Tweets show daily rhythm of our thinking.

Tweets show daily rhythm of our thinking. A study of 800-million tweets finds distinct daily cycles in our thinking patterns. Our mode of thinking changes at different times of the day and follows a 24-hour pattern, according to new findings published in PLOS ONE. University of Bristol researchers were able to study our thinking behaviour by analysing seven-billion words used in 800-million tweets. Researchers in artificial intelligence (AI) and in medicine used AI methods to analyse aggregated and anonymised UK twitter content sampled every hour over the course of four years across 54 of the UK's largest cities to determine if our thinking modes change collectively. The researchers revealed different emotional and cognitive modalities in our thoughts by identifying variations in language through tracking the use of specific words across the twitter sample which are associated with 73 psychometric indicators, and are used to help interpret information about our thinking style.

How to Use Twitter to Predict Sickness.

How to use twitter to predict sickness.Researchers have showed how Twitter can be used to predict how likely it is for a Twitter user to become sick. They have also used Twitter to model how other factors -- social status, exposure to pollution, interpersonal interaction and others -- influence health. "If you want to know, down to the individual level, how many people are sick in a population, you would have to survey the population, which is costly and time-consuming," said Adam Sadilek, postdoctoral researcher at the University of Rochester. "Twitter and the technology we have developed allow us to do this passively, quickly and inexpensively; we can listen in to what people are saying and mine this data to make predictions." Sadilek also explained that many tweets are geo-tagged, which means they carry GPS information that shows exactly where the user was when he or she tweeted. Collating all this information allows the researchers to map out, in space and in time, what people said in their tweets, but also where they were and when they were there. By following thousands of users as they tweet and go about their lives, researchers also could estimate interactions between two users and between users and their environment.