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      Using social media to promote academic research: Identifying the benefits of twitter for sharing academic work

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          Abstract

          To disseminate research, scholars once relied on university media services or journal press releases, but today any academic can turn to Twitter to share their published work with a broader audience. The possibility that scholars can push their research out, rather than hope that it is pulled in, holds the potential for scholars to draw wide attention to their research. In this manuscript, we examine whether there are systematic differences in the types of scholars who most benefit from this push model. Specifically, we investigate the extent to which there are gender differences in the dissemination of research via Twitter. We carry out our analyses by tracking tweet patterns for articles published in six journals across two fields (political science and communication), and we pair this Twitter data with demographic and educational data about the authors of the published articles, as well as article citation rates. We find considerable evidence that, overall, article citations are positively correlated with tweets about the article, and we find little evidence to suggest that author gender affects the transmission of research in this new media.

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          Experimental study of inequality and unpredictability in an artificial cultural market.

          Hit songs, books, and movies are many times more successful than average, suggesting that "the best" alternatives are qualitatively different from "the rest"; yet experts routinely fail to predict which products will succeed. We investigated this paradox experimentally, by creating an artificial "music market" in which 14,341 participants downloaded previously unknown songs either with or without knowledge of previous participants' choices. Increasing the strength of social influence increased both inequality and unpredictability of success. Success was also only partly determined by quality: The best songs rarely did poorly, and the worst rarely did well, but any other result was possible.
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            Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact

            Background Citations in peer-reviewed articles and the impact factor are generally accepted measures of scientific impact. Web 2.0 tools such as Twitter, blogs or social bookmarking tools provide the possibility to construct innovative article-level or journal-level metrics to gauge impact and influence. However, the relationship of the these new metrics to traditional metrics such as citations is not known. Objective (1) To explore the feasibility of measuring social impact of and public attention to scholarly articles by analyzing buzz in social media, (2) to explore the dynamics, content, and timing of tweets relative to the publication of a scholarly article, and (3) to explore whether these metrics are sensitive and specific enough to predict highly cited articles. Methods Between July 2008 and November 2011, all tweets containing links to articles in the Journal of Medical Internet Research (JMIR) were mined. For a subset of 1573 tweets about 55 articles published between issues 3/2009 and 2/2010, different metrics of social media impact were calculated and compared against subsequent citation data from Scopus and Google Scholar 17 to 29 months later. A heuristic to predict the top-cited articles in each issue through tweet metrics was validated. Results A total of 4208 tweets cited 286 distinct JMIR articles. The distribution of tweets over the first 30 days after article publication followed a power law (Zipf, Bradford, or Pareto distribution), with most tweets sent on the day when an article was published (1458/3318, 43.94% of all tweets in a 60-day period) or on the following day (528/3318, 15.9%), followed by a rapid decay. The Pearson correlations between tweetations and citations were moderate and statistically significant, with correlation coefficients ranging from .42 to .72 for the log-transformed Google Scholar citations, but were less clear for Scopus citations and rank correlations. A linear multivariate model with time and tweets as significant predictors (P < .001) could explain 27% of the variation of citations. Highly tweeted articles were 11 times more likely to be highly cited than less-tweeted articles (9/12 or 75% of highly tweeted article were highly cited, while only 3/43 or 7% of less-tweeted articles were highly cited; rate ratio 0.75/0.07 = 10.75, 95% confidence interval, 3.4–33.6). Top-cited articles can be predicted from top-tweeted articles with 93% specificity and 75% sensitivity. Conclusions Tweets can predict highly cited articles within the first 3 days of article publication. Social media activity either increases citations or reflects the underlying qualities of the article that also predict citations, but the true use of these metrics is to measure the distinct concept of social impact. Social impact measures based on tweets are proposed to complement traditional citation metrics. The proposed twimpact factor may be a useful and timely metric to measure uptake of research findings and to filter research findings resonating with the public in real time.
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              The Gender Citation Gap in International Relations

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Methodology
                Role: ConceptualizationRole: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: VisualizationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                6 April 2020
                2020
                : 15
                : 4
                : e0229446
                Affiliations
                [1 ] School of Government & Public Policy, University of Arizona, Tucson, AZ, United States of America
                [2 ] Department of Political Science, Stony Brook University, Stony Brook, NY, United States of America
                [3 ] Manship School of Mass Communication & Department of Political Science, Louisiana State University, Baton Rouge, LA, United States of America
                [4 ] School of Government & Public Policy, University of Arizona, Tucson, AZ, United States of America
                West Pomeranian University of Technology, POLAND
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-4863-9949
                Article
                PONE-D-19-22775
                10.1371/journal.pone.0229446
                7135289
                32251463
                b8155926-bd87-4e65-b0fc-9efb793b62eb
                © 2020 Klar et al

                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
                : 23 August 2019
                : 6 February 2020
                Page count
                Figures: 4, Tables: 3, Pages: 15
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Twitter
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Twitter
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Twitter
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Social Sciences
                Sociology
                Social Research
                Research and Analysis Methods
                Research Assessment
                Citation Analysis
                Research and Analysis Methods
                Scientific Publishing
                Social Sciences
                Political Science
                Research and Analysis Methods
                Research Assessment
                Bibliometrics
                Social Sciences
                Sociology
                Communications
                Custom metadata
                The dataset is available at the Harvard Dataverse. The relevant DOI is: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DLH12J.

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