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      Twitter Language Use Reflects Psychological Differences between Democrats and Republicans

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          Abstract

          Previous research has shown that political leanings correlate with various psychological factors. While surveys and experiments provide a rich source of information for political psychology, data from social networks can offer more naturalistic and robust material for analysis. This research investigates psychological differences between individuals of different political orientations on a social networking platform, Twitter. Based on previous findings, we hypothesized that the language used by liberals emphasizes their perception of uniqueness, contains more swear words, more anxiety-related words and more feeling-related words than conservatives’ language. Conversely, we predicted that the language of conservatives emphasizes group membership and contains more references to achievement and religion than liberals’ language. We analysed Twitter timelines of 5,373 followers of three Twitter accounts of the American Democratic and 5,386 followers of three accounts of the Republican parties’ Congressional Organizations. The results support most of the predictions and previous findings, confirming that Twitter behaviour offers valid insights to offline behaviour.

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          Most cited references28

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          Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures.

          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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            The Secret Lives of Liberals and Conservatives: Personality Profiles, Interaction Styles, and the Things They Leave Behind

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              National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic

              Social media have been proposed as a data source for influenza surveillance because they have the potential to offer real-time access to millions of short, geographically localized messages containing information regarding personal well-being. However, accuracy of social media surveillance systems declines with media attention because media attention increases “chatter” – messages that are about influenza but that do not pertain to an actual infection – masking signs of true influenza prevalence. This paper summarizes our recently developed influenza infection detection algorithm that automatically distinguishes relevant tweets from other chatter, and we describe our current influenza surveillance system which was actively deployed during the full 2012-2013 influenza season. Our objective was to analyze the performance of this system during the most recent 2012–2013 influenza season and to analyze the performance at multiple levels of geographic granularity, unlike past studies that focused on national or regional surveillance. Our system’s influenza prevalence estimates were strongly correlated with surveillance data from the Centers for Disease Control and Prevention for the United States (r = 0.93, p < 0.001) as well as surveillance data from the Department of Health and Mental Hygiene of New York City (r = 0.88, p < 0.001). Our system detected the weekly change in direction (increasing or decreasing) of influenza prevalence with 85% accuracy, a nearly twofold increase over a simpler model, demonstrating the utility of explicitly distinguishing infection tweets from other chatter.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                16 September 2015
                2015
                : 10
                : 9
                : e0137422
                Affiliations
                [001]Cognitive Science Research Group, Queen Mary University of London, School of Electronic Engineering and Computer Science, Mile End Road, London, United Kingdom
                University of Vermont, UNITED STATES
                Author notes

                Competing Interests: Purver holds grants for language processing research including concept creation (ConCreTe) and dementia diagnosis (SLADE). The SLADE project is funded by the Queen Mary Innovation Fund; the project ConCreTe acknowledges the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under FET grant number 611733. He co-founded the social media analytics company Chatterbox Labs Limited in 2011, and remains a shareholder; he is co-inventor on a pending patent application for language analysis for mental health diagnosis. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                Conceived and designed the experiments: KS MP. Performed the experiments: KS. Analyzed the data: KS MP. Contributed reagents/materials/analysis tools: KS MP. Wrote the paper: KS MP. Collected the data: KS.

                Article
                PONE-D-15-11526
                10.1371/journal.pone.0137422
                4574198
                26375581
                68e21e11-73c6-406d-ab81-72a9c2138d64
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 16 March 2015
                : 17 August 2015
                Page count
                Figures: 3, Tables: 9, Pages: 18
                Funding
                The authors received no specific funding for this work. This work was part of KS MSc degree. During the conduct of this study, Purver was partly supported by the ConCreTe project; the project ConCreTe acknowledges the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under FET grant number 611733.
                Categories
                Research Article
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                All files are available from the figshare repository at: figshare.com/s/0671bfc0c82211e4830306ec4b8d1f61.

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