Stefanie Haustein Lightning talk at NISO Altmetrics Initiative meeting

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National Information Standards Organization (NISO)

Stefanie Haustein – "Exploring disciplinary differences in the use of social media in scholarly communication" Lightning talk at NISO Altmetrics Initiative meeting in San Francisco, CA October 9, 2013

Technology Health & Medicine

Exploring disciplinary differences
in the use of social media
in scholarly communication
Stefanie Haustein

Mendeley coverage and correlation with citations of PubMed/WoS
Based on 1.4 million PubMed papers published between 2010 and 2012 and covered by WoS. Mendeley readers were collected via the API. Mendeley coverage is based on all papers, correlations are
calculated for papers published in 2011, which were at least read once, to reduce biases. Specialties (NSF classification) are shown, when they contained at least 100 papers in the PubMed/WoS set and at
least 30 papers from 2011 on Mendeley. Bubble size represents average number of readers per document on Mendeley.
• coverage: 66.2%
• documents with ≥1 Mendeley
reader: 951,421
• mean number of readers per
document with ≥1 Mendeley
reader: 9.7
• correlation with citations of
P(2011)read: ρ = 0.456**

Twitter
Twitter coverage and correlation with citations of PubMed/WoS
Based on 1.4 million PubMed papers published between 2010 and 2012 and covered by WoS. Tweets were collected from Altmetric.com. Twitter coverage is based on all papers, correlations are calculated for
papers published in 2011, which were at least tweeted once, to reduce biases. Specialties (NSF classification) are shown, when they contained at least 100 papers in the PubMed/WoS set and at least 30
tweeted papers in 2011. Bubble size represents average number of tweets per tweeted document.
• coverage: 9.4%
• documents with ≥1 tweets:
134.924
• mean number of tweets per
tweeted document: 2.5
• correlation with citations of
P(2011)read: ρ = 0.157**

Twitter coverage and correlation with citations of PubMed/WoS
Based on 1.4 million PubMed papers published between 2010 and 2012 and covered by WoS. Tweets were collected from Altmetric.com. Twitter coverage is based on all papers, correlations are calculated for
papers published in 2011, which were at least tweeted once, to reduce biases. Specialties (NSF classification) are shown, when they contained at least 100 papers in the PubMed/WoS set and at least 30
tweeted papers in 2011. Bubble size represents average number of tweets per tweeted document. Correlations are significant at the 0.01 (**) or 0.05 (*) level (two-tailed).

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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.

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This study explores the readerships in Mendeley across 5 major fields of science in Leiden Ranking 2013 for a data set of 1,107,917 Web of Science (WoS) publications (reviews and articles) from all disciplines published in 2011 with DOI available. The main objective is to know if there are different patterns in terms of readership and citation impact depending on the different ‘Academic Status’ of Mendeley readers. In case of finding different pattern, this could help to introduce the possibility of considering the different users as potential predicting elements of citations. The current study is built upon the previous study of analyzing Mendeley users with focus on the types of the different Mendeley users (known users) in order to explore their patterns of saving publications in terms of subject fields, citation and readership impact. Particular attention has been paid to the extent to which the readerships of the publications saved by the different types of users in Mendeley correlate with their citation indicators and across 5 major fields of science in the Leiden Ranking (LR); also, the potential of identifying highly cited papers by different user types in Mendeley has been investigated. For this reason, we present an exploratory analysis of the patterns of reading of the different types of users in Mendeley and we study their relationship with citations and across LR fields.

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Haustein, S. (2015). Scientific Interactions and Research Evaluation: From Bibliometrics to Altmetrics Keynote at ISI2015 in Zadar, Croatia http://isi2015.de/?session=keynote-c-i Abstract. Since its creation 350 years ago, the scientific peer-reviewed journal has become the central and most important form of scholarly communication in the natural sciences and medicine. Although the digital revolution has facilitated and accelerated the publishing process by moving from print to online, it has not changed the scientific journal and scholarly communication as such. Today publications and citations in peer-reviewed journals are considered as indicators of scientific productivity and impact and used and misused in research evaluation. As scholarly communication is becoming more open and diverse and manuscripts, data, presentations and code are shared online, the altmetrics and open science movement demand the adaption of evaluation practices. Parallels are drawn between the early days of bibliometrics and current altmetrics research highlighting possibilities and limitations of various metrics and warning against adverse effects.

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Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

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1. Exploring disciplinary differences in the use of social media in scholarly communication Stefanie Haustein

2. Mendeley coverage and correlation with citations of PubMed/WoS Based on 1.4 million PubMed papers published between 2010 and 2012 and covered by WoS. Mendeley readers were collected via the API. Mendeley coverage is based on all papers, correlations are calculated for papers published in 2011, which were at least read once, to reduce biases. Specialties (NSF classification) are shown, when they contained at least 100 papers in the PubMed/WoS set and at least 30 papers from 2011 on Mendeley. Bubble size represents average number of readers per document on Mendeley. • coverage: 66.2% • documents with ≥1 Mendeley reader: 951,421 • mean number of readers per document with ≥1 Mendeley reader: 9.7 • correlation with citations of P(2011)read: ρ = 0.456**

3. Twitter Twitter coverage and correlation with citations of PubMed/WoS Based on 1.4 million PubMed papers published between 2010 and 2012 and covered by WoS. Tweets were collected from Altmetric.com. Twitter coverage is based on all papers, correlations are calculated for papers published in 2011, which were at least tweeted once, to reduce biases. Specialties (NSF classification) are shown, when they contained at least 100 papers in the PubMed/WoS set and at least 30 tweeted papers in 2011. Bubble size represents average number of tweets per tweeted document. • coverage: 9.4% • documents with ≥1 tweets: 134.924 • mean number of tweets per tweeted document: 2.5 • correlation with citations of P(2011)read: ρ = 0.157**

4. Mendeley coverage and correlation with citations of PubMed/WoS Based on 1.4 million PubMed papers published between 2010 and 2012 and covered by WoS. Mendeley readers were collected via the API. Mendeley coverage is based on all papers, correlations are calculated for papers published in 2011, which were at least read once, to reduce biases. Specialties (NSF classification) are shown, when they contained at least 100 papers in the PubMed/WoS set and at least 30 papers from 2011 on Mendeley. Bubble size represents average number of readers per document on Mendeley. Correlations are significant at the 0.01 (**) or 0.05 (*) level (two-tailed).

5. Twitter coverage and correlation with citations of PubMed/WoS Based on 1.4 million PubMed papers published between 2010 and 2012 and covered by WoS. Tweets were collected from Altmetric.com. Twitter coverage is based on all papers, correlations are calculated for papers published in 2011, which were at least tweeted once, to reduce biases. Specialties (NSF classification) are shown, when they contained at least 100 papers in the PubMed/WoS set and at least 30 tweeted papers in 2011. Bubble size represents average number of tweets per tweeted document. Correlations are significant at the 0.01 (**) or 0.05 (*) level (two-tailed).

Stefanie Haustein Lightning talk at NISO Altmetrics Initiative meeting

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Stefanie Haustein Lightning talk at NISO Altmetrics Initiative meeting