Article Level Metrics

PLoS is doing some interesting with social networking and articles.  They provide “article level metrics” on all of the articles published within their titles.  Article level metrics refers to the data they are collecting on each article that can be used to help researchers to determine the value of the article within the scientific community.  So what kind of data are they collecting?  It includes citation information, online usage, social bookmarks, comments, notes, blog posts about the article, and ratings.  More information about how each of these data pieces are collected and used can be found at PLoS.

What I find most interesting is the online social networking tools they look at and use to help determine the impact of an article.  While much of this type of information is out there on the Internet, I think it is often overlooked.  News spreads rapidly through the social networking webverse.  There are countless reports where a breaking news story such as the plane landing  on the Hudson river was on Twitter before traditional news sources.  Twitter, blogs, and social bookmarks are naturally going to be quicker than traditional communication methods employed within research (letters to the editor, editorials, articles, etc.). 

I am not implying that an article that has a ton of Twitter chatter is on the same par as one that has been cited in many many research journals.  But I do think the Twitter chatter, the blog posts, and the bookmarks are an important indicator of what people think of an article in the present time.  This information should not be ignored.  This information along with traditional metrics data provide a more complete overall picture of the article.   People can would be able to see an article’s immediate impact on the scientific web community as well as the article’s long term impact.  

According to PLoS their metrics data is openly available for researchers to analyze.  The entire dataset for all article level metrics are available as an Excel file that is updated periodically.  What would be interesting is to take this data and see whether there is a correlation between online interest (blog posts, tweets, and bookmarks) and traditional research metrics that measure an article’s impact on the scientific community.  Do articles that get a lot of social media attention also generate a lot of attention in the long run with authors citing it or building the research upon it?

Interesting how social networking is wiggling its way into things we never thought it would.

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