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      Academic information on Twitter: A user survey

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          Abstract

          Although counts of tweets citing academic papers are used as an informal indicator of interest, little is known about who tweets academic papers and who uses Twitter to find scholarly information. Without knowing this, it is difficult to draw useful conclusions from a publication being frequently tweeted. This study surveyed 1,912 users that have tweeted journal articles to ask about their scholarly-related Twitter uses. Almost half of the respondents (45%) did not work in academia, despite the sample probably being biased towards academics. Twitter was used most by people with a social science or humanities background. People tend to leverage social ties on Twitter to find information rather than searching for relevant tweets. Twitter is used in academia to acquire and share real-time information and to develop connections with others. Motivations for using Twitter vary by discipline, occupation, and employment sector, but not much by gender. These factors also influence the sharing of different types of academic information. This study provides evidence that Twitter plays a significant role in the discovery of scholarly information and cross-disciplinary knowledge spreading. Most importantly, the large numbers of non-academic users support the claims of those using tweet counts as evidence for the non-academic impacts of scholarly research.

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          Most cited references51

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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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            Problems of citation analysis: A critical review

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              Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: SoftwareRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                17 May 2018
                2018
                : 13
                : 5
                : e0197265
                Affiliations
                [1 ] School of Library and Information Science, University of South Carolina, Columbia, South Carolina, United States of America
                [2 ] Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
                [3 ] Statistical Cybermetrics Research Group, School of Mathematics and Computer Science, University of Wolverhampton, Wolverhampton, United Kingdom
                Institut Català de Paleoecologia Humana i Evolució Social (IPHES), SPAIN
                Author notes

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

                Author information
                http://orcid.org/0000-0003-3481-6991
                Article
                PONE-D-17-35307
                10.1371/journal.pone.0197265
                5957360
                29771947
                793de139-a6e8-4b98-8b97-5b669a246482
                © 2018 Mohammadi 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
                : 29 September 2017
                : 30 April 2018
                Page count
                Figures: 2, Tables: 2, Pages: 18
                Funding
                Funded by: The National Institutes of Health’s National Center for Advancing Translational Sciences
                Award ID: UL1TR001422
                Award Recipient :
                This work was supported by the National Institutes of Health's National Center for Advancing Translational Sciences in grant UL1TR001422 awarded to Northwestern University. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                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
                Research and Analysis Methods
                Research Design
                Survey Research
                Surveys
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Sciences
                People and Places
                Population Groupings
                Professions
                Social Sciences
                Sociology
                Communications
                Research and Analysis Methods
                Research Design
                Survey Research
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Sciences
                Sociology
                Social Networks
                Custom metadata
                Data cannot be made publicly available due to the respect the privacy of participants and as Northwestern University IRB committee policy requires that the data should only be shared with qualified researchers. Therefore, the data can be shared with qualified scholars upon request. Data access requests can be sent to irb@ 123456northwestern.edu or sbsirb@ 123456northwestern.edu .

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