1. Identifying Twitter user communities
in the context of altmetrics
Stefanie Haustein, Andrew Tsou, Vanessa Minik, Delaney Brinson, Erica Hayes, Rodrigo Costas, & Cassidy R. Sugimoto

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
(e.g., Haustein, Costas, & Larivière, 2015)
(e.g., Rowlands et al. 2011; van Noorden, 2014)
(Priem & Costello, 2010)
*based on Altmetric.com data 06/2015
Alperin, 2015:
University affiliated: 64%
unspecified: 16%
employed: 24%
student: 23%
Not university affiliated: 36%
(Tsou, Bowman, Ghazinejad, & Sugimoto, 2010)
(Alperin, 2015)

3. engagement exposure Background brokers orators mumblers broadcasters
tweet text differs from paper title
exposure
engagement
median dissimilarity with paper title
median number of followers
brokers
orators
mumblers
broadcasters
tweet text is identical to paper title
few followers
many followers

4. 3 2 1 Background Network of 325 most frequent terms academic
Node size
number of accounts associated with term
Node color
cluster affiliation
topics and collectives
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.
personal

5. “Broadcasters” Terms Background High exposure Low engagement
Science and research
Organizational focus
News
accounts with a high exposure but low engagement (broadcasters) focus on cluster two (organizational)
Accounts with organizational descriptions seemed to have disseminative role

6. “Orators” Terms Background Low exposure High engagement
Scientists and students
Personal preferences
accounts classified as B (orators, discussing) use mostly terms from the personal and academic clusters
Accounts with academic or personal terms exhibit higher engagement

7. Who tweets about science?
Research Questions
Who tweets about science?
Can the four-quadrant hypothesis be validated through qualitative coding of Twitter accounts?
Which user groups can be identified?
To what extent are Twitter accounts automated?
Overarching question: Who tweets about science?

8. Methods 663,547 original tweets to WoS 2012 papers via Altmetric.com
89,768 users with English account settings
Stratified random sample of 200 Twitter accounts per quadrant:
“brokers” (high engagement & high exposure)
“orators” (high engagement & low exposure)
“broadcasters” (low engagement & high exposure)
“mumblers” (low engagement & low exposure)
4 coders

9. Methods Codebook What is the account status? Is the Twitter handle an:
Public
Private
Suspended
Is the Twitter handle an:
Individual
Organization
Is the organization:
Corporation
Government
Journal Publisher
College/University
Library
K-12
Non-English
Does not exist
Scientific society/association
Other nonprofit
Other Research-center
Other
Unable to tell

10. Methods Codebook What is the gender of the individual:
Female
Male
Does the individual indicate that they are a:
Student
Researcher
Professional
Based on the tweets and Twitter bio, does the Twitter handle appear:
Not automated
Mixed
Does the bio link to a separate Twitter handle(s)?
Does the bio link to a separate website(s)?
Other
Unable to tell
Science communicator
Personal characteristics
Completely automated
Unable to tell

11. Results Type of Twitter account
Majority of accounts run by individuals
About one fifth are organizations
For 12% it is impossibe to identify the user
Comparing the account types per quadrant (followers and title-tweet similarity), there are fewer organizations among Orators and more among Broadcasters, which confirms initial assumptions about these user types

12. Results Automation of tweets
Judging automation based on the tweets and account descriptions, for 42% of accounts it is impossible to tell if they are maintained by human or bot activity
45% appear not to be automated
8% seem fully automated and would thus be classified as bots
5% of accounts seem to be fed partly by automated, partly by original human content
Comparing quadrants, it is striking to notice that automation is significantly higher among Mumblers, where we expected most bot account: 13% of accounts seemed completely automated and 7% partially automated

13. Results
Individuals (n=542)

14. Results Individuals (n=542)
Most users could be identified as professors
Followed by researchers
Science communicoators were overrepresented among Brokers and Broadcasters
Students were overrepresented among Orators

15. Results
Organizations (n=165)

16. Conclusion
Although time-consuming, 88% of English Twitter users tweeting academic papers can be identified
68% individual users
21% organization
Scientific societies are most common organizations
Majority of individual users are professionals and researchers
13% of accounts seem at least partially automated
Four-quadrant classification reveals some differences between user groups but not sufficient to classify user types

17. for your attention!
Thank you
Rodrigo Costas
@RodrigoCostas1