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Sari18 sept2015

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Sari18 sept2015

  1. 1. Public Scientific Communication on Twitter Victoria Uren Systems and Data Analytics Workshop Aston University 18 Sept 2015
  2. 2. The team & the papers (so far) •  Uren, V., Dadzie, A.-S., Public scientific communication on Twitter: a visual analytic approach, Aslib Journal of Information Management, 67(3), 2015. •  Uren, V., Dadzie, A.-S., Nerding Out on Twitter: Fun, Patriotism and #Curiosity. In MSM 2013 Making Sense of Microposts, WWW 2013 Companion, Rio de Janeiro, Brazil, 2013. •  Uren, V., Dadzie, A.-S., Ageing Factor: a Potential Altmetric for Observing Events and Attention Spans in Microblogs, In: 1st International Workshop on Knowledge Extraction and Consolidation from Social Media ( KECSM 2012) collocated with the 11th International Semantic Web Conference. •  V.Uren, A.Dadzie, "Relative Trends in Scientific Terms on Twitter", In: altmetrics11: Tracking scholarly impact on the social Web, Workshop at: ACM Web Science Conference 2011.
  3. 3. Why study public science engagement? •  Directing public policy •  Enthuse kids to learn science •  Inform people about fascinating stuff •  Build consensus for social and economic change •  The public paid for the research •  Research Councils encourage engagement
  4. 4. Why look at altmetrics? •  Bauer et al., (2007, p. 90) have called “to expand the range of data ‘officially and legitimately’ relevant for monitoring public understanding of science” •  Altmetrics - measurement of science communication in Web 2.0 media (Priem 2012) •  mainly impact of scholarly papers in Web 2.0 •  Social media present a great opportunity to “talk nerdy” to the public (on.ted.com/Marshall) Bauer, M.A., Allum, N. and Miller, S. (2007), “What can we learn from 25 years of PUS survey research? Liberating and expanding the agenda”, Public Understanding of Science, Vol. 16 No. 1, pp. 79–95. Priem, J., Piwowar, H.A. and Hemminger, B.M. (2012), “Altmetrics in the wild: Using social media to explore scholarly impact”, available at: http://arxiv.org/abs/1203.4745 (accessed 8 March 2015).
  5. 5. Why look at Twitter? •  Low barriers to entry •  Expert and non expert participants •  Contributions on any topic (potentially) •  (AND Twitter data relatively easy to collect) •  BUT typically low levels of tweeting about science
  6. 6. Science Communication Studies Social Media Metrics
  7. 7. Method •  a mixed methods approach to framing analysis for science communication in social media, combining content analysis with high-dimensional visualisation of frames identified using pattern matching •  ‘message frames’ within the tweets, where frames are seen as ways of interpreting topics, identifiable by the use (or avoidance) of certain words and phrases •  visual analysis as a means to observe dynamic changes to the framing of science communication on Twitter
  8. 8. Research Questions •  Does the proposed method support the analysis of dynamic changes in non-trending topics? •  Can changes be observed across disconnected time periods (within days and in samples taken a year apart)? •  Does visualisation-based analysis reveal further information in addition to confirming the content analysis?
  9. 9. Datasets •  3 topics •  Curiosity – a NASA Mars rover with an adventurous lifestyle •  Phosphorus – chemical element with roles in agriculture, biology & warfare •  Permafrost – soil type recognized as a climate change indicator •  2 time periods •  4-9 Aug 2012 (Curiosity Landing) •  Tweets: Curiosity 1194470, Phosphorus 587, Permafrost 311. •  4-9 Aug 2013 (Anniversary) •  Tweets: Curiosity 3310, Phosphorus 6269, Permafrost 618.
  10. 10. Content Analysis •  Samples of 200 (selected using SQL ‘ORDER BY RAND()’) •  one set per topic per year •  Coded according to a frame schema based on (Schäfer 2009) •  Scientific, Political, Economic, ELSI (Ethical Legal & Social Implications) •  Fun, Other Languages, Off Topic •  Coded in rounds until agreement (Hooper) was above 0.6 (all actually above 0.7) Schäfer, M. S. (2009). From Public Understanding to Public Engagement  : An Empirical Assessment of Changes in Science Coverage. Science Communication, 30, 475
  11. 11. Content Analysis: Curiosity Celebration & cat jokes in 2012 More use of ‘curiosity’ in general sense in 2013 International event & USA becoming bi-lingual expected scientific peak concerning lander
  12. 12. Content Analysis Phosphorus Periodic table jokes trending in 2013 Shift of framing from ELSI to Political around ‘white phosphorus’
  13. 13. Content Analysis: Permafrost Record permafrost melt in 2013 Siberian Hairdresser
  14. 14. Visualization •  Sampled day by day •  Larger samples up to 2000 per batch •  Wider range of ‘frames’ detected via pattern matching but inspired by the knowledge built during coding •  Uses parallel coordinates visualization
  15. 15. Curiosity 4-12 Aug. 2012 Curiosity 4-12 Aug. 2013 Landing Day dwarfs other lines Happy Birthday! greater proportion of science in 2013 Spanish speakers
  16. 16. Phosphorus 4-12 Aug. 2012 Phosphorus 4-12 Aug. 2013 Pentagon news item regimeFoods containing P
  17. 17. Permafrost 4-12 Aug. 2012 Permafrost 4-12 Aug. 2013 Results of permafrost melt monitoring Affected populations Enbridge pipeline
  18. 18. Research Questions •  Does the proposed method support the analysis of dynamic changes in non- trending topics? •  Observed for non-trending topics, Permafrost & Phosphorus •  Can changes be observed across disconnected time periods (within days and in samples taken a year apart)? •  shrinkage & growth of audiences •  Shift of emphasis •  Polarisation •  Does visualisation-based analysis reveal further information in addition to confirming the content analysis? •  Smaller time slices •  Pattern matching & visualization -> Labour-saving •  Fluid addition of terms
  19. 19. THANK YOU!

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