This is a guest post from Scott W. H. Young. Scott is Digital Initiatives Librarian and Assistant Professor at Montana State University. His research interests include user experience of the web and social media and online communities.
Altmetrics are like the line numbers of a verse play, where numerical values mark and locate dialogue. In a similar way, altmetrics index and enumerate scholarly dialogue that occurs across the web on blogs, social media posts, news reports, policy papers, and Wikipedia articles. And as with a play—where the full story is shaped through the entirety of dialogue—altmetrics can help shape a rich story of academic impact by pointing toward conversations that occur within professional communities. In this post I’ll share how I use altmetrics to locate professional conversation and tell the story of my research.
Interpretation and Application
Much has been written about the meaning and purpose of altmetrics, with fine examples of article-level metrics presented creatively in academic databases. My own understanding of altmetrics crystallized when I began to see these quantifications primarily as a measure of context. More than the final published article of record, the context highlighted by altmetrics represents an extension of the scholarly record by including conversation that occurs in response to the final published research product. The altmetrics numbers themselves hint at the quantity of conversation and provide a starting point for understanding the context that surrounds my published work. To grasp the quality of conversation, I then go beyond the numbers to read blog posts, tweets, and Facebook posts that mention and discuss my work. The dialogue surfaced through altmetrics provides value by showing me what is being said about my work, and by whom. I use these insights two ways: first, to help me see different perspectives on my own work; second, to demonstrate externally that others have read my work (no small feat in itself!), and that my work has advanced my field.
In the first instance, altmetrics allow me to understand how my work has been received and interpreted. This alone is valuable to me as a researcher. The post-publication response from my scholarly community serves as a form of peer-review which I can use to help understand my own work in different ways, and possibly shape future research direction. In the second instance, altmetrics help surface feedback that I can integrate into my promotion and tenure documentation as evidence of impact. This approach is still new and under ongoing evaluation, with signs of its growing use and acceptance. Stacy Konkiel astutely address the issue by encouraging the practice of “digging deeper” into the metrics to find rich narratives of impact that can be included in promotion and tenure review. Indeed, this practice reflects my own recent experience when last summer I prepared retention documentation for my third-year review. In the next section, I’ll highlight the tool I use to track altmetrics, and my approach for transforming quantitative metrics into qualitative stories of impact.
Examples in Practice
A variety of tools can be used to capture and measure the context that surrounds scholarly outputs. Google Analytics is another tool that I use to track in-bound links and web traffic to my professional website, where I publish conference presentations, blog posts, and research articles. I can use Google Analytics to produce a map showing the global reach of my website [Fig.1], which has receive 6,000 visits from over 100 countries during the last 3 years.
Fig. 1: Location view for site visits to scottwhyoung.com
This quantitative view—itself a form of altmetrics—is the perfect starting point for digging deeper, discovering conversational context, and crafting stories of impact. Of these thousands of aggregate visits, two social media posts in particular help illustrate the connection points between my work and my professional community.
In the fall of 2013, I published on my website a talk on the topic of User Experience and A/B Testing that I gave at the LITA Forum. Google Analytics allowed me to see the social media conversation that occurred in response to this presentation, including a Twitter post from a fellow librarian who had shared my presentation with her own colleagues [Fig. 2].
Fig. 2: Twitter post
The Twitter conversation around my A/B testing research continued into the following year, after I published an article on the same topic in a peer-reviewed journal. I continued to tune into this conversation, and discovered another example of a colleague who had applied my work to advance her practice [Fig. 3].
Fig. 3: Twitter post
These examples help me understand how my work has been interpreted in different settings, while also opening opportunities for dialogue and community growth with others in my profession. Moreover, these social media posts demonstrate a direct form of feedback, and represent evidence that my work has been read and reused elsewhere to the benefit of my profession. In this way, altmetrics help me see a wider view of the scholarly conversation, and—importantly—where my voice fits in.
In the promotion and tenure documentation that I recently prepared, I therefore included these two posts as supporting evidence of the impact of my research. I intentionally elected against including the overall number of Twitter posts that mentioned this work, as this number serves more as a signal for richer context of the posts themselves. By digging deeper into the altmetrics and going beyond the number, I found and included these specific responses, ultimately bringing into focus a compelling story of research impact.
By revealing the context that surrounds scholarly work altmetrics can contribute to a rich narrative of academic impact. The quantitative elements of altmetrics are just the beginning points for understanding the deeper qualitative insights that spring from community dialogue and scholarly conversation.