Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter

By Peter Sheridan Dodds, Kameron Decker Harris, Isabel M. Kloumann, Catherine A. Bliss, Christopher M. Danforth. PLoS ONE, 2011

838 citations recorded by the source corpus.

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.

Subfields: Experimental and Cognitive Psychology, Literature and Literary Theory, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/09f692cf26f283a22484443cc07aa7cc1f8e70f0

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Citations in the corpus

Forecasting the onset and course of mental illness with Twitter data (2016, methodology): “Word shift graphs We averaged labMT happiness scores across observations in each class, after the removal of common neutral words and retweeted promotional material (38) , .”
The emotional arcs of stories are dominated by six basic shapes (2016, neutral): “Hedonometer, which comprise the emotional arc [22].”
Natural language processing in mental health applications using non-clinical texts† (2017, background): “Golder and Macy (2011) and Dodds et al. (2011) are purely observational, but the others check that geographic and temporal variations correlate well against external statistics.”
SemEval-2017 Task 4: Sentiment Analysis in Twitter (2017, background): “, 2010), social science (Dodds et al., 2011), and market research (Burton and Soboleva, 2011; Qureshi et al.”
Rapid assessment of disaster damage using social media activity (2016, background): “Sentiment in Twitter has been studied and demonstrated to reflect temporal (19, 70) and geographical (71) mood variations.”
Diffusion of Lexical Change in Social Media (2012, background): “A wave of recent research has shown how social media datasets can enable large-scale analysis of patterns of communication [10,11], sentiment [12–14], and influence [15–19].”
Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation (2017, result): “(2013) and by Dodds et al. (2011) were 0.”
The role of administrative data in the big data revolution in social science research. (2016, background): “Picot and Piraino (2012) demonstrate the value of producing cohorts using administrative data in their analysis of immigrant earnings in Canada.”
Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention (2019, methodology): “We used Language Assessment by Mechanical Turk (LabMT), a list of 10,222 words with happiness, rank, internet usage scores, employing strict match and soft match with Reddit posts [18].”
Day of the week and the cross-section of returns (2018, neutral): “This data originates from Dodds et al. (2011). In unreported analysis, using the data of Dodds et al.”
Characterizing the Google Books Corpus: Strong Limits to Inferences of Socio-Cultural and Linguistic Evolution (2015, background): “We present “word shifts” [16] for a few examples of inter-decade divergences in Figs 7–12, specifically comparing the 1940s to the 1930s and the 1980s to the 1950s for the first unfiltered English data set (Figs 7–8) and both English Fiction data sets (Figs 9–12).”
Do all birds tweet the same?: characterizing twitter around the world (2011, background): “[5], which is also more commonly referred to as valence.”