Tweeters on the West Coast are con­sid­er­ably hap­pier than those on the East Coast, and Twitter users are hap­piest on Sunday morn­ings and unhap­piest on Thursday evenings.

So say a team of researchers from North­eastern Uni­ver­sity and Har­vard Med­ical School, who recently ana­lyzed the lan­guage of 300 mil­lion tweets in an effort to mea­sure the col­lec­tive hap­pi­ness of people across the country.

The New York Times and CBS Evening News reported on the team’s research.

All of their data is rep­re­sented in a mod­i­fied map of the United States that tracks the mood of users throughout the day.

By gaining access to real-​​time data, researchers say they could even­tu­ally explain how the col­lec­tive mood of Twitter Nation changes in response to spe­cific events, such as a sudden dip in the stock market, or the results of the Super Bowl.

Our method is a new way to extract infor­ma­tion about people every­where,” said Sune Lehmann, a post­doc­toral research asso­ciate in theCol­lege of Com­puter and Infor­ma­tion Sci­ence at North­eastern, who con­tributed to the ongoing study of the char­ac­ter­is­tics and dynamics of Twitter.

Using this strategy, we could track how ideas and Internet memes spread,” he added.

Other col­lab­o­ra­tors included Northeastern’s Alan Mis­love, an assis­tant pro­fessor of com­puter and infor­ma­tion sci­ence; Yong-​​Yeol Ahn, a post­doc­toral research asso­ciate at Northeastern’s Center for Com­plex Net­work Research; and Jukka-​​Pekka Onnela, and J. Niels Rosen­quist, both of Har­vard Med­ical School.

Researchers were able to infer the mood of each tweet using a psy­cho­log­ical word-​​rating system devel­oped by the National Insti­tute of Mental Health’s Center for the Study of Emo­tion and Atten­tion. The system ranks words based on how they make people feel.

Tweets con­taining words such as “love,” or tri­umph,” for example, received high mood scores, whereas mes­sages with words such as “hell,” or “death” earned low marks.

Researchers then cal­cu­lated an hour-​​by-​​hour average mood score for users in each state, and geo­graph­i­cally rep­re­sented the data using a density-​​equalizing map in which each region is scaled to rep­re­sent its number of tweets as opposed to its land area.

Sparsely pop­u­lated states rack up far fewer tweets than those in more densely pop­u­lated regions, leading to “higher fluc­tu­a­tions in mood that do not rep­re­sent the under­lying data and create too much [visual] ‘noise’ when using a reg­ular U.S. map,” noted Mislove.