Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Vu17072014

582 views

Published on

Use of metrics and visualization to look at public science communication on Twitter

Published in: Internet
  • Be the first to comment

  • Be the first to like this

Vu17072014

  1. 1. Analysing Public Engagement with Science on Twitter Victoria Uren
  2. 2. The Team & the Papers (so far) • Under construction: Uren, V., Dadzie, A.-S., Framing public scientific communication on Twitter: a visual analytic approach. • 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. Science engagement Scientists Public(s) one way – public understanding of science, outreach, media, science literacy one way – consultation two way – public participation, social media Information Flows
  4. 4. Why look at science discussion on Twitter? • Public engagement with science matters: • Enthuse kids to learn science, • Inform people about fascinating stuff, • Build consensus for social and economic change, • The public paid for the research • Social media present a great opportunity to “talk nerdy” to the public (on.ted.com/Marshall) • Twitter particularly • Low barriers to entry • Expert and non expert participants • Contributions on any topic • BUT typically low levels of tweeting about science
  5. 5. METRICS – AGEING FACTOR 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
  6. 6. Aging Factor Where: i is the cut-off time in hours, k is the number of retweets originating at least i hours ago, l is the number of retweets originating less than i hours ago, k + l is therefore all the tweets in the sample If I = 1 simple ratio AF = k k +l i Based on Brookes, B.C. Nature 232, 458-461, 1971.
  7. 7. Assumptions • Aging Factor • Provides a snapshot of retweeting rate for tweets containing particular terms • Assumes an exponential decay in the rate of retweeting • Does NOT require the original tweets to be in the dataset • Assumption 1: ageing factors for topics which concern special events will be lower than suitable baselines. • Assumption 2: ageing factors which are higher than suitable baselines are associated with topics in which interest is sustained over time.
  8. 8. Meteor Showers – coming to a sky near you! • Debris from comets stream to earth on parallel paths • Quadrantid 3 Jan 2012 • At the same time • Grail spacecraft moved into Moon orbit 2nd of Jan • Moon & Jupiter close and aligned vertically 2nd Jan Images from Wikipedia
  9. 9. Dataset • 24h 3 January 2012 • Filtered on UNESCO Thesaurus ‘Astronomical terms’ subheading (excluding ‘Time’), containing 32 terms. • Total of tweets 408,800 • Total retweets 83,993 • 12,513 containing ‘space’ • 82,611 containing ‘earth|moon|sun|stars|universe|space’ (abbreviated as Astro) • Divided into quarter days (labeled 6, 12, 18, 24)
  10. 10. Subsets – Query & Negation Search label Terms Space AND grail Space AND (nasa|soyuz|satellite|spaceflight|orbit|hubble |telescope|spacecraft|voyager) AND (grail|lunar|moon) Space NOT grail Space AND (nasa|soyuz|satellite|spaceflight|orbit|hubble |telescope|spacecraft|voyager) AND NOT (grail|lunar|moon) Space AND jupiter Space AND (interstellar|black hole|comet|moon|geminid) AND (planet|mercury|venus| mars|jupiter|saturn|neptune|uranus|pluto) AND (jupiter AND moon) Space NOT jupiter Space AND (interstellar|black hole|comet|moon|geminid) AND (planet|mercury|venus| mars|jupiter|saturn|neptune|uranus|pluto) AND NOT (jupiter AND moon) Astro AND quad (Earth|moon|sun|stars|universe|space) AND (quadrantid|meteor shower) Astro NOT quad (Earth|moon|sun|stars|universe|space) AND NOT (quadrantid|meteor shower)
  11. 11. Results – Modified Queries Space AND grail @18 lies within the expected variance of the population
  12. 12. Results – 3 “Interesting” Sets • 2 Astro AND quad points • @18 0.15 182, @24 0.22 330 • Inference: retweeting activity around the Quadrantid meteor shower was significant in the hours of darkness for the UK and USA • 1 Space NOT grail • @6 0.71 274 • 216 of the retweets contained the phrase “join NASA” • “Oh really? You need space? You might as well join NASA.” • Inference: this is a funny joke (apparently)!
  13. 13. VISUALIZATION •Under construction: Uren, V., Dadzie, A.-S., Framing public scientific communication on Twitter: a visual analytic approach. •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.
  14. 14. Research Questions Is it possible to observe dynamic changes to the framing of science communication in non-trending topics on Twitter? Can changes be observed across disconnected time frames (within days and in samples taken a year apart)? Can visualisation provide further information in addition to confirming the content analysis?
  15. 15. 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.
  16. 16. Content Analysis 1/2 • 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
  17. 17. Content Analysis 2/2 More use of ‘curiosity’ in general sense in 2013 Periodic table jokes trending in 2013 Shift of framing from ELSI to Political around ‘white phosphorus’ Siberian Hairdresser Record permafrost melt in 2013 Celebration & cat jokes in 2012
  18. 18. 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
  19. 19. Curiosity 4-12 Aug. 2012 Curiosity 4-12 Aug. 2013 Landing Day dwarfs other lines
  20. 20. Phosphorus 4-12 Aug. 2012 Phosphorus 4-12 Aug. 2013
  21. 21. Permafrost 4-12 Aug. 2012 Permafrost 4-12 Aug. 2013
  22. 22. Conclusions Is it possible to observe dynamic changes to the framing of science communication in non-trending topics on Twitter? Yes – for reasonably populated topics Can changes be observed across disconnected time frames (within days and in samples taken a year apart)? Yes – with appropriate normalisation the parallel coordinates produce comparable polycurves Can visualisation provide further information in addition to confirming the content analysis? Yes – allows us to be more fine grained, more explorative
  23. 23. Where Next? • Socially important science (volcanos, bioenergy) • More on Aging Factor (event detection)
  24. 24. THANK YOU!

×