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

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)

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

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

“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

“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

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?

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

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

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

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

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

Results Individuals (n=542)

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

Results Organizations (n=165)

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

for your attention! Thank you Rodrigo Costas @RodrigoCostas1