Tag Archives: hedonometrics

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.)

happinessScatterMatrix1

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…

Now online: the Dow Jones Index of Happiness

Total excitement people: our website hedonometer.org has gone live.  We’re measuring Twitter’s happiness in real time.  Please check it out!

If you’re still here, here’s the blurb from the site’s about page:

Happiness: It’s what most people say they want. So how do we know how happy people are? You can’t improve or understand what you can’t measure. In a blow to happiness, we’re very good at measuring economic indices and this means we tend to focus on them. With hedonometer.org we’ve created an instrument that measures the happiness of large populations in real time.

Our hedonometer is based on people’s online expressions, capitalizing on data-rich social media, and we’re measuring how people present themselves to the outside world. For our first version of hedonometer.org, we’re using Twitter as a source but in principle we can expand to any data source in any language. We’ll also be adding an API soon.

So this is just a start – we invite you to explore the Twitter time series, let us know what you think, and follow the daily updates through the hedonometer twitter feed: .

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):

Image

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.

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:

Image

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.

wordshiftNapaRedacted

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.

cityClusters

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.

The Daily Unraveling of the Human Mind

Each morning we find ourselves in wide flung arms of drowsy possibilites. Cradled by the warm embrace of our beds, we begin our day, rebooted and rejuvenated. Having not eaten for a full eight hours, we can enjoy a guilt free breakfast, setting a blissful tone for the day.

figtwitter_daily_anewfreq_spesh_thetruth001_noname

Hourly frequency of meal references on twitter.
See figure 1 page 3 of our paper for details.

Last night’s dreams of victory and triumph bolster our delusions of adequacy, preparing us to surmount any of life’s challenges. But the moment we step outside, reality commences its slow and insidious descent. Its weight, compressing our spine, crushing our dreams, alters the course of the day completely.  The soul crushing litany of work, interacting with people, and generally living our lives takes its toll. As our sanity unravels, apathy takes root. The profane becomes our standard of expression. In the throes of despair, we swear just to feel something. We swear increasingly as we realize the inevitability of repeating this all again tomorrow.

F***, that’s a terrifying thought.

This ephemeral pattern is reflected in our tweets, our spontaneous burst of being. Below, we see our happiness peaks during the early hours of the day, and degrades as the hours progress (yellow circles). The proportion of profanity in our tweets, however, follows a reverse cycle. Profanity appears in a smaller percent of tweets at the start of each day, and increases gradually as time wears on.

Daily Unraveling

Daily Unraveling
See figure 10 page 15 of our paper for details.

Remarkably, the relative frequency of these five expressions of frustration (a******,  b****, s***, f***, m***********) are quite similar.

Well done, humans.

To avoid experiencing the daily unraveling, we recommend eating organic, local dark chocolate all day long.

Hedonometrics

Our paper “Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter” appears in PLoS ONE this week. Their blog encourages you to tweet for the sake of science!

Among other findings, in this paper we demonstrate that human ratings of the happiness of an individual word correlate very strongly with the average happiness of the words that co-occur with it. This implies that tweets containing particular keywords can be used as an unsolicited public opinion poll.

For example, tweets containing “Tiger Woods” became decidedly less positive after his Thanksgiving disaster in 2009 as the words ‘accident’, ‘crash’, ‘scandal’, and ‘cheating’ are more abundant, while the word ‘love’ appears less often.

Happiness is measured relative to the ambient background of all tweets.

Sad words are blue, happy words are yellow. Up (down) arrow indicates that the word appeared more (less) frequently in tweets containing "Tiger Woods".

Generally, tweets containing personal pronouns tell a positive prosocial story with ‘our’ and ‘you’ outranking ‘I’ and ‘me’ in happiness. The least happy pronoun on our list is the easily demonized ‘they’.

Emoticons in increasing order of happiness are ‘:(’, ‘:-(’, ‘;-)’, ‘;)’, ‘:-)’, and ‘:)’. In terms of increasing information content (diversity of words co-occuring with each emoticon), the order is ‘:(’, ‘:-(’, ‘:)’, ‘:-)’, ‘;)’, and ‘;-)’. We see that happy emoticons co-occur with words of higher levels of both happiness and information but the ordering changes in a way that appears to reflect a richness associated with cheekiness and mischief: the two emoticons involving semi-colon winks are third and fourth in terms of happiness but first and second for information.

A list of the happiness ratings of tweets containing some interesting keywords can be seen here.

And not surprisingly, the happiness of all tweets appearing on a given day of the week correlates well with the happiness ratings humans give each day.

Happiness of tweets appearing on a given day

Human ratings of the happiness of each day of the week

You can download the language assessment by Mechanical Turk (labMT 1.0) word list here. It is a text file containing the set of 10,222 most frequently occurring words in the New York Times, Google Books, music lyrics, and tweets, as well as their average happiness evaluations according to users on Mechanical Turk.  See the paper for details.

Much more to come regarding sociotechnical phenomena…

Tweets and happiness.

Quotidian Twitter verbiage

Relative use of food-based keywords in tweets over the course of a day.

Below is our first treatment of oodles of Twitter data, searching for basic patterns, happiness, and information levels. On the left, we have strong evidence that people really do tweet about what’s going on in their lives right now, at least food-wise.

The paper: Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter

http://arxiv.org/abs/1101.5120

Peter Sheridan Dodds, Kameron Decker Harris, Isabel M. Kloumann, Catherine A. Bliss, Christopher M. Danforth

Abstract:

Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often been indirectly characterized and overshadowed by more readily quantifiable economic indicators, such as gross domestic product. Here, we use a real-time, remote-sensing, non-invasive, text-based approach—a kind of hedonometer—to uncover collective dynamical patterns of happiness levels expressed by over 50 million users in the online, global social network Twitter. With a data set comprising nearly 2.8 billion expressions involving more than 28 billion words, we explore temporal variations in happiness, as well as information levels, over time scales of hours, days, and months. Among many observations, we find a steady global happiness level, evidence of universal weekly and daily patterns of happiness and information, and that happiness and information levels are generally uncorrelated. We also extract and analyse a collection of happiness and information trends based on keywords, showing them to be both sensible and informative, and in effect generating opinion polls without asking questions. Finally, we develop and employ a graphical method that reveals how individual words contribute to changes in average happiness between any two texts.