Practical Applications of Altmetrics

This post is written by Fereshteh Didegah from the Scholarly Communications Lab at Simon Fraser University.

The first talk of the afternoon session was about leveraging altmetrics as opportunity indicators by David Sommer. He talked about a model of opportunity developed by Kudos including three levels of Opportunity, Attention, and Effectiveness. Opportunity level includes potentials, audiences and reach. The attention level is about being aware, seeing the relevance, and taking action. He added that they look at data providers to find the attention. However, they may miss some places of attention like researchgate.net. For effectiveness, they look at the citation counts, downloads and readership rate which they retrieve from Scopus, Crossref, Counter, etc. 

After mentioning a quotation from a librarian saying that “Weighing a pig doesn’t make it any fatter”, he emphasized that Kudos is about fattening the pig rather than only giving it a weight.

He talked about Kudos research performance framework which is mainly about doing the research and then communicating the research. He continued with talking about Kudos custom recommendations comprising Reach, Resonance, and Results levels. The Reach level measures the size of audience. The Resonance measures the attention actions are generating, and the Results is about measuring how outputs are performing.

 

The second talk by Sacha Noukhovitch was about using examples of high Altmetric Attention Scores as a roadmap for pre-/post-publication editorial support. He talked about scholarly publisher’s success and that it is important to understand the increasing online actions occurring in the social networks about the scholarly publications. He believes that the altmetric score could be used by publishers in order to know on which online platforms they should focus more and publish their products to get a higher traction.

There are papers that the feedback they get from the online platforms are more significant. Hence, the academic assessment control has started drifting from publishers and editors feedback towards online attention and assessment. The open and collaborative forms of peer review are increasing on some platforms such as GitHub or computational science platform.

He continues that, anyway, a high altmetric score is a roadmap which can help to gain a higher online attention to research publications. He emphasized that the pre-/post-publication social network activity should be more focused and this needs more editorial support to plan and implementing these functions. He adds that scholarly publishers and editors should engage in digital media marketing more and consider the feedback and reviews from the online community. Moreover, the editors should engage in starting conversations over social networks around their publications.

The third talk was “Can altmetrics data help researchers fine-tune their publication strategies?” by Camilla Lindelöw. Camilla started explaining three issues that she experienced about altmetric data as a librarian: Altmetric data as a discussion source, a group thing and a chasing shadow. She showed a graph of dissertations in different languages with their library holdings over time. What could be seen from the graph clearly was the dominance of English dissertations and that some of them had a maximum of 125 library holdings. The Swedish dissertations have less spread. She added an example of a PhD student here who did not like to do her thesis in Swedish.

With regards to the issue of “a group thing”, she pointed out the researcher’s response and the validity of individual metrics for others but the researchers themselves.

And regarding the issue of avoiding the shadow, she asked the question of how to express the uncertainty of data and answered that we should emphasize the exploratory focus and also how to deal with heterogeneity. Her answer is that we need to be clear about the target groups, data sources and visible and less visible biases. Finally, she stated the question: can altmetric data help researchers fine-tune their publication strategies? She suggested that we need to have fruitful discussions, testing and development and that researchers should find their way.
The final talk by Melanie Cassidy and Ali Versluis was about “Who’s talking about you? Using altmetrics in library assessment”. They talked about their project in which they used the altmetric data to assess their library program named PAWS based on the mentions, comments, shares, likes and any other feedback from students at the university of Guelph on Twitter, Facebook, Instagram, etc.

The project had 7 steps: what to assess, identifying the service, where to look, collecting the data, analyzing the data, using the data, and preserving and sharing the data. They considered some points such as user behavior, and purpose of data collection and if the user is satisfied with preserving, backing up or sharing the data (like on google drive, Microsoft one drive, dropbox, or local shared network drive, institutional repository). The also ran a sentiment analysis on the online comments and feedback from students.