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        <datestamp>2026-09-28T05:39:05Z</datestamp>
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          <dc:title>Data Sheet 1_Differences in disciplinary concentration and linguistic complexity between scientific posts on Bluesky and X.docx</dc:title>
          <dc:creator>Piotr Religa (166716)</dc:creator>
          <dc:creator>Michel-Edwar Mickael (5261429)</dc:creator>
          <dc:creator>Marzena Łazarczyk (22689179)</dc:creator>
          <dc:creator>Norwin Kubick (5261426)</dc:creator>
          <dc:creator>Jarosław Olav Horbańczuk (16076486)</dc:creator>
          <dc:creator>Biniyam Tsegaye (25119963)</dc:creator>
          <dc:creator>Mariusz Sacharczuk (3280638)</dc:creator>
          <dc:creator>Atanas G. Atanasov (27419)</dc:creator>
          <dc:subject>Informetrics</dc:subject>
          <dc:subject>Bluesky</dc:subject>
          <dc:subject>data analysis</dc:subject>
          <dc:subject>health information</dc:subject>
          <dc:subject>social media</dc:subject>
          <dc:subject>twitter</dc:subject>
          <dc:description>&lt;p&gt;Several social media platforms provide a route for communication about scientific topics. X, formerly known as Twitter, has been shown to offer researchers an opportunity to exchange scientific information with audiences extending well beyond the research community. In recent years another platform, Bluesky, has emerged and appears to encourage scientific discourse, and a wave of migration of researchers from X to Bluesky has been documented in survey data, poll responses and altmetric records. Whether scientific claims are described differently on the two platforms has not been fully studied. We collected 8,697 Bluesky posts and 3,48,900 X posts mentioning the term science over a matched observation window, restricted in both cases to English-language text written by the posting account. A combination of data analysis and natural language processing tools was then used to compare the discourse on the two platforms, quantifying disciplinary composition, field concentration, cross-disciplinary reference and linguistic register, with effect sizes reported alongside every test and correction applied for multiple comparisons. The two platforms carried posts across a similar range of science-related topics, including health and medicine, biology and life sciences, and engineering and technology. However, posts on X were easier to read (Flesch–Kincaid grade 9.07 against 10.02), addressed the reader more directly (second-person pronouns 0.0153 against 0.0093) and were less complex (complex word density 0.092 against 0.108). Posts on Bluesky were longer (30.6 against 28.7 words; 15.98 against 14.54 words per sentence), more concentrated within single disciplines (mean field isolation 0.6882 against 0.6200), higher in first-person pronoun use (0.0255 against 0.0208) and more often classified as academic (16.6% against 12.7%). These patterns are consistent with greater peer-oriented scientific discourse on Bluesky and greater audience-oriented dissemination on X, although audience composition was not measured directly.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-28T05:39:05Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.3389/frma.2026.1912970.s001</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/Data_Sheet_1_Differences_in_disciplinary_concentration_and_linguistic_complexity_between_scientific_posts_on_Bluesky_and_X_docx/34009380</dc:relation>
          <dc:rights>CC BY 4.0</dc:rights>
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