  Citations acknowledge the impact a scientific publication has on subsequent
work. At the same time, deciding how and when to cite a paper, is also heavily
influenced by social factors. In this work, we conduct an empirical analysis
based on a dataset of 2010-2012 global publications in chemical engineering. We
use social network analysis and text mining to measure publication attributes
and understand which variables can better help predicting their future success.
Controlling for intrinsic quality of a publication and for the number of
authors in the byline, we are able to predict scholarly impact of a paper in
terms of citations received six years after publication with almost 80 percent
accuracy. Results suggest that, all other things being equal, it is better to
co-publish with rotating co-authors and write the papers' abstract using more
positive words, and a more complex, thus more informative, language.
Publications that result from the collaboration of different social groups also
attract more citations.
