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Related Experiment Video

Updated: Feb 23, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

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Twitter as a means to study temporal behaviour.

Till Roenneberg¹

¹Institute for Medical Psychology, Medical Faculty, LMU, Munich, 80336, Germany.

Current Biology : CB

|September 13, 2017

Summary

This summary is machine-generated.

Social media data, like tweets, can reveal individual sleep-wake patterns. This study analyzed Twitter activity, finding daily peaks and seasonal trends related to dawn.

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Area of Science:

Computational biology
Chronobiology
Social media analytics

Background:

Biomedical research traditionally uses vital statistics, but the internet offers vast digital data for studies.
Internet-based research often analyzes large populations, with less focus on individual social media behavior.
Analyzing individual sleep-wake patterns using social media data is an emerging research area.

Discussion:

Time series analysis, a method common in sleep research, was applied to a single Twitter account's data.
The study examined approximately 12,000 tweets from December 2014 to March 2017.
The consistent use of an Android phone for tweeting was noted, suggesting a primary user or consistent group.

Key Insights:

Tweet activity from the analyzed account exhibited two daily peaks: early morning and late night.
These daily activity peaks demonstrated a significant seasonal variation, correlating with the timing of dawn.
This suggests that social media posting times can reflect underlying circadian rhythms and seasonal changes.

Outlook:

Further research can explore using social media data to understand individual circadian rhythms and behavioral patterns.
This methodology could be expanded to analyze other social media platforms and user behaviors.
Investigating the influence of external factors like dawn on social media activity provides novel insights into human behavior patterns.