We analyze the online response to the preprint publication of a cohort of 4,606 scientific articles submitted to the preprint database arXiv.org between October 2010 and May 2011. We study three forms of responses to these preprints: downloads on the arXiv.org site, mentions on the social media site Twitter, and early citations in the scholarly record. We perform two analyses. First, we analyze the delay and time span of article downloads and Twitter mentions following submission, to understand the temporal configuration of these reactions and whether one precedes or follows the other. Second, we run regression and correlation tests to investigate the relationship between Twitter mentions, arXiv downloads, and article citations. We find that Twitter mentions and arXiv downloads of scholarly articles follow two distinct temporal patterns of activity, with Twitter mentions having shorter delays and narrower time spans than arXiv downloads. We also find that the volume of Twitter mentions is statistically correlated with arXiv downloads and early citations just months after the publication of a preprint, with a possible bias that favors highly mentioned articles.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0047523 | PLOS |
J Public Health Policy
January 2025
Center for Social Norms and Behavioral Dynamics, University of Pennsylvania, Philadelphia, PA, 19104-6304, USA.
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View Article and Find Full Text PDFR Soc Open Sci
January 2025
Mathematics Application Consortium for Science and Industry (MACSI), University of Limerick, Limerick, Ireland.
The analysis of social networks enables the understanding of social interactions, polarization of ideas and the spread of information, and therefore plays an important role in society. We use Twitter data-as it is a popular venue for the expression of opinion and dissemination of information-to identify opposing sides of a debate and, importantly, to observe how information spreads between these groups in our current polarized climate. To achieve this, we collected over 688 000 tweets from the Irish Abortion Referendum of 2018 to build a conversation network from users' mentions with sentiment-based homophily.
View Article and Find Full Text PDFJ Med Internet Res
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Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
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View Article and Find Full Text PDFPLoS One
December 2024
Institute of Industrial Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
To prevent widespread epidemics such as influenza or measles, it is crucial to reach a broad acceptance of vaccinations while addressing vaccine hesitancy and refusal. To gain a deeper understanding of Japan's sharp increase in COVID-19 vaccination coverage, we performed an analysis on the posts of Twitter users to investigate the formation of users' stances toward COVID-19 vaccines and information-sharing actions through the formation. We constructed a dataset of all Japanese posts mentioning vaccines for five months since the beginning of the vaccination campaign in Japan and carried out a stance detection task for all the users who wrote the posts by training an original deep neural network.
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