Big data-based analysis of massive datasets of modern and historical news, social media, and Wikipedia page views can reveal periodic patterns in collective population behavior that could otherwise be overlooked.
By analyzing these sources in two studies, researchers from the ThinkBIG project at the U.K.'s University of Bristol found these patterns are not only more predictable than previously thought, but are usually only uncovered by analyzing a large population's activities for a prolonged period.
"What emerges is a glimpse at the regularities in our behavior that are hidden behind the day-to-day variations in our lives," says ThinkBIG leader and Bristol professor Nello Cristianini.
The first study examined 87 years of U.S. and U.K. newspapers between 1836 and 1922. The researchers found the weather and seasons strongly regulated people's leisure and work, with words such as picnic or excursion peaking every summer in both countries.
The second study analyzed the aggregate sentiment in Twitter in the U.K. and aggregate Wikipedia access over four years, and found seasons also have a significant influence on mental health.
Researchers observed an overexpression of negative sentiment in wintertime, while anxiety and anger are overexpressed between September and April.
From University of Bristol News
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