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Identifying Twitter audiences: Who is tweeting about scientific papers?

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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

Editor's Notes

  • Alperin, 2015:
    University affiliated: 64%
    unspecified: 16%
    employed: 24%
    student: 23%
    Not university affiliated: 36%
  • 8.9% Twitter handles did not exist anymore
  • Cluster 3 can be clearly identified as ‘academic’ based on terms such as university, science, professor and PhD, which reflect that academics often identify themselves professionally on Twitter.
    Cluster 1 consists of terms that describe users with a focus on ‘personal’ attributes such as life, lover, father, husband, fan or geek as well as non-academic professional terms such as consultant, advocate, work, founder, co-founder or entrepreneur.
    Overlapping with cluster 3, Cluster 1 also contains terms such as scientist, student and biologist. As the network structure is based on the cooccurrence of terms, it can be inferred that Twitter descriptions are often used to identify professionally but reveal also private interests.
    The terms in cluster 2 seem to focus on ‘topics and collectives’, suggesting descriptions for interest groups, organizations or journals, particularly on health-related topics.
  • There is a clear tendency of the academic and personal cluster to show more engagement (red) than the organizational cluster (blue).
    This might be explained by accounts that frequently distribute only paper titles, for example, those of scientific journals, publishers, or even automated accounts.
  • accounts with a high exposure but low engagement (broadcasters) focus on cluster two (organizational)
  • accounts classified as B (orators, discussing) use mostly terms from the personal and academic clusters
  • Random sample of 500 per quadrant

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