Negative citations – the ‘dislike’ button in science

 
 

Science builds on science by citing previous work. Highly cited work is then often regarded as reliable or impactful. However, you are possibly citing a study because it was actually NOT the way to go. Looking at the citation, is it a thumbs up? Good study, we found something similar. Or is it a thumbs down? We describe completely the opposite. In other words, could the scientific system benefit from implementing negative citations? And could the social world of Facebook benefit from a similar thing: finally implementing the ‘dislike’ button?

The impact of a publication, a researcher or a journal is often measured by the number of citations. These values are taken as a proxy for the quality and relevance of the published work. Importantly, these values can make and break scientific careers. These statistics, however, don’t distinguish positive from negative. Even in the most complex bibliometric analyses one citation is one citation. As the world is filled with both positive and negative signals, perhaps a scientific ‘dislike’ could improve feedback and thus aid in, for example, identifying controversial publications.

A recent study by Alexander Oettl and his colleagues (1) investigated whether scientists actually cite in a negative manner by examining their language use. Using a computer algorithm to categorize over 760.000 citations from 16.000 articles published in the Journal of Immunology, they found that only 2.4% of all these citations were negative and few (7.1%) of the 150.000 cited papers received negative citations at all. Interestingly, highly cited papers tended to have more negative references. So one could say bad or minor work is often ignored, while the most impactful articles are more thoroughly discussed and questioned – they receive more citations, including more negative ones.

One could even take a step further and add more substantive data on the context of a citation.

Besides providing a valence judgment (positive or negative), more metadata could be attached to each citation. What is the reason for referring to this study? In which of the four sections of an article (introduction, methods, results and discussion) is the reference located? This additional information could be time-saving for a researcher: for example it could show that the results of a certain study might have been invalid, but the methods were solid and repeatable.

This idea of metadata is exactly what Facebook implemented last month (February 2016). How does Facebook metadata work? Say your friend posts a devastating message of his breakup. Do you happily click the ‘like’ button? Probably not, as you’d prefer to express something more nuanced. With six new emoji you can now quickly respond to the post with emotions such as shock, sadness or anger. Or, if you thought this breakup was all for the best, perhaps you can react with love, happiness or simple laughter.

So Facebook has enabled more diverse options than the harsh ‘dislike’ button. Science is luckily no social media and among the million differences between them there might be one of optimism. Apparently scientists rarely cite adversely and a special positivity prevails, with journals such as Current Opinion in Neurobiology, for example, requesting  that authors highlight their references for special attention. Accompanied by a brief explanation of relevance, they spell out each citation they ‘superlike’ (one star) or ‘supersuperlike’ (two stars).

So far, the low incidence of negative citations might suggest that scientists are less concerned with disliking. At least the call for negative citations in science is outweighed by the cry for a ‘dislike’ button on Facebook, a demand that has finally been answered. Scientists, on the other hand, seem better off not being bothered with such emoji, although they definitely love and laugh at each other’s studies.

Bibliography:
1. Positive: Catalini C, et al. (2015) The incidence and role of negative citations in science. Proc Natl Acad Sci 112(45):13823–13826.

 


 

MatthijsOurde-01

Matthijs oude Lohuis is a master’s student in the Systems Neuroscience lab, Champalimaud Neuroscience Programme. He has written a popular science book about the most fascinating topics in neuroscience and has never cited negatively.

 


 

Edited by: Andres Laan (page editor), Tiago Marques (section editor), Clara Howcroft Ferreira (editor-in-chief)

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