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      Analysing Mood Patterns in the United Kingdom through Twitter Content

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          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.

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          Most cited references 15

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          An algorithm for suffix stripping

           M.F. Porter (1980)
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            The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic

            Twitter is a free social networking and micro-blogging service that enables its millions of users to send and read each other's “tweets,” or short, 140-character messages. The service has more than 190 million registered users and processes about 55 million tweets per day. Useful information about news and geopolitical events lies embedded in the Twitter stream, which embodies, in the aggregate, Twitter users' perspectives and reactions to current events. By virtue of sheer volume, content embedded in the Twitter stream may be useful for tracking or even forecasting behavior if it can be extracted in an efficient manner. In this study, we examine the use of information embedded in the Twitter stream to (1) track rapidly-evolving public sentiment with respect to H1N1 or swine flu, and (2) track and measure actual disease activity. We also show that Twitter can be used as a measure of public interest or concern about health-related events. Our results show that estimates of influenza-like illness derived from Twitter chatter accurately track reported disease levels.
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              Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures.

               S Golder,  M Macy (2011)
              We identified individual-level diurnal and seasonal mood rhythms in cultures across the globe, using data from millions of public Twitter messages. We found that individuals awaken in a good mood that deteriorates as the day progresses--which is consistent with the effects of sleep and circadian rhythm--and that seasonal change in baseline positive affect varies with change in daylength. People are happier on weekends, but the morning peak in positive affect is delayed by 2 hours, which suggests that people awaken later on weekends.
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                Author and article information

                Journal
                1304.5507

                Social & Information networks, General physics

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