In the past, even the very recent past, if you were involved in Research and Development of new medical technologies, be they drugs, implements or other Scientific, Technical, Engineering or Medical (STEM) developments, you were inevitably on a constant hunt for bleeding edge research materials as they were published, or cited in the scientific literature.
Seen in terms of discovery and research, both publication and citation were (and are) a form of information filtering, which allow researchers to assign a level of quality and audience engagement around research. If an item is heavily cited, one can assume, using citations as a proxy for enthusiasm, that the research has had a meaningful and powerful impression upon other researchers who also publish.
Old and slow metrics give old and slow insights
Traditionally the filters of citation or publication have worked fairly well to help those who are hunting for potential sources of innovation to hone in on new developments that have resonated loudly. There have, however, always been limitations to this method of chasing down disruptive and innovative new research. For one, the mere publication is too broad as a filter by itself, and the rise of predatory open access has devalued this currency in some regard (although the commendable push-back by the rigorous publisher community has maintained the value.)
Waiting for an article to garner citations, on the other hand, requires patience. After all, a citation accrues only after an item has been read, responded to, and that response has been authored, formatted, submitted, accepted, published and then indexed in a large citations database. This can easily take years. Innovations need to be detected in months, weeks or preferably days in order to allow for a real competitive edge when it comes to developing something new, innovative and ground-breaking.
Time is money … and the competitive advantage
More importantly, however, especially for those in the business and R&D community, attention to research found within news, the blogosphere and especially on social media platforms act as a kind of market signal. For example, this item from February 2017 is a meta-analysis of Vitamin D in the human diet and its effects on health and included articles as well as clinical trials in its dataset. The Scopus citation count sits at 8 (a very impressive number given the 4 months which have passed since its publication.) However, it’s not an eye-catchingly “high” number. Given purely this indicator, R&D researchers might be forgiven for scanning over yet another meta-analysis on Vitamins.
At the same time, these findings triggered 234 news stories across 100 different news platforms varying from “Call for Vitamin D Fortification” to “Vitamin D may protect against Cold and Flu”. These stories appeared in Mid May and Late February of this year respectively. On top of that, the article has garnered over 11,000 tweets with an upper bound audience of 2.7 million people. It has been used on Wikipedia, has been recommended twice on the Faculty of 1000 Platform, and has been shared 99 times on public Facebook walls. Interest in Vitamin D fortification, or its benefits, is extremely high, particularly when it comes to cold and flu.
I could find this item easily thanks to the fact that Altmetric’s database of research attention can be filtered by the time in which attention occurred. This article’s attention has been strong in the past 7 days. Coincidentally (or rather, not coincidentally as I would argue), in the same 7 days, an experimental vaccine for the common cold (thought always to be incurable) advanced to its first human trials in February as well. This Clinical Trial, which is still in its recruitment phase as of the time of writing, will never receive a Scopus citation since Clinical trials don’t typically qualify as citable objects, as they are themselves records and not articles.
If you’re in R&D literature scanning mode, this might be a complete blind spot if you’re relying on reading what is traditionally considered “publications”, or citations. And yet, recent attention to the clinical trial record itself is high and shows again a burgeoning and current interest in developments around the common cold. Consequently, one can infer that not only is there an increase in research interest in the Rhinovirus arena, there’s also massive public interest and a potential market waiting for an available application or product.
LifeArc: Putting the Altmetric attention database to practical use
Using Altmetric as a major early detection tool for new technologies and to the public’s interest in new areas is something which one of our clients, LifeArc (formerly MRC Technology) has been specializing in in the last few months.
We spoke to Dr. Kerstin Papenfuss, Therapeutics Review Team Leader, about the use of Altmetric as part of LifeArc’s newly established horizon scanning program.
1) Did you try and track online attention to research before you had access to the Altmetric Explorer? How did you try and do this? Did you use Google Alerts or attempt manual searching?
Kerstin: Here at LifeArc, we are excited to now be using Altmetric in our scientific horizon scanning program to identify groundbreaking science with potential for patient impact as soon as it is published. We think that social media activity is one of the ways to be alerted to ground-breaking science. It is basically a “wisdom of the crowds approach” based on the assumption that if the science has the potential to make a great impact, then people will read it, share it and talk about it.
Before signing up to Altmetric, we tried the approach manually and signed up to a variety of social media platforms, including mainstream and specialized platforms such as F1000. We found that in the short 6 weeks testing period we were already able to identify some new technologies with high potential. However, tracking all the social media activity manually is very overwhelming, labour intensive and also not very quantitative. We are looking to build a system that is ideally unbiased, quantitative and semi-automatic and have therefore been looking for an application that could deliver this in a more automated, quantitative fashion which has led us to Altmetric.
2) What timeframes do you mostly search? Last day or 3 days etc.? And do you use alerting systems or simply check every day?
Kerstin: We are still in the process of optimizing our data-mining cycles, but initial experiments have shown that the last 3 months are a reasonable time frame. This allows for the social media reaction to build up and publications are also captured in other information sources, which helps with the follow-up.
3) How does social media attention weigh in your mind as opposed to attention from mainstream news etc? Or is it more about how soon something gets traction rather than where it’s from?
Kerstin: Again, we are still in the process of figuring this out, but for our purposes, since we are looking for new technologies, we seem to get better data when biasing our system towards more specialist forums, such as F1000. That isn’t to say that new technologies do not get picked up by mainstream media, but we find that following the more specialist social media platforms rather than mainstream media reduces the noise caused by attention surrounding publications about e.g. Neanderthals or cute animals; publications like these do not get as much attention in the more specialist forums.
4) How have the score or the distribution of attention helped you when looking for immediacy?
Kerstin: For us, the Altmetric Attention Score serves a quantitative measure of excitement, and it is very useful to have the comparison with articles of the same age and same source to put these values into context. Also, the feature to be able to track attention over time is very useful, when you are evaluating the growth of a trend for example.
We wish to thank Kerstin and LifeArc for giving us an insight into the methods and considerations for early detection to research attention. We eagerly await the results of their unique use-case as they work with Altmetric attention data over the coming period!