Category Archives: social phenomena

The Ferguson protests: Quantifying state-level sentiment on Twitter

Reporting on the August 9, 2014 shooting death of Michael Brown, David Carr concluded his August 17 piece for the New York Times by observing that “nothing much good was happening in Ferguson until it became a hashtag”. Following the story’s rise and spread on Twitter, the protests in Missouri swiftly captured the news cycle in the U.S., and brought into focus the consequences of militarization and racial inequality in police forces throughout the country.

Using our new and improved instruments at hedonometer.org, we can strongly quantify and visualize the texture of sentiment surrounding the protests on Twitter.  Over the course of a week starting Wednesday, August 13, our all-of-Twitter time series dipped several times, and we saw a large increase in negative words related to events in Ferguson as viewed in the word shift below.  Because we currently break tweets into individual words, “tear” is separated from “tear gas” but is still a negative term that rises to the top (we will move to phrases in a major future update of hedonometer).

Click on the graphic below for an interactive version of the word shift or here.

Click on the graphic for an interactive version.In comparing the week of August 13 to 20 with the last 90 days, we see Missouri’s happiness ranking dropped from 18th to 32nd. The geography of happiness for the U.S. is remarkably stable, and this is the first time we’ve observed such a large, rapid change for a state ranking.

Click on the graphic for an interactive version.

 

Over the 7 days of August 15 to 21, the positive words “lol”, “hahaha”, and “laughing” have been used relatively less frequently in Missouri than in the entire U.S., and the negative words “racist”, “violence”, and “protest” have been used relatively more frequently. Click here or on the image above to explore our sentiment map.

We’re in the process of building interactive sentiment maps for other languages and at scales of cities, countries, and regions.   Our hope is that through hedonometer, anyone will able to make and share geographically localized observations of crowd-sourced public opinion, and generate a defensible quantification of the collective conversation on Twitter and elsewhere.

 

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Moose on the Loose!

Note: a version of this post was given by the author for Invocation at the UVM College of Engineering & Mathematical Sciences graduation ceremony, Flynn Theatre, May 18, 2014.

A few weeks ago—on one of those beautiful spring mornings that makes the long winter seem like it happened elsewhere—something quite remarkable took place here at the University of Vermont.

At the time, I was sitting outside on a small wooden picnic bench near my office on Trinity campus. The sun was shining, the birds were chirping their layered periodic rhythms, and the Green Mountains were finally living up to their name after months of radiating white.

Vermont Mountains Panorama at Sunrise Mt Ascutney, Vermont, New England, USA

I’d love to say that I was meditating within our glorious landscape. I’d really like to say that I was deeply appreciating the sacred gifts offered by Mother Nature. The truth is that I was totally geeking out.

I was reading storylabber Dilan Kiley’s undergraduate Honors thesis. It was awesome! He quantified the spread of information on Twitter in response to sudden, unanticipated events. These system-scale shocks can briefly synchronize our society’s chaotic collective attention. And while reading about them in Dilan’s thesis, I experienced one myself.

I heard a strange noise nearby, and looked up to find a moose staring at me, from 10 feet away. Well played Dilan.

The moose was out of breath, having been chased up the hill from the lake by an excited mob of followers. In a moment that seemed to last several seconds, we looked at each other. I was in awe. The moose looked unsure, confused, and lost.

Our time together was quickly interrupted by animal control officers. They were sprinting after the moose, trying to steer it safely into the woods. A small flock of undergraduates followed, looking at each other in disbelief at what they were seeing.

Not surprisingly, a seven foot tall, thousand pound wild animal jogging through campus caused quite a stir! Pictures of the moose received thousands of likes on social media, and #mooseontheloose started trending, at least here in Vermont. After a few hours in the spotlight, Vermont Fish & Wildlife happily reported that the moose found its way back into the woods north of campus.

I tell you this story today because I think the moose’s adventure offers some lessons for us as you wander off campus to find your way home.

In the past few weeks, I’ve spoken to many of you, asking about your plans for the future. This is a time of great transition in your life. Most of you don’t have a grand plan, or even a muddy pond to call home.

Like the moose, you too may feel a bit lost. You too will have many people taking your picture, and making a big fuss over you. 

Over the coming months, you too will have well-intentioned loved ones trying to steer you to a safe path in life, advising you where to go, and what to do. You too will have to find your way through a noisy, often confusing set of uncertain options.

As people, we imitate role models whom we admire, using their past choices to inform our own. As scientists, we use mathematical models to make predictions, which are helpful, because unfortunately, observations of the future are not available at this time [1].

Seemingly inconsequential decisions, that you make, may change your life in the biggest ways. But which decisions are most important? To which decisions will your life be sensitively dependent?

I reached out to the hero of our story via his parody Twitter account @BTVMoose. Really. Talk about geeking out. I asked for words of wisdom for the class of 2014. Overcoming the great modern difficulty of finding a wireless internet signal in the dense forest, he was able to tweet this advice:

To paraphrase this bit of spiritual guidance: you may need to wander around a bit, before you find your way.

[1] Original quote from Knutson and Tuleya, Journal of Climate, 2005.

 

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How our storytelling nature means we deeply misunderstand the mechanics of fame (and much else…)

Should the Mona Lisa be our most famous painting?

Was Harry Potter destined to (repeatedly) sweep the globe?

What would happen to everyone and everything famous if we ran the experiment that is our world over again?

Find out why fame is truly unpredictable, how it lives and dies entirely in our social stories, and why “… there is no such thing as fate, only the story of fate” in a current Nautilus Magazine piece by the Computational Story Lab’s co-team leader Peter Dodds:

“Homo Narrativus and the Trouble with Fame: We think that fame is deserved. We are wrong.”  

Nautilus is a new, design-driven publication on science published both online (free) and in print (unfree).  The Nautilus team is creating a beautiful showcase for scientific knowledge, and we encourage you to explore everything they have on offer.

nautilus-crowd

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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

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.

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.

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.

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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…

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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: .

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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 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.

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.

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.

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.

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.

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