Analysing Public Engagement with Science on Twitter

Victoria Uren

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.

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

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

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

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

Based on Brookes, B.C. Nature 232, 458-461, 1971.

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.

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

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)

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)

Results – Modified Queries

Space AND grail @18 lies within the expected variance of the population

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)!

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.

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?

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.

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

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

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

Curiosity 4-12 Aug. 2012

Curiosity 4-12 Aug. 2013

This is NOT a line graph!

Landing Day dwarfs other lines

Phosphorus 4-12 Aug. 2012

Phosphorus 4-12 Aug. 2013

Permafrost 4-12 Aug. 2012

Permafrost 4-12 Aug. 2013

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

Where Next?

• Socially important science (volcanos, bioenergy)• More on Aging Factor (event detection)

THANK YOU!