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Identifying Twitter audiences: Who is tweeting about scientific papers?

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Haustein, S. & Costas, R. (2015). Identifying Twitter audiences: Who is tweeting about scientific papers?

Presentation at METRICS2015 ASIS&T SIG/MET Workshop
https://www.asist.org/SIG/SIGMET/

Published in: Data & Analytics
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Identifying Twitter audiences: Who is tweeting about scientific papers?

  1. 1. Stefanie Haustein and Rodrigo Costas @stefhaustein @RodrigoCostas1 Identifying Twitter audiences Who is tweeting about scientific papers?
  2. 2. Background • ~20% of recent journal papers shared on Twitter • ~10-15% of researchers use Twitter for work • <3% of researchers’ tweets contain links to papers • Who tweets scientific papers? • Altmetric.com classification*: • Among a random sample of 2,000 accounts tweeting papers, 34% of individuals identified as having PhD • Of 286 users linking to SciELO articles, 24% employed at university, 23% students, 36% not university affiliated *based on Altmetric.com data 06/2015 (e.g., Haustein, Costas, & Larivière, 2015) (e.g., Rowlands et al. 2011; van Noorden, 2014) (Priem & Costello, 2010) (Tsou, Bowman, Ghazinejad, & Sugimoto, 2010) (Alperin, 2015)
  3. 3. Research motivation and objective • Identifying Twitter user types and engagement related to scientific papers • Distinguishing user groups based on: • Twitter account descriptions • Number of followers • Level of engagement with paper
  4. 4. Methods • 1.3 million papers published in WoS papers in 2012 • 663,547 original tweets (no RTs) as captured by Altmetric.com until July 2014 linked to papers via DOI • Twitter profile information for 115,053 handles via Twitter API in April 2015 • Reduction to 89,768 users with English account settings • Account description • Number of followers = exposure • Dissimmilarity with paper title = engagement
  5. 5. exposure engagement influencers / brokers orators / discussing disseminators / mumblers broadcasters Results
  6. 6. Methods • Noun phrases extraction with VOSviewer part-of-speech tagger based on 80,939 account descriptions • 185,824 unique terms extracted from 78,991 accounts • Visualization and clustering of co-occurrence network of 325 most frequent terms (≥100) • Identification of 3 clusters Clustering resolution = 0.9, minimum cluster size = 5 • Calculation per term: Number of Twitter accounts associated with term Average exposure of accounts associated with term Average engagement of accounts associated with term Identification of predominant quadrant of term
  7. 7. 1 2 3 Network of 325 most frequent terms Node size number of accounts associated with term Node color cluster affiliation topics and collectives academic personal Results
  8. 8. low high Node color average engagement of accounts associated with term Node size average exposure of accounts associated with term Co-occurrence network of frequent terms topics and collectives academic personal 1 2 3 Results
  9. 9. Results
  10. 10. Users • High exposure • Low engagement Terms • Science and research • Organizational focus • News Results
  11. 11. Users • Low exposure • High engagement Terms • Scientists and students • Personal preferences Results
  12. 12. Conclusions • Scientific papers are tweeted by • Individuals who identify professionally, personally or both • Organizations or interest groups • Accounts with organizational descriptions seemed to have disseminative role • Accounts with academic or personal terms exhibit higher engagement
  13. 13. Limitations and Outlook • VOSviewer noun phrase extraction • limited to English language • not optimized for Twitter account descriptions • Uncontrolled, uncleaned vocabulary • Reduction to top terms • No systematic analysis of terms  Qualitative coding of accounts  Systematic identification of keywords associated with account types  Testing of four-quadrant hypothesis (engagement↔exposure)  Testing of other user characteristics
  14. 14. Stefanie Haustein and Rodrigo Costas @stefhaustein @RodrigoCostas1 Thank you for your attention! Tuesday, 11/10 1:30pm Grand D

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