Tracking the digital footprints to scholarly articles: the fast accumulation and rapid decay of social media referrals
Academicsare increasingly encouraged to share their scholarly articles via
social media, as part of a wider drive to maximize their dissemination
and engagement. But what effect does this have? Xianwen Wang
has studied the referral data of academic papers, with particular focus
on social media referrals and how these change over time. Referrals
from social media do indeed account for a significant number of visits
to articles, especially in the days immediately following publication.
But this fast initial accumulation soon gives way to a rapid decay.
PeerJ,
an open access, peer reviewed scholarly journal, provides data on the
referral source of visitors to all of its article pages. This is quite
unique as such data is not available on other publisher or journal
websites. These metrics are updated on a daily basis following an
article’s publication, meaning for the first time we are able to track
the digital footprints to scholarly articles and explore people’s
visiting patterns.
an open access, peer reviewed scholarly journal, provides data on the
referral source of visitors to all of its article pages. This is quite
unique as such data is not available on other publisher or journal
websites. These metrics are updated on a daily basis following an
article’s publication, meaning for the first time we are able to track
the digital footprints to scholarly articles and explore people’s
visiting patterns.
In our previous study examining referral data collected from PeerJ,
social network platforms were proven to be among the top referral
sources. Social media directs many visitors to scholarly articles. In
our more recent study, we used the daily updated referral data of 110 PeerJ articles collected over 90 days (22 January – 20 April 2016) to track the temporal trend of visits directed by social media.
social network platforms were proven to be among the top referral
sources. Social media directs many visitors to scholarly articles. In
our more recent study, we used the daily updated referral data of 110 PeerJ articles collected over 90 days (22 January – 20 April 2016) to track the temporal trend of visits directed by social media.
Image credit: 20070912-16 by Matt Binns. This work is licensed under a CC BY 2.0 license.
Twitter and Facebook account for most social media referrals
During our observation period, 19 February
was the first day on which all 110 sample articles had visiting data,
with 20 April being the last day of the research period and the point at
which all papers in our sample had been published for at least 60 days.
According to the findings of our study, article visits directed by
social referrals account for more than 12% of all visits (as shown in
Figure 1). Twitter and Facebook are the two most important social
referrals directing people to scholarly articles; between them
accounting for more than 95% of all social referrals. Individually
Twitter and Facebook were roughly equivalent to one another, each
falling within the 42-54% range.
was the first day on which all 110 sample articles had visiting data,
with 20 April being the last day of the research period and the point at
which all papers in our sample had been published for at least 60 days.
According to the findings of our study, article visits directed by
social referrals account for more than 12% of all visits (as shown in
Figure 1). Twitter and Facebook are the two most important social
referrals directing people to scholarly articles; between them
accounting for more than 95% of all social referrals. Individually
Twitter and Facebook were roughly equivalent to one another, each
falling within the 42-54% range.
Figure 1: The proportion of article visits from social referrals on two specific days. Source: Wang et al, (2016). Tracking the digital footprints to scholarly articles from social media, Scientometrics. © Akadémiai Kiadó and republished here with permission.
Attention from social media: “easy come, easy go”
To track temporal trends in what
percentages of total visits to articles could be accounted for by social
media referrals, the daily visiting data of each article were grouped
according to the publish–harvest interval days (the number of days from
publication to data being recorded). The visiting dynamics analysis
(Figure 2) shows an obvious overall downward temporal trend in the
proportion of all visits originating from social media. Where papers had
been published for just one day, social referrals accounted for 20% of
all visits. After 90 days, this percentage falls to only 9%.
percentages of total visits to articles could be accounted for by social
media referrals, the daily visiting data of each article were grouped
according to the publish–harvest interval days (the number of days from
publication to data being recorded). The visiting dynamics analysis
(Figure 2) shows an obvious overall downward temporal trend in the
proportion of all visits originating from social media. Where papers had
been published for just one day, social referrals accounted for 20% of
all visits. After 90 days, this percentage falls to only 9%.
Overall, during the initial period
following a scholarly article’s publication, social attention comes very
quickly. In most cases, visits from social media are much faster to
accumulate than visits from other referrals, with most of those visits
directed by social referrals being concentrated in the few days
immediately following publication. About 77% of the visits from social
media are generated in the first week after publication. However – “easy
come, easy go” – social buzz around scholarly articles doesn’t last
long, leading to a rapid decay in the article visits from social
referrals.
following a scholarly article’s publication, social attention comes very
quickly. In most cases, visits from social media are much faster to
accumulate than visits from other referrals, with most of those visits
directed by social referrals being concentrated in the few days
immediately following publication. About 77% of the visits from social
media are generated in the first week after publication. However – “easy
come, easy go” – social buzz around scholarly articles doesn’t last
long, leading to a rapid decay in the article visits from social
referrals.
Figure 2: Temporal trend of the proportion of visits from social media in the total visits. Source: Wang et al, (2016). Tracking the digital footprints to scholarly articles from social media, Scientometrics. © Akadémiai Kiadó and republished here with permission.
The role of social buzz in directing
people to scholarly articles can be illustrated by a specific example.
As shown in Figure 2, a small but noticeable increase occurs at the
middle part of the curve. We reviewed the data and discovered that this
small burst is attributable to a jump in visits from Twitter to paper 1605.
Paper 1605 was published on 2 February 2016. To 6 March, the number of
article visitors directed by Twitter had reached 381. On 7 March, a
particularly influential Twitter account
(with 1.97 million followers) tweeted about the paper. That tweet was
retweeted 11 times on the same day and is the reason the number of
article visitors from Twitter rose dramatically from 381 to 751 in only a
few days.
people to scholarly articles can be illustrated by a specific example.
As shown in Figure 2, a small but noticeable increase occurs at the
middle part of the curve. We reviewed the data and discovered that this
small burst is attributable to a jump in visits from Twitter to paper 1605.
Paper 1605 was published on 2 February 2016. To 6 March, the number of
article visitors directed by Twitter had reached 381. On 7 March, a
particularly influential Twitter account
(with 1.97 million followers) tweeted about the paper. That tweet was
retweeted 11 times on the same day and is the reason the number of
article visitors from Twitter rose dramatically from 381 to 751 in only a
few days.
The fluctuation visible towards the end of
the curve is caused by the vast decrease in the number of samples with
sufficiently long time windows (in number of days since publication).
the curve is caused by the vast decrease in the number of samples with
sufficiently long time windows (in number of days since publication).
Synchronism between the number of tweets and article visitors from Twitter
Figure 3: Synchronism of temporal trend of tweets and their procured visits for the paper 1605. Source: Wang et al, (2016). Tracking the digital footprints to scholarly articles from social media, Scientometrics. © Akadémiai Kiadó and republished here with permission.
The synchronism of the growth in the
number of tweets and that in article visitors from Twitter testifies
partially that social mentions do direct people to read scholarly
articles, although we don’t know who is directed by which tweet. Article
visitors from social referrals may be researchers, students, or even
the general public. However, it does prove that the public attention on
social media can be transformed into the real clicks on scholarly
articles.
number of tweets and that in article visitors from Twitter testifies
partially that social mentions do direct people to read scholarly
articles, although we don’t know who is directed by which tweet. Article
visitors from social referrals may be researchers, students, or even
the general public. However, it does prove that the public attention on
social media can be transformed into the real clicks on scholarly
articles.
This blog post is based on the author’s co-written article, ‘Tracking the digital footprints to scholarly articles from social media’, published in Scientometrics (DOI: 10.1007/s11192-016-2086-z).
Note: This article gives the views of
the author, and not the position of the LSE Impact Blog, nor of the
London School of Economics. Please review our comments policy if you have any concerns on posting a comment below.
the author, and not the position of the LSE Impact Blog, nor of the
London School of Economics. Please review our comments policy if you have any concerns on posting a comment below.
About the author
Xianwen Wang is a Professor at WISE Lab, Dalian University of Technology in China and an Associate Editor of Frontiers in Research Metrics and Analytics. His ORCID iD is 0000-0002-7236-9267.
Impact of Social Sciences – Tracking the digital footprints to scholarly articles: the fast accumulation and rapid decay of social media referrals
No comments:
Post a Comment