Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter

TL;DR

This study analyzes over 46 billion Twitter expressions to uncover temporal patterns in happiness and information levels, introducing a robust, real-time hedonometer based on a large, frequency-driven word set.

Contribution

It presents a new, large-scale, frequency-based word list and a real-time hedonometer to measure happiness from social media data, revealing temporal variations.

Findings

Identified significant daily and seasonal happiness patterns.

Developed a robust, scalable method for real-time happiness measurement.

Provided insights into how information levels fluctuate over time.

Abstract

Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often been indirectly characterized and overshadowed by more readily quantifiable economic indicators such as gross domestic product. Here, we examine expressions made on the online, global microblog and social networking service Twitter, uncovering and explaining temporal variations in happiness and information levels over timescales ranging from hours to years. Our data set comprises over 46 billion words contained in nearly 4.6 billion expressions posted over a 33 month span by over 63 million unique users. In measuring happiness, we use a real-time, remote-sensing, non-invasive, text-based approach---a kind of hedonometer. In building our metric, made available with this paper, we conducted a survey to obtain happiness evaluations of over 10,000 individual words, representing a tenfold size improvement over similar existing word sets. Rather than being ad hoc, our word list is chosen solely by frequency of usage and we show how a highly robust metric can be constructed and defended.