Work-in-progress presentation at Social Media & Society 2015 Haustein, S., Bowman, T.D. & Costas, R. (2015). Communities of attention' around journal papers: Who is tweeting about scientific publications? https://socialmediaandsociety.com/ http://smsociety15.sched.org/event/91e44f025248a9f40e64302c12ce567d/edit#.VbfKfBNViko Background: ‘Altmetrics’ have been introduced as a way to capture scientific output and impact beyond papers and citations based on traces on various social media platforms (Priem, Taraborelli, Groth, & Neylon, 2010), of which Twitter is believed to have a particular potential to reflect societal impact of research. The analysis and application of various altmetrics such as tweets to scientific papers, however, still lack adequate interpretative frameworks mainly because the processes behind the metrics are not yet fully understood. Currently each tweet is counted equally on platforms such as Altmetric.com or ImpactStory and studies tend to ignore user type and tweet content, although tweets have been shown to range from serious discussions to humour and self-promotion to automated mentions (Haustein et al., 2015). Objective: Communities of attention around scientific publications on Twitter are identified based on engagement and exposure of users. Engagement is measured as the degree to which the tweet text differs from the title of the tweeted paper. Exposure refers to the potential audience of the tweet as measured by the number of the user’s followers. Methods: Publications from 2012 covered by Web of Science were matched to tweets (until June 2014, excluding retweets) recorded by Altmetric.com via DOI resulting in 660,149 tweets, 237,222 tweeted papers, and 125,083 Twitter users. Engagement was calculated based on the dissimilarity between the tweet text (excluding user names and URLs) and the title of the tweeted document. User data (including the number of followers representing exposure) was collected from Altmetric.com and the Twitter API. Four user categories were defined, classifying users into four quadrants A, B, C and D according to engagement and exposure values above and below the median of the whole dataset (Figure 1). Statistics based on the tweeting behaviour of users were calculated for each of the categories. The connections between 708 users with more than 100 publications based on co-mentions of the same papers were visualized in a network graph in Figure 1.   Results: Users in the four categories differ according to tweeting behavior (Figure 1). Users in A have the highest mean tweets per day (based on all tweets) and those in D tweet more about scientific papers (typical for bots identified by Haustein et al. (2015)), while users in A and B discuss publications with slightly higher relative citation rates.

1. Communities of attention
around journal papers
Who is tweeting about scientific
publications?
Stefanie Haustein, Timothy D. Bowman & Rodrigo Costas
@stefhaustein @timothydbowman @RodrigoCostas1

2. Outline
• Introduction
Bibliometrics and altmetrics
• Background
Twitter in scholarly communication
• Research Questions and Objectives
• Methods
• Preliminary Results
• Outlook

3. Introduction: altmetrics
• alternative use and visibility of publications
on social media:
more traditional forms of use:
• alternative forms of research output
“study and use of scholarly impact measures based on activity in
online tools and environments”
“a good idea but a bad name”
…
…
…
Priem (2014, p. 266)
Rousseau & Ye (2013, p. 3289)

4. Introduction: bibliometrics

5. Introduction: altmetrics

6. Background
• social media activity around scholarly articles grows
5% to 10% per month
• Mendeley and Twitter largest sources for mentions of
scholarly documents
• Twitter
• used by ca. 10% of researchers in a professional context
• 22% of Web of Science journal papers published in 2012
• number of tweets per paper highly skewed
• low correlations with citations
• popularity of humorous and curious topics
• automated diffusion of scientific papers on Twitter
Adie & Roe (2013)
Costas, Zahedi & Wouters (2015)
Haustein, Costas & Larivière (2015)
Haustein, Costas & Larivière (2015)
Rowlands, Nicholas, Russell, Canty, & Watkinson (2011)

7. Research questions and objectives
• What information about tweeting behavior can be
used to distinguish different kinds of Twitter impact of
journal articles?
• Who is diffusing scientific journal articles on Twitter?
• What user groups can be distinguished:
• regarding engagement with papers and
• potential reach and audiences of users?
• How does the tweeting behavior of these user groups
differ?

8. Research questions and objectives
• distinguishing between:
exposure = number of followers
engagement = dissimilarity between tweet and paper title

9. Methods
• 660,149 original tweets
• 237,222 tweeted documents
• 125,083 unique users
• number of tweets to 2012 papers
• mean tweets per day
• mean relative citation rate of tweeted papers
• mean engagement
• mean exposure
• mean number of followers
• mean number of following
• tweeted document coupling user network

10. Methods
exposure
engagement influencers /
brokers
orators /
discussing
disseminators /
mumblers
broadcasters

11. Preliminary results
mean tweets to papers
tp = 5.3
exposure
engagement
tp = 3.2tp = 1.7
tp = 11.5tp = 4.4

12. Preliminary results
mean tweets per day
tpd = 5.9
exposure
engagement
tpd = 10.1tpd = 1.8
tpd = 9.4tpd = 1.7

13. Preliminary results
mean relative citation rate
mncs = 2.3
exposure
engagement
mncs = 2.4mncs = 2.5
mncs = 2.1mncs = 2.2

14. Preliminary results
9 57
130 512
708 of 125,083 users (0.6%) tweeting
WoS papers published in 2012 (>100)
Node size represents number of papers
Network of users tweeting the same papers

15. Preliminary results
708 of 125,083 users (0.6%) tweeting
WoS papers published in 2012 (>100)
Node size represents number of papers
Network of users tweeting the same papers

16. Preliminary results

17. Preliminary results

18. Preliminary results

19. Preliminary results

20. Outlook
• Systematic analysis of users in different groups
• Identifying particular differences in tweeting behavior
• Differentiating between various types of impacts of
journal articles on Twitter
• Investigating the motivation behind tweeting scientific
papers
 Improving scholarly metrics

21. Thank you
for your attention!
Stefanie Haustein, Timothy D. Bowman & Rodrigo Costas
@stefhaustein @timothydbowman @RodrigoCostas1
http://www.slideshare.net/StefanieHaustein