Social networks are collections of users who influence each other through their generated content. Viral or word-of-mouth advertising, which relies on user influence, is one of the best marketing strategies applied on social networks. As influence affects sales, businesses try to identify influential users and encourage them to create positive influence. Different models have been proposed for detecting those influential to maximize the spread of influence in social networks, but most yield inaccurate results in different networks with different semantics of links, specifically in open social networks. Therefore, to have a practical result on the spread of influence in open social networks, we conducted a propagation analysis on YouTube by crawling one of its friendship networks and one of its subscribers (followers) networks. Our study is unique because it investigates the two main types of connections (i.e., friends and followers) within the same environment and interaction features. We observed that the effect on propagation of people who are not either in a friendship network or a subscription network is higher than that of friends or subscribers. Meanwhile, we found that even though the network of subscribers was denser than the network of friends, the magnitude of propagation in the subscription network was less than in the friendship network. We also noticed a low correlation between the popularity of content and its propagation in general, with a greater correlation in subscription networks than that in friendship networks.