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

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          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.

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          Would you be happier if you were richer? A focusing illusion.

          The belief that high income is associated with good mood is widespread but mostly illusory. People with above-average income are relatively satisfied with their lives but are barely happier than others in moment-to-moment experience, tend to be more tense, and do not spend more time in particularly enjoyable activities. Moreover, the effect of income on life satisfaction seems to be transient. We argue that people exaggerate the contribution of income to happiness because they focus, in part, on conventional achievements when evaluating their life or the lives of others.
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            Predicting consumer behavior with Web search.

            Recent work has demonstrated that Web search volume can "predict the present," meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes. We also find that search counts generally boost the performance of baseline models fit on other publicly available data, where the boost varies from modest to dramatic, depending on the application in question. Finally, we reexamine previous work on tracking flu trends and show that, perhaps surprisingly, the utility of search data relative to a simple autoregressive model is modest. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries provide a useful guide to the near future.
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              Computer science. Beyond the data deluge.

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                Author and article information

                Journal
                2011-01-26
                2011-12-08
                Article
                10.1371/journal.pone.0026752
                3233600
                22163266
                1101.5120
                d72e9ae3-fb8c-4a12-9692-f3687e5c3e4e

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                PLoS ONE, Vol 6(2): e26752, 2011
                27 pages, 17 figures, 3 tables. Supplementary Information: 1 table, 52 figures
                physics.soc-ph cs.SI

                Social & Information networks,General physics
                Social & Information networks, General physics

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