We feel: mapping emotion on Twitter.

Abstract

Research data on predisposition to mental health problems, and the fluctuations and regulation of emotions, thoughts, and behaviors are traditionally collected through surveys, which cannot provide a real-time insight into the emotional state of individuals or communities. Large datasets such as World Health Organization (WHO) statistics are collected less than once per year, whereas social network platforms, such as Twitter, offer the opportunity for real-time analysis of expressed mood. Such patterns are valuable to the mental health research community, to help understand the periods and locations of greatest demand and unmet need. We describe the "We Feel" system for analyzing global and regional variations in emotional expression, and report the results of validation against known patterns of variation in mood. 2.73 ×10(9) emotional tweets were collected over a 12-week period, and automatically annotated for emotion, geographic location, and gender. Principal component analysis (PCA) of the data illustrated a dominant in-phase pattern across all emotions, modulated by antiphase patterns for "positive" and "negative" emotions. The first three principal components accounted for over 90% of the variation in the data. PCA was also used to remove the dominant diurnal and weekly variations allowing identification of significant events within the data, with z-scores showing expression of emotions over 80 standard deviations from the mean. We also correlate emotional expression with WHO data at a national level and although no correlations were observed for the burden of depression, the burden of anxiety and suicide rates appeared to correlate with expression of particular emotions.