Stewart
-0.20
52,084 (97)
33,086 (96)
62. gas
-0.22 1,022,879 (65)
812,029 (68)
63. no
-0.22 95,129,093 (5) 38,894,616 (6)
64. Democrat
-0.23
93,193 (93)
75,450 (93)
65. left
-0.27 4,893,634 (34) 4,611,878 (43)
66. Senate
-0.29
447,732 (78)
316,835 (80)
67. election
-0.30
560,184 (75)
375,055 (78)
68. Sarah Palin
-0.34
225,577 (87)
150,096 (88)
69. Obama
-0.35 2,981,150 (44) 1,998,326 (54)
70. economy
-0.36
608,878 (73)
460,834 (76)
-0.36
391,510 (79)
279,695 (81)
71. Congress
72. drugs
-0.39
509,606 (77)
469,091 (74)
73. Muslim
-0.42
215,300 (88)
146,506 (89)
74. George Bush
-0.43
32,341 (98)
23,102 (98)
75. climate
-0.44
364,177 (80)
229,129 (83)
76. Pope
-0.51
152,320 (91)
135,955 (90)
77. oil
-0.53 1,377,355 (62) 1,148,990 (66)
78. I feel
-0.54 5,173,513 (31) 4,702,352 (42)
79. Glenn Beck
-0.54
113,991 (92)
101,090 (91)
80. Islam
-0.54
187,223 (89)
70,311 (94)
81. :-(
-0.65
341,141 (81)
244,215 (82)
82. :(
-0.70 2,907,145 (45) 1,891,225 (55)
83. flu
-0.75
901,403 (68)
639,000 (70)
84. rain
-0.78 3,233,464 (41) 5,959,903 (38)
-0.78
582,167 (74)
326,100 (79)
85. BP
86. mosque
-0.79
69,812 (95)
46,736 (95)
87. dark
-0.95 1,577,553 (61) 3,233,911 (47)
88. Lehman Brothers -1.08
8,500 (100)
4,280 (100)
89. Goldman Sachs
-1.08
52,703 (96)
30,769 (97)
90. Afghanistan
-1.15
273,519 (83)
172,637 (87)
91. Iraq
-1.37
238,931 (85)
213,425 (84)
92. cold
-1.39 3,670,447 (36) 7,015,518 (32)
93. gun
-1.81
680,903 (72) 1,263,217 (63)
94. hate
-2.43 9,652,881 (23) 18,158,870 (15)
95. hell
-2.49 6,266,162 (30) 11,056,735 (24)
96. sick
-2.55 3,576,058 (37) 6,783,395 (36)
97. sad
-2.56 3,563,745 (38) 6,951,686 (33)
98. war
-2.63 1,955,901 (57) 3,417,588 (46)
99. depressed
-2.64
280,872 (82)
541,394 (72)
100. headache
-2.83
856,600 (69) 1,446,064 (61)
Measuring
Happiness
Some motivation
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
65 of 83
(amb)
invite the reader to explore the tables beyond the observations we detail here. We begin with the highest
(amb)
and lowest rankings of ambient happiness havg , for
our list, finding them to be reassuring sensible. The
three top ranked words are ‘love’ (havg =+1.42), ‘happy’ (havg =+1.32), and ‘win’ (havg =+1.22), and the
last five, in reverse order, are ‘headache’ (havg =−2.55),
‘depressed’ (havg =−2.56), ‘war’ (havg =−2.63), ‘sad’
Text element and context correlate in happiness
scores:
I
I
I
I
Compare ambient happiness with text element
happiness.
Spearman correlation coefficient:
rs ' 0.79, p-value < 10−10 .
Complex
Sociotechnical
Systems
Complex
Sociotechnical
Systems
Twitter—location:
Tref : NY (havg =6.32)
Tcomp: CA (havg =6.38)
Measuring
Happiness
mad −↓
1
Some motivation
fun +↑
beach +↑
home +↑
hell −↓
dead −↓
free +↑
hate −↓
Measuring emotional
content
Data sets
5
Analysis
Songs
Blogs
Positivity Bias
References
An on-average result: says nothing about any
individual sentence.
Data sets
Songs
Blogs
Tweets
10
+↓snow
−↑time
cold −↓
10
Text size:
Tref Tcomp
Positivity Bias
References
−↑rain
fall −↓
15
+↓music
+↓sex
1
10
Extra random piece: stemming fails.
20
bus −↓
cut −↓
2
10
−↑bomb
−↑ambulance
10
25
0
P
r
i=1
−20
100
δhavg,i
−10
Balance:
−79 : +179
+↓
+↑
square −↓
hit −↓
accident −↓
−↑
3
63 of 83
Some motivation
Analysis
car +↑
0
Measuring
Happiness
Measuring emotional
content
+↓wit
Tweets
Word rank r
TABLE III: A selection of 100 keywords and text elements ordered by average ambient happiness havg . We define ambient
happiness of a keyword as the average happiness of tweets containing that keyword, relative to the overall happiness of tweets,
havg � 6.37. The number of tweets and total number of ANEW study words are listed in the third and fourth columns, with
the ranking of the keyword according to these quantities shown in brackets. Note that all pattern matches with tweets were
case-insensitive.
−↑fire
0
10
−↓
20
Per word average happiness shift δhavg,r (%)
66 of 83
Twitter—popularity based on follower count:
09/09/08 − 06/30/09
9
# Words
# ANEW
8
10
7
10
6
10
Measuring
Happiness
8
10
6
5
10
0
10
1
10
2
3
10
4
10
10
5
10
Complex
Sociotechnical
Systems
Twitter—interactions:
09/09/08 − 06/30/09
10
10
10
Complex
Sociotechnical
Systems
0
10
10
1
10
2
10
3
10
4
10
5
10
Measuring
Happiness
Some motivation
Some motivation
Measuring emotional
content
Measuring emotional
content
Data sets
Data sets
Analysis
Analysis
Songs
Songs
Blogs
Ambient Diversity (N(amb)
)
S
Happiness (havg)
6.5
6.45
6.4
6.35
6.3
6.25
0
10
1
10
2
3
10
4
10
Positivity Bias
Positivity Bias
40
References
References
20
0
−20
−40
−60
Background: 336.96
−80
5
10
10
Tweets
60
0
10
1
10
Follower Count
I
Blogs
Tweets
6.55
2
10
3
10
4
10
5
10
Follower Count
I
Decay in happiness correlation in social network.
I
ρ = Spearman’s correlation coefficient.
Dunbar’s number ' 150.
67 of 83
70 of 83
free +↑
1
0.15
+↓home
hate −↓
sick −↓
bored −↓
good +↑
5
Complex
Sociotechnical
Systems
Positive bias in the English language:
T ref: ≤ 10 2 followers (h avg=6.29)
T c o m p: ≥ 10 3 followers (h avg=6.44)
Measuring
Happiness
Some motivation
0.125
Measuring emotional
content
Data sets
people +↑
hell −↓
stupid −↓
love +↑
cold −↓ Text size:
10
0
Tweets
1
Positivity Bias
References
0.05
+↓god
money +↑ Ba la nce:
20
0.075
social +↑
success +↑
sad −↓
10
Songs
Blogs
T r e f T c om p
+↓sleep
10
15
Analysis
0.1
N
Word rank r
+↓bed
+↓christmas
2
0.025
−93 : +193
+↑
10
headache +↓
−↓
−↑news
lost −↓
hungry −↓
−↑
war −↓
3
10
25
0
P
r
100
i =1 δ h av g, i
−20
0
1
−↓
2
3
4
5
−10
0
10
6
7
8
9
havg
−↑failure
20
Per word average happi ness shi ft δ h av g, r (%)
71 of 83
6000
6.1
4000
6
2000
5.9
1
45
90
135
180
225
day number
270
315
0
0.04
1
3
5 7
havg
100
75
50
25
0
9
positive
0.08
negative
0
percentile
0.12
percentage
A. Twitter
100
75
50
25
0
1 2 3
|havg − 5|
4
0.04
Complex
Sociotechnical
Systems
B. Books
1
3
5 7
havg
Measuring
Happiness
9
Some motivation
positive
Measuring emotional
content
negative
0
1 2 3
|havg − 5|
Data sets
4
Analysis
Songs
Blogs
Tweets
0
1
0.16
0.12
0.08
0.04
0
1
2
100
75
50
25
0
100
75
50
25
0
3
4
5
6
7
8
9
0
1
0.16
C. New York Times
0.12
1
3
5 7
havg
9
positive
0.08
negative
0
1 2 3
|havg − 5|
2
3
percentile
6.2
100
75
50
25
0
0.16
4
4
percentage
8000
6.3
percentile
10000
6.4
0.08
percentage
12000
percentile
6.5
0.12
100
75
50
25
0
percentage
14000
Normalized frequency P
6.6
average follower number
average valence v
0.16
0.04
5
6
7
8
9
0
1
2
100
75
50
25
0
100
75
50
25
0
3
4
5
6
7
8
9
Positivity Bias
References
D. Music Lyrics
1−1000
1
3
5 7
havg
1001−2000
9
2001−3000
3001−4000
positive
4001−5000
negative
0
1 2 3
|havg − 5|
2
3
4
4
5
6
7
8
9
Average happiness havg
72 of 83
Complex
Sociotechnical
Systems
New York Times
Complex
Sociotechnical
Systems
1
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2
3
4
5
6
7
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Measuring
Happiness
Some motivation
B. Books
1000
1000
2000
2000
3000
3000
Measuring
Happiness
Some motivation
Measuring emotional
content
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
Word Usage Frequency Rank r
1
1
4000
Songs
4000
Blogs
Tweets
5000
0
1
2
3
5000
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References
1
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Times
1000
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1000
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3
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73 of 83
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Complex
Sociotechnical
Systems
1000
2000
2000
3000
3000
Measuring
Happiness
Some motivation
Measuring emotional
content
4000
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4000
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Tweets
3
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(in development)
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2
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4
5
6
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Positivity Bias
I
References
1
Fifteen additional languages being scored on
Mechanical Turk
Some motivation
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
1
I
How does happiness vary with proximity to nature?
to Walmart?
2000
I
Emotional contagion.
3000
3000
I
Quantifying metaphor and narrative and stories . . .
4000
4000
C. New York
Times
1000
1000
2000
5000
1
2
3
4
5
6
7
8
9
5000
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Lyrics
2
3
4
5
6
7
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74 of 83
Average happiness havg
Complex
Sociotechnical
Systems
Books
1
500
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Complex
Sociotechnical
Systems
Measuring
Happiness
I
Data sets
Analysis
2
Random other things (now and next):
B. Books
1000
5000
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76 of 83
1000
1500
2000
2500
3000
3500
4000
4500
5000
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Measuring
Happiness
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References I
Complex
Sociotechnical
Systems
h
his wit
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i
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e
nc ugh
d
r
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thi xpe eno nea lor
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to
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t min li
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n
e
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e ad vy
rn
b
er
sitlee ow reem hea outhe cem
g
s
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ht arc
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ig
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s
w
co stor fis poe
se
ium l
ly
ed a
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s
here’s
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es
k
ow nan ump
d
c
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te
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77 of 83
1
1.5
2
2.5
[1]
Some motivation
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
M. Bradley and P. Lang.
Affective norms for english words (anew): Stimuli,
instruction manual and affective ratings.
Technical report c-1, University of Florida,
Gainesville, FL, 1999. pdf ()
Measuring
Happiness
T. Conner Christensen, L. Feldman Barrett,
E. Bliss-Moreau, K. Lebo, and C. Kaschub.
A practical guide to experience-sampling
procedures.
Journal of Happiness Studies, 4:53–78, 2003.
Positivity Bias
Tweets
Some motivation
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
Tweets
Positivity Bias
[2]
References
[3]
References
M. Csikszentmihalyi.
Flow.
Harper & Row, New York, 1990.
3
Standard deviation of happiness hσ
75 of 83
78 of 83
References II
[4]
[5]
[6]
M. Csikszentmihalyi, R. Larson, and S. Prescott.
The ecology of adolescent activity and experience.
Journal of Youth and Adolescence, 6:281–294,
1977.
E. Diener and M. Y. Chan.
Happy people live longer: Subjective well-being
contributes to health and longevity.
Applied Psychhology: Health and Well-Being,
3:1–43, 2011. pdf ()
Complex
Sociotechnical
Systems
Measuring
Happiness
Some motivation
Measuring emotional
content
References V
[13] S. Lyubomirsky.
The How of Happiness.
The Penguin Press, New York, 2007.
Data sets
Complex
Sociotechnical
Systems
Measuring
Happiness
Some motivation
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
[14] C. Martinelli and S. W. Parker.
Deception and misreporting in a social program.
forthcoming in Journal of the European Economic
Association, 2007. pdf ()
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
[15] G. Miller.
Social Scientists wade into the Tweet stream.
Science Magazine, 333:1814–1815, 2011. pdf ()
P. S. Dodds and C. M. Danforth.
Measuring the happiness of large-scale written
expression: Songs, blogs, and presidents.
Journal of Happiness Studies, 2009.
doi:10.1007/s10902-009-9150-9. pdf ()
[16] G. Mishne and M. de Rijke.
Capturing global mood levels using blog posts.
AAAI 2006 Spring Symposium on Computational
Approaches to Analysing Weblogs, 2005.
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References III
[7]
P. S. Dodds, K. D. Harris, I. M. Kloumann, C. A.
Bliss, and C. M. Danforth.
Temporal patterns of happiness and information in a
global social network: Hedonometrics and Twitter.
PLoS ONE, 6:e26752, 2011.
Draft version available at
http://arxiv.org/abs/1101.5120v4.
Accessed October 24, 2011. pdf ()
Complex
Sociotechnical
Systems
W. T. Jones.
The Classical Mind.
Harcourt, Brace, Jovanovich, New York, 1970.
[9]
D. Kahneman, A. B. Krueger, D. A. Schkade,
N. Schwarz, and A. A. Stone.
A survey method for characterizing daily life
experience: The day reconstruction method.
Science, 306(5702):1776–1780, 2004. pdf ()
[10] M. A. Killingsworth and D. T. Gilbert.
A wondering mind is an unhappy mind.
Science Magazine, 330:932, 2010. pdf ()
[11] I. M. Kloumann, C. M. Danforth, K. D. Harris, C. A.
Bliss, and P. S. Dodds.
Positivity of the English language.
PLoS ONE, 7:e29484, 2012.
Draft version available at
http://arxiv.org/abs/1108.5192. Accessed
October 24, 2011. pdf ()
Complex
Sociotechnical
Systems
Measuring
Happiness
Some motivation
Some motivation
Measuring emotional
content
Measuring emotional
content
Data sets
Data sets
Analysis
Analysis
Songs
Songs
Blogs
Tweets
Positivity Bias
80 of 83
References IV
References VI
Measuring
Happiness
References
[8]
82 of 83
Complex
Sociotechnical
Systems
Measuring
Happiness
Some motivation
Measuring emotional
content
Data sets
Analysis
Songs
Blogs
Tweets
Positivity Bias
References
[12] R. Layard.
Happiness.
The Penguin Press, London, 2005.
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[17] E. Sandhaus.
The New York Times Annotated Corpus.
Linguistic Data Consortium, Philadelphia, 2008.
Blogs
Tweets
Positivity Bias
References
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