# Hedonometer 2.0

Geography of Happiness for the US

Over the summer of 2014, we have worked very hard to bring many new pieces to our Hedonometer, and we’re pleased to tell you about what we’ve done, and where we’re going next.

Snapshot of Hedonometer 2.0′s happiness time series.

All along, one of the central goals for the Hedonometer has been to provide a new instrument for society’s dashboard, one that measures population-level happiness in real time from any streaming text source. Like flying a plane, where we would never want just one dial with the limits “all good” and “uh-oh”, we need a sophisticated dashboard to quantify how well a population is faring. We want to see unconventional measures like ours added to traditional, easier-to-gauge quantities often concerned with economic activity. Money doesn’t equal happiness. We hope the Hedonometer will enable individuals, journalists, policy makers, corporations, and other research teams in their various pursuits.

Because our Hedonometer works for any large text, we’re able to explore other areas for basic science purposes, particularly the vast realm of sociotechnical systems and the digital humanities. And some of our work will be simply just for fun (hopefully yours and ours).

Harry Potter and the Prisoner of Azkaban.

As you’ll see below, we have many plans for the future. So far, we’ve received crucial support from the NSF and the MITRE Corporation, and we’re always looking for more ways to continue to lift our enterprise. If you’re interested in or have suggestions about funding our work, please contact us.

Okay—here’s what we’ve put together. We now have four main interactive views of emotion up and running:

1. A completely rebuilt global Twitter happiness time series in English, updated daily and with powerful new word shifts;
2. An interactive map of happiness for the 50 US States plus DC, also based on Twitter;
3. A ranked list of cities by happiness for the US (Twitter again);
4. and an explorable visualization of the emotional plot trajectories of 10,000 books in 10 major languages including Harry Potter along with classic and obscure works.

We’ll go into more depth about how to use and share these visualizations in our following blog posts. As always, Hedonometer stands on a team effort but we have to acknowledge and praise Andy Reagan (@andyreagan) for his incredible efforts in leading the charge to Hedonometer 2.0. Building things is fun.

Some of the many new elements we’re looking to add in the next year are:

1. Other kinds of real-time, population-scale meters based on word usage including sleep, food consumption, exercise, binge-drinking, and boredom. We’ll apply these meters to Twitter but they could in principle be used on any text.
2. Global Twitter happiness time series and maps for all 10 languages: English, Spanish, French, German, Brazilian Portuguese, Indonesian, Korean, Simplified Chinese, Arabic, and Russian;
3. Real-time Twitter happiness at 1 minute time scales.
4. An interactive world map with the ability to explore at scales of country, state, city, and district (or equivalents).
5. Simple ways to embed our interactive visualizations into webpages;
6. A simple interface for uploading and comparing two texts, and for generating shareable visualizations;
7. Phrase-based rather than word-based analysis in English;
8. More stand-alone projects such as interactive visualizations of music lyrics over the last 60 years;
9. Measures based on other major emotions such as fear, disgust, anger, and surprise.

We also have two longer-term, major projects in development:

1. A fast search facility in the Hedonometer for users to find the emotional spectrum around specific words or phrases. This is a computationally bundensome problem. We’ll be able to show how the emotional texture of how people are talking about an event or a product.
2. Storybreaker: a real-time extractor of stories and narratives emerging around major events. Our algorithm will include emotion but our goal is to measure frames around issues and ultimately meaningful stories.

One last thing: we’ve moved our blog from onehappybird to compstorylab.org. All old links will still work.

# How does movement influence your daily happiness?

Imagine commuting an hour to work, one way, grinding through miles of traffic to get from your suburban home to a desk job in the big city. Excited yet?

Ok, now imagine that you lead a life of leisure traveling the world. You fly coast-to-coast to see a concert, soak in some culture, and drink fine wine. Does this lifestyle seem more appealing?

Lets try to quantify the influence of these travel patterns on individual happiness. We do this using geolocated tweets, which we have previously used to reveal the happiness of cities, and to quantify patterns of movement.

Each point corresponds to a geo-located tweet from 2011.
(A) USA (B) Washington, D.C. (C) Los Angeles (D) Earth

First, we find the average location of each individual’s tweets. We call this their expected location. Then we draw circles emanating from this spot, like rings on a dart board. Some messages are written close to home, others from very far away.

Then we collect all of the words written at each distance, roughly 500,000 tweets per ring. Averaging the happiness of words found at each distance, remarkably we find that happiness increases logarithmically with distance from expected location. Tweets authored far from home contain a smaller number of negative words.

Tweets are grouped into ten equally populated bins by the distance from their author’s average location, and the average happiness of words written at each distance is plotted. Expressed happiness grows logarithmically with distance from home.

Home is where the hate is? What? No.

Below we look at the difference between the happiest and saddest distances from home. Words appearing on the right increase the happiness of the 2500km distance relative to the 1km distance. For example, tweets authored far from an individual’s expected location are more likely to contain the positive words beach’, new’, great’, park’, restaurant’, dinner’, resort’, coffee’, lunch’, cafe’, and food’, and less likely to contain the negative words no’, don’t’, not’, hate’, can’t’, damn’, and never’ than tweets posted close to home. Words going against the trend appear on the left, decreasing the happiness of the 2500km distance group relative to the 1km group.

Word shift graph comparing the lowest average word happiness distance group to the words authored farthest from home.

Tweets written close to home are more likely to contain the positive words me’, lol’, love’, like’, haha’, my’, you’, and good’. Moving clockwise, the three insets show that the two text sizes are comparable, the biggest contributor to the happiness difference is the decrease in negative words authored by individuals very far from their average location, and the 50 words listed make up roughly 50% of the total difference between the two bags of words. For you visual learning folks, here is a short video explaining how these word shifts work.

Take home story: people tweeting far from home talk about food more, and they swear less than people tweeting close to home. These people are probably enjoying awesome vacations, and tweeting about it!

In summary, if you are a fellow with a daily commute that makes you feel a little bit sad, you are not alone! Try swearing less. Or ride your bike.

If you are lucky enough to travel often, then keep smiling…maybe send the rest of us some pictures to cheer us up!

For more details on our analysis, check our paper “Happiness and the Patterns of Life: A Study of Geolocated Tweets” recently published in Nature Scientific Reports.

# Now Published: The Geography of Happiness

Today we’re pleased to announce that our article “The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place” has been officially published by PLoS ONE.  We wanted to tell you about one key piece we’ve added to the paper and an unusual new Twitter account we’ve created.

After our three blog posts (which coincided with the release of the preprint), we received plenty of media attention, as well as some fantastic feedback from readers (thanks!). One very important question kept coming up: “How well does happiness agree with other measures of well-being?”, or more simply: “Why should we believe you?”

Well, we’re glad you asked.  For the final paper, we’ve added a US state-level comparison between our happiness measure and five other kinds of well-being indices:

• the Behavioral Risk Factor Surveillance Survey (BRFSS)  for which people were asked to rate their life satisfaction on a scale of 1 to 4 (the BRFSS was explored in this Science paper on well-being from a few years back);
• Gallup’s health survey-based well-being index;
• the Peace Index, which aggregates various crime data;
• the America’s Health Ranking, which aggregates health data; and
• gun violence, specifically the number of shootings per 100,000 people.

In the figure below, we show a series of scatter plots comparing all pairs of well-being metrics  (happiness runs along the top row).  Each dot represents a US state, and the colors represent strength of correlation or agreement between measures, with blue meaning strong agreement, and red representing no (statistically significant) agreement. (We include the exact Spearman correlation coefficienr and p-value in each scatter plot.)

Scatter matrix showing comparison between different well-being metrics for all US states. The top row shows comparisons with happiness. Colors indicate the strength of correlation between pairs of metrics; shades of blue indicate increasingly significant correlation.

Looking at the top row, we can immediately see that happiness agrees with all measures except for the BRFSS. However, the BRFSS itself doesn’t agree with any other measure except for the Gallup well-being index.  The most striking departure was the BRFSS ranking Louisiana as the happiest state whereas our happiness measure placed it last.  There are a number of possible explanations for these disagreements: one is that the BRFSS data was taken between 2005 and 2008, while all other data is from 2011 only; another is that unlike the other measures, happiness is self-reported in the BRFSS. How would you answer if asked how happy you are? Do you expect that your answer is representative of the population you live in at large? There are certainly many different ways to define “happiness”, as a number of different readers have pointed out.

Of course, this is not to criticize the BRFSS (it remains a significant data source, and Oswald & Wu did fine work analyzing it in their Science paper), but merely to suggest that our word happiness score is measuring something different but perhaps complementary to traditional survey-based techniques. There certainly appears to be plenty of value to observing people “in the wild” via social network data, e.g. with the real-time instrument hedonometer.org.

Finally, to celebrate the publication of our article we created a Twitter feed, @geographyofhapp, dedicated to tweeting the happiest and saddest city every day, and we invite you to follow.  We’re hoping that this is the first research article with its own Twitter account, but perhaps not hoping that it represents the future of scientific publishing…

# A data-driven study of the patterns of life for 180,000 people

Here at the Computational Story Lab, some of us commute by foot, some by car, and a few deliver themselves by bike, even in the middle of our cold, snowful Vermont winter.  Occasionally, we transport ourselves over very long distances in magic flying tubes with wings to attend conferences, to see family, or for travel.  So what do our movement patterns look like over time?  Are there distinct kinds of movement patterns as we look across populations, or are they variations on a single theme?

Inspired by an analysis of mobile phone data by Marta Gonzalez at MIT, James Bagrow at Northwestern, and colleagues, we used 37 million geotagged tweets to characterize the movement patterns of 180,000 people during their 2011 travels. We used the standard deviation in their position, a.k.a. radius of gyration, as a reflection of their movement. As an example, below we plot a dot for each geotagged tweet we found posted in the San Francisco Bay area, colored by the author’s radius of gyration.

The Bay Area is shown with a dot for each tweet, colored by the radius of gyration of its author. The color scale is logarithmic, so we can compare people with very different habits.

You can see from the picture that there are many people with a radius near 100km tweeting from downtown San Francisco. This pattern could reflect a concentration of tourists visiting the area, or individuals who live downtown and travel for work or pleasure. Images for New York City, Chicago, and Los Angeles are also quite beautiful.

In the image below, we rotated every individual’s movement pattern so that the origin represents their average location, and the horizontal line heading to the left represents their principle axis (most likely the path from home to work). We also stretched or shrunk the vertical and horizontal axes for each individual, so that everyone could fit on the same picture. Basically, we have a heatmap of collective movement, with each individual in their own intrinsic reference frame.  The immediate good news for these kinds of data-driven studies is that we see a very similar form to those found for mobile phone data sets.  Apart from being a different social signal, Geotagged Tweets also have much better spatial resolution than mobile phone calls which are referenced by the nearest cellphone tower.

Movement pattern exhibited by 180,000 individuals in 2011, as inferred from 37 million geolocated tweets. Colormap shows the probability density in log10. Note that despite the resemblance, this image is neither a nested rainbow horseshoe crab, nor the Mandelbrot set.

Several features of the map reveal interesting patterns. First, the teardrop shape of the contours demonstrates that people travel predominantly along their principle axis, with deviations becoming shorter and less frequent as they move farther away. Second, the appearance of two spatially distinct yellow regions suggests that people spend the vast majority of their time near two locations. We refer to these locations as the work and home locales, where the home locale is centered on the dark red region right of the origin, and the work locale is centered just left of the origin.

Finally, we see a clear horizontal asymmetry indicating the increasingly isotropic variation in movement surrounding the home locale, as compared to the work locale. We suspect this to be a reflection of the tendency to be more familiar with the surroundings of one’s home, and to explore these surroundings in a more social context. The up-down symmetry demonstrates the remarkable consistency of the movement patterns revealed by the data.

We see a clear separation between the most likely and second most likely position.

Looking just at the messages posted along the work-home corridor, the distribution is skewed left, with movement from home in a heading opposite work seen to be highly unlikely.

The isotropy ratio shows the change in the probability density’s shape as a function of radius.

Above we see that individuals who move around a lot have a much larger variation in their positions along their principle axis, exhibiting a less circular pattern of life than people who stay close to home. Remarkably, the isotropy ratio decays logarithmically with radius.

Finally, we grabbed messages from the most prolific tweople, those 300 champions who had posted more than 10,000 geotagged messages in 2011. We received 10% of these messages through our gardenhose feed from Twitter. Below, we plot the times during the week that they post from their most frequently visited location. These folks most likely have the geotag switch on for all messages, and exhibit a very regular routine.

A robust diurnal cycle is observed in the hourly time of day at which statuses are updated, with those from the mode location (black curve) occurring more often than other locations (red curve) in the morning and evening.

Peaks in activity are seen in the morning (8-10am) and evening (10pm-midnight), separated by lulls in the afternoon (2-4pm) and overnight (2-4am) hours.  As we and our friend Captain Obvious would expect, people tend to tweet more from their home locale than any other locale (red curve) in the morning and evening.

Bottom line: Despite our seemingly different patterns of life, we are remarkably similar in the way we move around. Our walks are a far cry from random.

Next up: We’ll examine the emotional content of tweets as a function of distance.  Is home where the heart is?

For more details on these results, see our paper Happiness and the Patterns of Life: A Study of Geolocated Tweets.

# Where is the happiest city in the USA?

(Update: this work is now published at PLoS ONE)

Is Disneyland really the happiest place on Earth?* How happy is the city you live in? We have already seen how the hedonometer can be used to find the happiest street corner in New York City, now it’s time to let it loose on the entire United States.

We plotted over 10 million geotagged tweets from 2011 (all our results are in this paper, also on the arxiv), coloring each point by the average happiness of nearby words (detail on how we calculate happiness can be found in this article published in PLoS ONE):

As well as cities and the roads between them, we can make out many regions of higher and lower happiness, even within individual cities. As an example, check out this tweet-generated map of the city of Chicago:

Tweet-generated map of Chicago. Click to enlarge.

Notice the striking contrast between the relatively happy Central/North Side of the city, and the sadder South Side. You can also find a few airports in this map, and if you look very closely you might even be able to pick out happy and sad terminals!

To quantify this variation in happiness a bit better, let’s look at the average happiness of each state:

Southern states tend to produce sadder words than those in northern New England or out west. Hawaii emerges as the happiest state and Louisiana as the saddest, due to relative differences in the frequencies of happy and sad words used in each state. Here at onehappybird, we characterize such differences by “word shifts”, which are basically word clouds for grown-ups. You can find examples of these, as well as the full list of the average happiness of each state, here (page best viewed using Google Chrome).

Zooming in further to the level of cities, we produced a similar list for 373 cities in the lower 48 states (you can find the full list, as well as maps and word shifts for each city, here). With a score of 6.25, we found the happiest city to be Napa, CA, due to a relative abundance of such happy words as “restaurant”, “wine”, and even “cheers”, along with a lack of profanity.

At the other end of the spectrum, we found the saddest city to be Beaumont, TX, with a score of 5.82. In general, cities in the south tended to be less happy than those in the north, with a major contributing factor being the relative abundance of profanity used in those cities.

We can go even further than this, and group cities by similarities in word usage. Each square in the heatmap below represents the similarity (Spearman correlation for you mathematically minded onehappybird watchers) between word distributions for the largest cities in the US. Red squares mean that the corresponding cities use words in a similar fashion, while blue means that those cities tend to use different types of words with respect to each other. The colors in the tree diagram at the top signify clusters of cities exhibiting similar word usage (below a certain threshold).

As we might expect for two cities that are geographically nearby, New Orleans and Baton Rouge are clumped together at the bottom right of the figure. On the other hand, New York and Seattle get clumped together as well, suggesting that similarities in language depend on more than just geographical proximity.

You can find more information about happiness and cities, as well as details on the methods used to produce these results, in our arxiv research article. In our next post, we’ll look at how these results are related to various underlying socioeconomic characteristics of cities. What makes a city happy or sad? Can we use Big Data to predict future changes in the demographics, health, or happiness of a city? How does happiness relate to the food you eat?

*By the way, to answer the question at the start of this post: According to this analysis Disneyland is not the happiest place on Earth; it isn’t even the happiest place in Southern California! See if you can find it in this tweet-generated map of LA! Or find your city here.

# Question: Where is the happiest place in New York City?

1. Immediately adjacent to any hot dog stand.
2. Madison Square Garden during moments of Linsanity.
3. Tim Tebow’s new apartment building.

No really though, let’s measure some stuff.

Facts: (1) New York City is the most populous city in the US and (2) Manhattan streets are arranged on a rectangular grid. We have already seen how cities, airports, and even streets can be identified using geotagged tweets – here we use more than a half million messages from 2011 to investigate the happiness of NYC streets and avenues (clearly visible in the image below, as is Central Park).

Binning tweets by avenue and street, we use the labMT word list to measure happiness in tweets as a function of avenue and street number:

The results suggest that the west side is slightly happier than the east side, and that happiness actually declines as one moves further uptown. Next we bin by intersection and plot a heat map showing the distribution of happiness over all of the street corners in Manhattan:

The happiest “corner” is actually just inside the western edge of Central Park, where the intersection of 7th and 77th would be (this is just north of the lake and east of the Hayden Planetarium)*. This corner elicits tweets with a relatively high abundance of the positive words “loves” and “sky”, and proportionally less negative words like “not”, “fear” and “no”. Many of the happiest locations actually fall within Central Park!

* Please note that the results reported in this post have not been vetted through panels of experts, statistical tests of significance, or scientific peer review.  They are intended to be a fun and lighthearted exploration of our more formal research interests.

# Tweet Cartography

Six months of geo-located messages from Twitter’s gardenhose feed, roughly 20 million.  World, US, and NYC twitterific projections.