A stock market-style "happiness index" that measures the mood of the world has been launched by US scientists, charting the mood of the planet on a graph that rises or falls, based on tweets.
The Hedonometer plots happiness on a graph that peaks and dips, in much the same way as the FTSE 100 index.
But highs and lows have nothing to do with the financial health of major companies. Instead, they represent the averaged out emotional state of tens of millions of people.
A handout photo issued by the University of Vermont of the results of an online sensing device, called a hedonometer, which shows a drop in global happiness on the day of the Boston Marathon
A team of US scientists constructed the hedonometer from data obtained from the social messaging site Twitter.
Some 50 million tweets from around the world are collected each day and analysed for "happy", "sad" and "neutral" word content.
Words are assigned scores with the happiest and most positive placed at the top of a 1 - 9 scale. From this, an average happiness rating is calculated and plotted.
"Reporters, policymakers, academics - anyone - can come to the site and see population-level responses to major events," said Dr Chris Danforth, from the University of Vermont, one of two US mathematicians who developed the hedonometer.
The team hit the headlines in February after revealing Napa, in the heart of California's wine-growing region, to be the happiest city in the US.
But the global website, providing a way to gauge the happiness of the world, only went public today.
A dramatic hedonometer dip can be seen on Monday, April 15, the day of the Boston marathon bombings - showing how shock waves from such events resonate around the world.
In fact, April 15, 2013, turns out to be the saddest day since the scientists started gathering their data five years ago.
"Many of the articles written in response to the bombing have quoted individual tweets reflecting qualitative micro-stories," said Dr Danforth.
"Our instrument reflects a kind of quantitative macro-story, one that journalists can use to bring big data into an article attempting to characterise the public response to the incident."
The hedonometer is based on a psychological assessment of around 10,000 words. Paid volunteers rated the words for their "emotional temperature", ranking the happiest at the top of the scale and the saddest at the bottom.
Averaging the volunteers' responses, the scientists assigned an overall score to each word. The word "happy" itself scored 8.30, "hahaha" 7.94, "cherry" 7.04 and the more neutral "pancake" 6.96. The words "and" and "the" scored a truly neutral 5.22 and 4.98.
At the bottom of the scale, the word "crash" scored 2.60, "war" 1.80 and "jail" 1.76.
Trending words such as "explosion", "victims" and "kill" pushed the hedonometer down to its lowest ever level on April 15.
Positively scored words such as "prayers" and "families" also spiked that day - but not for positive reasons.
"If we remove 'prayers', 'love', and 'families' it's not going to change the day's overall deviation from the background because of all the other words," said Dr Danforth.
Currently the hedonometer is updated every 24 hours, but further development could see billions of words collected daily to provide a minute-by-minute barometer of global happiness.
The team is also trying to expand beyond "atoms" of single words to "molecules" of two-word expressions.
"It's the relative context that is so important, which is why the sudden drop from the Boston Marathon bombings jumps out at you," said Brian Tivnan, a hedonometer researcher from the MITRE Corporation, a US big-data not-for-profit organisation. "The hedonometer shows the pulse of a society."
The scientists acknowledge that happiness is a slippery word that means different things to different people.
"We're not trying to tell you that contentment is better than happiness - we're not trying to define the word," said Dr Danforth. "We're just saying we're measuring something important and interesting. And, now, sharing it with the world."
Soon the hedonometer will be using data from other sources besides Twitter, including Google Trends, the New York Times, online blogs, CNN transcripts and text captured by the link-shortening service Bitly. It will also be mining data in 12 languages.