Twitter is one of the largest social networks with over 330 million active users. Therefore, by being able to better create tweets that spread further, the message of the tweet can reach more people. It is also a social platform that is widely used by news networks to share news and is the main source of news for many people. Twitter also has an API that researchers can use to easily extract data from the website. This in combination with the reasons abovehas made Twitter into a hot research topic. This study has, to the best of the knowledge of the authors, introduced a novel approach of analyzing twitter data. It has focused on tweets containing links to news articles and groups these into clusters based on the contents of said news articles. Tweets that share near identical news articles, will be grouped into clone sets, which allows to only analyze tweets that share the same content. This eliminates content as a factor that could impact the popularity and allows to better understand theunderlying factors that make a tweet popular. While only subtle differences were found in this study when controlling for content (e.g., regardless if we control for content, we found that followers, following, and whether a user was verified were the most important predictive factors), the approach provided new insights into the timing of when tweets are being posted. Tweets posted early on had a great majority of total retweets as well as the most successful tweet. While tweets posted late had a great majority of the least successful tweets. The methodology of controlling for content gave interesting insights and the authors believe it deserves further attention when doing similar research.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-178915 |
| Date | January 2021 |
| Creators | Christensson, Martin, Holmgren, William |
| Publisher | 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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