How much research shared on Facebook is hidden from public view? A comparison of public and private online activity around PLOS ONE papers

Authors : Asura Enkhbayar, Stefanie Haustein, Germana Barata, Juan Pablo Alperin

Despite its undisputed position as the biggest social media platform, Facebook has never entered the main stage of altmetrics research. In this study, we argue that the lack of attention by altmetrics researchers is not due to a lack of relevant activity on the platform, but because of the challenges in collecting Facebook data have been limited to activity that takes place in a select group of public pages and groups.

We present a new method of collecting shares, reactions, and comments across the platform-including private timelines-and use it to gather data for all articles published between 2015 to 2017 in the journal PLOS ONE.

We compare the gathered data with altmetrics collected and aggregated by Altmetric. The results show that 58.7% of papers shared on the platform happen outside of public view and that, when collecting all shares, the volume of activity approximates patterns of engagement previously only observed for Twitter.

Both results suggest that the role and impact of Facebook as a medium for science and scholarly communication has been underestimated. Furthermore, they emphasise the importance of openness and transparency around the collection and aggregation of altmetrics.

URL : https://arxiv.org/abs/1909.01476

Science and Facebook: the same popularity law!

Authors : Zoltán Néda, Levente Varga, Tamás S. Biró

The distribution of scientific citations for publications selected with different rules (author, topic, institution, country, journal, etc.) collapse on a single curve if one plots the citations relative to their mean value.

We find that the distribution of shares for the Facebook posts re-scale in the same manner to the very same curve with scientific citations. This finding suggests that citations are subjected to the same growth mechanism with Facebook popularity measures, being influenced by a statistically similar social environment and selection mechanism.

In a simple master-equation approach the exponential growth of the number of publications and a preferential selection mechanism leads to a Tsallis-Pareto distribution offering an excellent description for the observed statistics.

Based on our model and on the data derived from PubMed we predict that according to the present trend the average citations per scientific publications exponentially relaxes to about 4.

URL : https://arxiv.org/abs/1701.05347