Stefanie Haustein and Rodrigo Costas@stefhaustein @RodrigoCostas1

Identifying Twitter audiencesWho is tweeting about scientific papers?

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

*based on Altmetric.com data 06/2015

(e.g., Haustein, Costas, & Larivière, 2015)

(e.g., Rowlands et al. 2011; van Noorden, 2014)

(Priem & Costello, 2010)

(Tsou, Bowman, Ghazinejad, & Sugimoto, 2010)

(Alperin, 2015)

Research motivation and objective

• Identifying Twitter user types and engagement related to

scientific papers

• Distinguishing user groups based on:

• Twitter account descriptions

• Number of followers

• Level of engagement with paper

Methods

• 1.3 million papers published in WoS papers in 2012

• 663,547 original tweets (no RTs) as captured by

Altmetric.com until July 2014 linked to papers via DOI

• Twitter profile information for 115,053 handles via Twitter

API in April 2015

• Reduction to 89,768 users with English account settings

• Account description

• Number of followers = exposure

• Dissimmilarity with paper title = engagement

exposure

en

ga

ge

men

tinfluencers /

brokers

orators /

discussing

disseminators /

mumblers

broadcasters

Results

Methods

• Noun phrases extraction with VOSviewer part-of-speech

tagger based on 80,939 account descriptions

• 185,824 unique terms extracted from 78,991 accounts

• Visualization and clustering of co-occurrence network of

325 most frequent terms (≥100)

• Identification of 3 clustersClustering resolution = 0.9, minimum cluster size = 5

• Calculation per term:

Number of Twitter accounts associated with term

Average exposure of accounts associated with term

Average engagement of accounts associated with term

Identification of predominant quadrant of term

1

2

3

Network of 325 most frequent terms

Node size

number of accounts

associated with term

Node color

cluster affiliation

topics and

collectives

academic

personal

Results

low high

Node color

average engagement of

accounts associated

with term

Node size

average exposure of

accounts associated

with term

Co-occurrence network of frequent terms

topics and

collectives

academic

personal

1

2

3

Results

Results

Users

• High exposure

• Low engagement

Terms

• Science and

research

• Organizational

focus

• News

Results

Users

• Low exposure

• High engagement

Terms

• Scientists and

students

• Personal

preferences

Results

Conclusions

• Scientific papers are tweeted by

• Individuals who identify professionally, personally or both

• Organizations or interest groups

• Accounts with organizational descriptions seemed to

have disseminative role

• Accounts with academic or personal terms exhibit higher

engagement

Limitations and Outlook

• VOSviewer noun phrase extraction

• limited to English language

• not optimized for Twitter account descriptions

• Uncontrolled, uncleaned vocabulary

• Reduction to top terms

• No systematic analysis of terms

Qualitative coding of accounts

Systematic identification of keywords associated with

account types

Testing of four-quadrant hypothesis (engagement↔exposure)

Testing of other user characteristics

Stefanie Haustein and Rodrigo Costas@stefhaustein @RodrigoCostas1

Thank you for your attention!

Tuesday, 11/10

1:30pm

Grand D