As so called fake news spread widely on the internet it is important to examine how they are spread, and thereby, how much of a problem they are. This thesis investigates how the spread of news articles on Twitter can be represented via a tree structure, as well as whether or not the trees have patterns that correlate to attributes such as the source of the shared news article and how many followers the original tweeter has. As part of the study a tool was built in Python 2.7 that, amongst other things, allows tracking and reconstruction of a news article's propagation on Twitter.It could be concluded that most links that are shared on Twitter propagate over a period of a few days and most retweets are made within the first twelve hours. We observe patterns suggesting that having more followers correlates to getting more retweets. Users who have few followers have to rely on their tweets being retweeted in a longer chain of users for it to reach a larger audience. Tweets that have a substantial spread often spread widely, but not especially deep. Finally, our results suggest that both the news site that created the article and the content of the article has an impact on how much it is retweeted.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-148681 |
| Date | January 2018 |
| Creators | Lundström, Linnea, Ragnarsson, Sebastian |
| 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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