Social networks attract millions of users who want to share information and connect with people. One of those platforms are Twitter,which has the power to greatly shape people's opinions and thoughts. It is therefore important to understand how information is shared among users. In this thesis, we characterize the link sharing usage on Twitter, placing particular focus on third-party link shortener services that hide the actual URL from the users until the users click on a generic, shortened URL, focusing mainly on the link management platform Bitly. The purpose of this thesis is to analyze link usage among users over a specific time period, the domains that different users and link shortens direct their users to and compare the click rates of such links with the corresponding retweet rates to see how this vary over time. We use a measurement framework that is developed by two other students from Linköping University to collect datasets over different time periods. First, we will compare a one-week-long dataset from the spring of 2019 to one that is gathered one year later in the spring of 2020. Two additional one-week-long datasets has also been collected during the spring of 2020. We use the two main datasets, separated by a year, to evaluate long-term differences, and the three datasets from the spring of 2020 to analyze shorter-term variations in the link usage. The study highlights with this approach is to be able to highlight significant patterns over time, including with regard to what domains that are tweeted. We have found that the usage of URL link shorterns has not decreased over the last year, though the usage of specifically Bitly has done so. The top domains with highest occurrences from 2019 did not get to keep their high rankings in 2020, this is especially true for facebook.com whose occurrence has dropped by 2.7 percentage points in 2020. Our conclusion is that the difference between the years is not huge but that there are some interesting trends and patterns. Given the prevailing pandemic Covid-19, we have also chosen to do a minor analysis of how many users of Twitter link to domains related to this. It turned out that the link sharing of Covid-19 related substances decreased quite sharply during our analysis period.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-168629 |
| Date | January 2020 |
| Creators | Moström, Mathilda, Edberg, Alexander |
| Publisher | Linköpings universitet, Institutionen för datavetenskap, Linköpings universitet, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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