Celeste Kidd and Steven Piantadosi had sued the university over its handling of sexual harassment allegations made against colleague Florian Jaeger.
A new analysis finds is no link between a researcher’s citations and Twitter mentions of her scientific research.
December 11, 2013|
WIKIMEDIA, ALTONWhen Journal of Medical Internet Research publisher Gunther Eysenbach showed in 2011 that “tweets can predict highly cited articles within the first three days of article publication,” his work was greeted with fanfare, criticism, and—you guessed it—a flood of activity on Twitter. Now, a new analysis of tweets and citations has drawn a different conclusion, showing that of 15 highly tweeted articles published in a three-year period, only four had accrued more than a dozen citations. Another four garnered zero mentions in the literature.
The new work, led by the University of Montreal’s Stefanie Haustein, was published in the Journal of the American Society for Information Science and Technology last month (November 26).
In an interview with The Chronicle of Higher Education, Haustein said that, although she did not find a link between tweets and citations, that doesn’t mean social media posts about science are not impactful in some way. “People don’t tweet for the scientific content, but because it’s interesting,” she told The Chronicle. In other words, members of the public might be tweeting about science for its informative or entertainment value more so than its scholarly impact.
“The most popular scientific articles on Twitter stress health implications or have a humorous or surprising component,” Haustein said in a statement.
September 16, 2014
Haustein and colleagues unexplicably did not include JMIR, Biomed Central, or PloS journals in their analysis (all electronic journals which have article IDs instead of printed page numbers were not included due to a mistake in their data analysis). Secondly, they analysed their data differently - they looked at a linear correlation across all journals rather than dichotmizing their data as I did in my JMIR analysis. My approach was to use the most highly tweeted articles on a journal-level to predict the most highly cited articles in a specific journal. Haustein ignored the effect of the journal as a confounder and did not do a "per journal" analysis. See http://gunther-eysenbach.blogspot.ca/2013/12/on-jasist-haustein-paper-on-tweets-and.htmlOn the JASIST Haustein paper on tweets and citations
for a full critique of the Haustein paper.