Video dissemination through sites such as YouTube can have widespread impacts on opinions, thoughts, and cultures. Not all videos will reach the same popularity and have the same impact. Popularity differences arise not only because of differences in video content, but also because of other "content-agnostic" factors. The latter factors are of considerable interest but it has been difficult to accurately study them. For example, videos uploaded by users with large social networks may tend to be more popular because they tend to have more interesting content, not because social network size has a substantial direct impact on popularity. In this paper, we develop and apply a methodology that is able to accurately assess, both qualitatively and quantitatively, the impacts of various content-agnostic factors on video popularity. When controlling for video content, we observe a strong linear "rich-get-richer" behavior, with the total number of previous views as the most important factor except for very young videos. The second most important factor is found to be video age. We analyze a number of phenomena that may contribute to rich-get-richer, including the first-mover advantage, and search bias towards popular videos. For young videos we find that factors other than the total number of previous views, such as uploader characteristics and number of keywords, become relatively more important. Our findings also confirm that inaccurate conclusions can be reached when not controlling for content.

We propose EMO, a model to evaluate delay tolerant networks (DTN) and opportunistic systems, which focuses on simulating encounter events between mobile radios, rather than node locations as done in existing models and simulators. Our approach introduces a more accurate simulation of DTNs on the main system timescale (the encounter timescale), while trading off some accuracy at the bit-level, through an abstraction of radio propagation simulation. To design EMO, we extract and characterize the necessary parameters from experimental data and propose a method to generate synthetic node encounter traces based on this characterization. The output of the model is validated using hold-out cross-validation method. Our validation results indicate that EMO is able to maintain the statistical properties of experimental data over a wide range of time (simulation duration) and space (number of nodes) scales, with mean square errors of less than 3% for the main system parameters.