2011/01/31 by Peter Sheridan Dodds, Kameron Decker Harris, Isabel M. Kloumann +2 · 1 citation
Arts and Humanities · Computer Science · Physics and Astronomy · Psychology · #Business #Complex Network Analysis Techniques #Computer science #Construct (python library) #Data science #Economics #Gross domestic product #Happiness #Information retrieval #Marketing #Media Influence and Health #Mental Health Research Topics #Metric (unit) #Microblogging #Psychology #Ranging #Set (abstract data type) #Social media #Social psychology #Telecommunications #World Wide Web #cs.SI #physics.soc-ph
paper · pdf · doi:10.1371/journal.pone.0026752
published as PLoS ONE, Vol 6(2): e26752, 2011 · 27 pages, 17 figures, 3 tables. Supplementary Information: 1 table, 52 figures
openalex publication_date 2011/12/07 · arxiv created 2011/12/08 · arxiv updated 2015/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 construct a tunable, real-time, remote-sensing, and non-invasive, text-based 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 and tunable metric can be constructed and defended.