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According to a 2009 study by Sysomos, social media analytics company in Toronto, there were slightly more women using Twitter than men – 53 percent to 47 percent, and that five percent of users accounted for 75 percent of all activity. Meanwhile, a Cornell study has looked for insight into Twitter messages and users’ moods.

That’s what life’s like according to Twitter. A study in the journal Science examined the contents of more than 500 million Twitter messages sent in 84 countries over the course of two years, looking for signs of good moods and bad. It found what a lot of us could tell by looking at our own lives.

Optimism is reborn with each new day and slowly erodes as we work, study and go about our quotidian affairs. Our mood lifts as we head home to friends, family, entertainment and beer. Our outlook tends to be sunnier on weekends. And speaking of sun, when it starts to pile up in the spring or disappear in the fall, that affects our mood, too.

The fact that two researchers at Cornell University confirmed such obvious truths across cultures using Twitter as their data source is the other — and possibly more important — finding of the study.

“This is a stone in the foundation of a new social science that is being built,” said Nicholas A. Christakis, a sociologist at Harvard University who was not involved in the research.

“We’re in a similar place that we were in in the 17th century with the discovery of the telescope and microscope.”

The study isn’t the first to use “data-mining” of social media for scholarly (as opposed to commercial) purposes.

A study presented at a conference in Hyderabad, India, last spring analyzed how information flows through Twitter networks of celebrities, bloggers, organizations and media outlets. The Cornell study goes beyond that to examine the emotional state of millions of users.

The research community has not yet judged whether such a sample — non-random, English-speaking, heavily tipped toward the young, well-educated and talkative — is a reasonable surrogate for humanity as a whole. But the fact that it gives predictable answers suggests that it may be.

“This should reassure people the method is not crazy. You want to sort of calibrate the instrument, and I think these results are a good indication that the instrument is telling you reasonable things,” said Duncan J. Watts, a sociologist and researcher at Yahoo! Labs.

Other experts, however, wonder whether just knowing a person’s or a population’s emotional state tells you much.

“The real problem with this method is that you don’t know what the people are doing,” said Jonathan Gershuny, a sociologist who directs the Center for Time Use Research at the University of Oxford. “All you know is they’re on their social network sites. The real job is to find out what has got them steamed up.”

Traditionally, researchers have used surveys, records of daily activity that a sample of people are asked to keep and even individual diaries to get an idea of how people’s moods change with activity and time.

If it turns out that Twitter and other forms of “massive passive” data is a way of tracking the emotional state of individuals and populations, it may be possible to answer other, more detailed questions.

How do news events, changes in government policies or economic conditions affect mood? Are there “emotional bubbles” similar to “market bubbles”?

The questions don’t have to be about emotion; they can involve attitude and behavior. Does the frequency and timing of religious expression differ by religion or region? Does the prevalence of eating disorders, as indicated by certain words in messages, differ by country or locality? Already, the Cornell researchers have analyzed Twitter messages and found an interesting connection: peak usage of the word “beer” and “drunk” occur seven hours apart.

In the study, Scott A. Golder, a doctoral candidate in sociology, and his faculty adviser, Michael W. Macy, used an archive of Twitter messages sent from February 2008 through January 2010. Originally designed for use by third-party software developers, the archive is increasingly being used by scholars, too.

Golder and Macy examined 510 million tweets written by 2.5 million users, collecting up to 400 per person and ignoring only occasional tweeters. Only messages from Twitter users who made their tweets public when they signed up for the service were used. Age, sex and other demographic information wasn’t available, but most Twitter accounts include time-zone information, and often the user’s country, so some information could be inferred.

Using a specially written software program, the researchers looked for the presence of words known to signal “positive affect” — which consisted of enthusiasm, delight, alertness, determination — and “negative affect” — indicating distress, fear, anger, guilt and disgust.

Positive feelings peaked in the morning between about 7 and 9 o’clock, then descended to a trough between 3 and 6 p.m. before rising again and peaking around midnight. Negative feelings were both less commonly expressed and showed much less variability. They were lowest early in the morning, rising slightly all day long. Like good feelings, bad ones also rose at night, peaking around 10 p.m.

The fact that both positive and negative Twitter messages are common in the evening hours suggests that’s the most emotional time of the day. The weekly peak for both types of message is Saturday night.

The researchers also found that the baseline “positivity” of Twitter messages grew as the day length grew most dramatically around the summer solstice.

The consistency suggests something powerful is driving the emotional rhythm that isn’t directly linked to work or school.

“People do seem to be refreshed by sleep,” Golder said, pointing to one obvious possibility. “Perhaps underlying we are all the same, responding to the same biological rhythms.”

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