Twitter Users with Access to Academic Library Services Request Health Sciences Literature through Social Media
Bibliographic record
Abstract
A Review of:
 Swab, M., & Romme, K. (2016). Scholarly sharing via Twitter: #icanhazpdf requests for health sciences literature. Journal of the Canadian Health Libraries Association, 37(1), 6-11. http://dx.doi.org/10.5596/c16-009
 
 Abstract
 
 Objective – To analyze article sharing requests for health sciences literature on Twitter, received through the #icanhazpdf protocol.
 
 Design – Social media content analysis.
 
 Setting – Twitter.
 
 Subjects – 302 tweets requesting health sciences articles with the #icanhazpdf tag.
 
 Methods – The authors used a subscription service called RowFeeder to collect public tweets posted with the hashtag #icanhazpdf between February and April 2015. Rowfeeder recorded the Twitter user name, location, date and time, URL, and content of the tweet. The authors excluded all retweets and then each reviewed one of two sets. They recorded the geographic region and affiliation of the requestor, whether the tweet was a request or comment, type of material requested, how the item was identified, and if the subject of the request was health or non-health. Health requests were further classified using the Scopus subject category of the journal. A journal could be classified with more than one category. Any uncertainties during the coding process were resolved by both authors reviewing the tweet and reaching a consensus.
 
 Main results – After excluding all the retweets and comments, 1079 tweets were coded as heath or non-health related. A final set of 302 health related requests were further analyzed. Almost all the requests were for journal articles (99%, n=300). The highest-ranking subject was medicine (64.9%, n=196), and the lowest was dentistry (0.3%, n=1). The most common article identifier was a link to the publisher’s website (50%, n=152), followed by a link to the PubMed record (22%, n=67). Articles were also identified by citation information (11%, n=32), DOI (5%, n=14), a direct request to an individual (3%, n=9), another method (2%, n=6), or multiple identifiers (7%, n=22). The majority of requests originated from the UK and Ireland (29.1%, n=88), the United States (26.5%, n=80), and the rest of Europe (19.2%, n=58. Many requests came from people with affiliations to an academic institution (45%, n=136). These included librarians (3.3%, n=10), students (13.6%, n=41), and academics (28.1%, n=85). When tweets of unknown affiliation were excluded (n=117), over 70% of the requests were from people with academic links. Other requesters included journalists, clinicians, non-profit organisations, patients, and industry employees. The authors examined comments in the tweets to gain some understanding of the reasons for seeking articles through #icanhazpdf, although this was not the primary focus of their study. A preliminary examination of the comments suggested that users value the ease, convenience, and the ability to connect with other researchers that social media offers.
 
 Conclusion – The authors concluded that the number of requests for health sciences literature through this channel is modest, but health librarians should be aware of #icanhazpdf as another method through which their users might seek to obtain articles. The authors recommend further research into the reasons why users sometimes choose social media over the library to obtain articles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.508 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".