The stability of Twitter metrics: A study on unavailable Twitter mentions of scientific publications

Authors : Zhichao Fang, Jonathan Dudek, Rodrigo Costas

This paper investigates the stability of Twitter counts of scientific publications over time. For this, we conducted an analysis of the availability statuses of over 2.6 million Twitter mentions received by the 1,154 most tweeted scientific publications recorded by this http URL up to October 2017.

Results show that of the Twitter mentions for these highly tweeted publications, about 14.3% have become unavailable by April 2019. Deletion of tweets by users is the main reason for unavailability, followed by suspension and protection of Twitter user accounts.

This study proposes two measures for describing the Twitter dissemination structures of publications: Degree of Originality (i.e., the proportion of original tweets received by a paper) and Degree of Concentration (i.e., the degree to which retweets concentrate on a single original tweet).

Twitter metrics of publications with relatively low Degree of Originality and relatively high Degree of Concentration are observed to be at greater risk of becoming unstable due to the potential disappearance of their Twitter mentions.

In light of these results, we emphasize the importance of paying attention to the potential risk of unstable Twitter counts, and the significance of identifying the different Twitter dissemination structures when studying the Twitter metrics of scientific publications.

URL : https://arxiv.org/abs/2001.07491

Tracking the Digital Footprints to Scholarly Articles from Social Media

Authors : Xianwen Wang, Zhichao Fang, Xinhui Guo

Scholarly articles are discussed and shared on social media, which generates altmetrics. On the opposite side, what is the impact of social media on the dissemination of scholarly articles and how to measure it? What are the visiting patterns?

Investigating these issues, the purpose of this study is to seek a solution to fill the research gap, specifically, to explore the dynamic visiting patterns directed by social media, and examine the effects of social buzz on the article visits.

Using the unique real referral data of 110 scholarly articles, which are daily updated in a 90-day period, this paper proposes a novel method to make analysis. We find that visits from social media are fast to accumulate but decay rapidly.

Twitter and Facebook are the two most important social referrals that directing people to scholarly articles, the two are about the same and account for over 95% of the total social referral directed visits.

There is synchronism between tweets and tweets resulted visits. Social media and open access are playing important roles in disseminating scholarly articles and promoting public understanding science, which are confirmed quantitatively for the first time with real data in this study.

URL : http://arxiv.org/abs/1608.00798