Summary: Using AI to examine over 800 million tweets, researchers reveal how our mode of thinking alters during the course of the day. The study reports early morning tweets tend to be correlated with more logical thinking patterns, while middle of the night tweets tend to exhibit more existential concerns.
Source: University of Bristol.
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 AImethods 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.
At 6 am, analytical thinking was shown to peak, the words and language at this time were shown to correlate with a more logical way of thinking. However, in the evenings and nights this thinking style changed to a more emotional and existential one.
Although 73 different psychometric quantities were tracked, the team found there were just two independent underlying factors that explained most of the temporal variations across the data.
The first factor, with a peak expression time starting at around 5 am to 6 am, linked with measures of analytical thinking through the high use of nouns, articles and prepositions, which has been related, in other studies, to intelligence, improved class performance and education. This early-morning period also shows increased concern with achievement and power. At the opposite end of the spectrum, the researchers find a more impulsive, social, and emotional mode.
The second factor had a peak expression time starting at 3 am to 4 am, the aggregated twitter content found this time to be correlated with the language of existential concerns but anticorrelated with expression of positive emotions.
Overall, the study discovered strong evidence that our language changes dramatically between night and day, reflecting changes in our concerns and underlying cognitive and emotional processes. These shifts also occur at times associated with major changes in neural activity and hormonal levels, suggesting possible relations with our circadian clock. Furthermore, the study revealed both cognitive and emotional states change in a predictable way during the 24 hours.
Professor Nello Cristianini, Professor of Artificial Intelligence and the project lead, said: “The analysis of media content, when done correctly, can reveal useful information for both social and biological sciences. We are still trying to learn how to make the most of it.”
Stafford Lightman, Professor of Medicine and a neuroendocrinology expert at Bristol Medical School, and one of the study’s authors, added: “Circadian rhythms are a major feature of most systems in the human body, and when these are disrupted they can result in psychiatric, cardiovascular and metabolic disease. The use of media data allows us to analyse neuropsychological parameters in a large unbiased population and gain insights into how mood-related use of language changes as a function of time of day. This will help us understand the basis of disorders in which this process is disrupted.”
Source: University of Bristol
Publisher: Organized by NeuroscienceNews.com.
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Original Research: Open access research for “Diurnal variations of psychometric indicators in Twitter content” by Fabon Dzogang, Stafford Lightman, and Nello Cristianini in PLOS ONE. Published June 20 2018
[cbtabs][cbtab title=”MLA”]University of Bristol “Study of 800 Millions Tweets Finds Distinct Daily Cycles in Our Thinking Patterns.” NeuroscienceNews. NeuroscienceNews, 20 June 2018.
<https://neurosciencenews.com/twitter-thinking-patterns-9390/>.[/cbtab][cbtab title=”APA”]University of Bristol (2018, June 20). Study of 800 Millions Tweets Finds Distinct Daily Cycles in Our Thinking Patterns. NeuroscienceNews. Retrieved June 20, 2018 from https://neurosciencenews.com/twitter-thinking-patterns-9390/[/cbtab][cbtab title=”Chicago”]University of Bristol “Study of 800 Millions Tweets Finds Distinct Daily Cycles in Our Thinking Patterns.” https://neurosciencenews.com/twitter-thinking-patterns-9390/ (accessed June 20, 2018).[/cbtab][/cbtabs]
Diurnal variations of psychometric indicators in Twitter content
The psychological state of a person is characterised by cognitive and emotional variables which can be inferred by psychometric methods. Using the word lists from the Linguistic Inquiry and Word Count, designed to infer a range of psychological states from the word usage of a person, we studied temporal changes in the average expression of psychological traits in the general population. We sampled the contents of Twitter in the United Kingdom at hourly intervals for a period of four years, revealing a strong diurnal rhythm in most of the psychometric variables, and finding that two independent factors can explain 85% of the variance across their 24-h profiles. The first has peak expression time starting at 5am/6am, it correlates with measures of analytical thinking, with the language of drive (e.g power, and achievement), and personal concerns. It is anticorrelated with the language of negative affect and social concerns. The second factor has peak expression time starting at 3am/4am, it correlates with the language of existential concerns, and anticorrelates with expression of positive emotions. Overall, we see strong evidence that our language changes dramatically between night and day, reflecting changes in our concerns and underlying cognitive and emotional processes. These shifts occur at times associated with major changes in neural activity and hormonal levels.