Ragebait och algoritmisk spridning av missinformation : Effekter på användarengagemang och samhällelig polarisering

Sammanfattning

During recent years ragebait has become a more common phenomenon. The goal with ragebait posts is to create some kind of enragement, where the content of the posts can contain anything: true or false information. Therefore, this work aims to find out how engagement increases in posts, on Tiktok and X, created to provoke enragement versus neutral on the subject of immigration. As well as how the content distorts truths and publishes despite references to misinformation and disinformation. Through a quantitative content analysis, a coding scheme has been created using theories to develop a system for determining whether a post should be interpreted as enragement or neutral. Two coders used the coding scheme to decide whether a video should be coded as enragement and neutral. The test conducted was a hypothesis test of two proportions; engagement variables are obtained to see which type of content receives the most interaction. Results showed that people were more likely to like and share enraged videos, and neutral videos had more comments, where the content of the comments showed higher polarization. Meanwhile the comments in the enraged created a togetherness. The content on X was solely coded as enragement.