Analysis of viral values ​​of posts on social networks

Currently, social media plays a key role in sharing information, it is important to understand how posts go viral. The content of this bachelor thesis is based on the analysis of viral values of posts on social networks Facebook and X. The methodology involves collecting data using automatic scrapers from selected social networks and their subsequent analysis. From the conclusion of these analyses, it follows that the elements of post structure that increase the tendency of a post to go viral include visual elements such as images and emoticons. Furthermore, posts with a shorter text section are more likely to create viral content. In contrast, inserting a link to an external site reduces the likelihood that the post will gain viral coverage. A factor that differently affects posts on followed social networks is the hashtag. On Facebook, hashtags have proven to be a factor that reduces the chances of a post going viral, while on X, the opposite is true. The results of this work provide a basic overview of the structure of viral content on social networks and provide recommendations for creating more viral posts and optimizing strategies to achieve wide reach and higher user engagement.