While we generally start off the morning on the right foot, a new study has used millions of tweets from around the world to determine that our collective moods deteriorate over the course of a day, using software that Kiwis helped to develop.
The American research, published in international journal Science, was based on a sample of 509 million tweets from 2.4 million users in 84 countries.
The researchers analysed the effect (mood) of the language used in tweets at various times of the day, using an analysis system that also took emoticons such as :) into account.
The text analysis software used in the study, known as Linguistic Inquiry and Word Count (LIWC), was first developed in the early 2000s as a joint project between the University of Auckland Medical School and the University of Texas (check it out at www.analyzewords.com).
Around the world, people showed similar rhythms of mood across the day, with two daily positive peaks early in the morning and near midnight – a pattern suggesting mood may be shaped by work-related stress.
Positive tweets were also more abundant on Saturdays and Sundays, with the morning peaks occurring about two hours later in the day.
Positive tweets and late-morning mood peaks were more prominent on Fridays and Saturdays in the United Arab Emirates, where the traditional working week is Sunday through Thursday.
Prof Roger J Booth of the University of Auckland Medical School, who helped to create the LIWC, said the similarity of diurnal, weekly and season changes across cultures was interesting to see.
“One of the strengths of this study is its size, another is its cross-cultural nature, a third is that it is ‘naturalistic’ – i.e. it is people in their normal lives sending twitter messages," he told the Science Media Centre.
“They also discussed the potential problems that might have been associated with emotion words being part of phrases. An example here would be 'good morning' or 'good night' elevating the scores of positive emotion at the times of day these greetings are used. However, when they filtered out such phrases and re-analysed their data they got the same results, so these potential limitations were not causing and significant skewing.”
He said the software assigned words to one or more categories based on what they generally denoted, then reported their occurrence as a percentage of the total words in the text being analysed.
“Although everyone will express their emotions in slightly different ways, this study has examined the expression throughout the day and looked for changes in each person’s emotional expression related to their average expression. By doing this, they have controlled for some of the individual variability."