EXPLORATORY ANALYSIS OF SLEEP PATTERNS AND FACTORS USING TWITTER DATA

Abstract

Online networking examinations on various social media platforms can reveal impressive insights into human conduct, increasing factual quality from the size of such connections. Not many studies have been conducted on how demographics and geographic data are related to social media activities to predict day-to-day human activities or their precise sleeping cycles. In this paper, we break down Twitter information to extract knowledge regarding factors influencing how much sleep different populations get. Reduction in sleep duration can be influenced by various factors including time zones, weather, age, seasonal impacts, and even locations. We have utilized Twitter data timestamps, location geographical tagging, and personal bios of Twitter users to analyze the sleep pattern influenced by such external factors. To establish a better relationship between Twitter activities and the demographics and geographic factors, Twitter activities from all the US states, and European and Asian countries over the period of a few months in a year were analyzed. Based on our observations and analysis we may assert, that connecting social media activity to geographic and demographic variables can aid in our understanding of daily anthropological activity.