Third-party Link Shortener Usage on Twitter
Martin Lindblom, Oscar Jarpehult, Mathilda Mostrom, Alexander Edberg, Niklas Carlsson
Paper:
Martin Lindblom, Oscar Jarpehult, Mathilda Mostrom, Alexander Edberg, Niklas Carlsson,
"Third-party Link Shortener Usage on Twitter",
Proc. IFIP Network Traffic Measurement and Analysis Conference (TMA),
Sept. 2021.
(pdf,
extended)
Abstract:
Twitter has proven a powerful tool to shape peoples' opinions and thoughts.
One efficient way to spread information and misinformation is with the use of links.
In this paper, we characterize the link sharing usage on Twitter, placing particular
focus on third-party link shortener services that hide the full URL from the user.
First, we present a measurement framework that combines two Twitter APIs and the Bitly API,
and allows us to collect detailed statistics about tweets, their posters, their link usage,
and the retweets and clicks 24 hours after the original tweet.
Second, using two one-week-long datasets, collected one year apart
(April 2019 and 2020), we then characterize and analyze important
difference in link usage among users, the domains that different
users and shorteners (re)direct users too, and compare the click
rates of such links with the corresponding retweet rates.
The analysis provides insights into link sharing biases on Twitter,
skews, and behavioral differences in usage, as well as reveal interesting
observations capturing differences in how a tweet containing a link may be
retweeted versus how the embedded link is clicked. Our observations have
implications on how easily fake news and other misinformation can spread.
Software and datasets
To help build upon our work, below, we make available code and example datasets.
-
Sotware:
The software and code used in our paper is made available
here
for use by the wider research community.
(The file contains commented source codes and a
README file which should help in getting started with the files.)
-
Datasets:
Add a click-based example datasets here (with some tweet info) + a fuller sanitized dataset ...
with description similar as for ASONAM paper ...
and hopefully some more dataset other (description in extended version)
Note: If you use our datafiles or code in your research,
please include a reference to our TMA 2021 paper
(pdf) in your work.