Analysing Mood Patterns in the United Kingdom through Twitter Content

TL;DR

This study analyzes Twitter data from the UK to identify daily and seasonal mood patterns using affective words, revealing significant circadian rhythms with a consistent mid-morning peak across emotions.

Contribution

It introduces a method for computing mood scores from Twitter content and applies it to large-scale data to uncover daily and seasonal mood variations.

Findings

Strong circadian mood patterns detected across all emotions.

No significant seasonal variation observed in mood signals.

Mid-morning peak identified as a common feature for all moods.

Abstract

Social Media offer a vast amount of geo-located and time-stamped textual content directly generated by people. This information can be analysed to obtain insights about the general state of a large population of users and to address scientific questions from a diversity of disciplines. In this work, we estimate temporal patterns of mood variation through the use of emotionally loaded words contained in Twitter messages, possibly reflecting underlying circadian and seasonal rhythms in the mood of the users. We present a method for computing mood scores from text using affective word taxonomies, and apply it to millions of tweets collected in the United Kingdom during the seasons of summer and winter. Our analysis results in the detection of strong and statistically significant circadian patterns for all the investigated mood types. Seasonal variation does not seem to register any important divergence in the signals, but a periodic oscillation within a 24-hour period is identified for each mood type. The main common characteristic for all emotions is their mid-morning peak, however their mood score patterns differ in the evenings.