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Abstract
User-generated video content sites such as Youtube have become extremely popular.
Understanding video online popularity is of great value for network and service providers,
marketing industries, entertainment businesses and content creators.
In this thesis, we develop novel frameworks to evaluate
the impacts of different content-agnostic factors on
Youtube videos popularity
and use the resulting insights to study and model their
popularity growth patterns.
The first significant subject of our thesis is developing
and applying a novel 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.
We analyze a number of phenomena that may contribute
to rich-get-richer, including the first-mover advantage,
and search bias towards popular videos.
Our findings also confirm that inaccurate conclusions
can be reached when not controlling for video content.
The second central topic of our research is
performing a characterization and modeling of the videos popularity dynamics
using only the total view count for analysis.
We develop a framework
for studying the popularity dynamics of user-generated videos,
present a characterization of the popularity dynamics,
and propose a model that captures the key properties of these dynamics.
Using a dataset that tracks the views to a sample of recently-uploaded
Youtube videos over the first eight months of their lifetime, we study
the popularity dynamics.
We find that the relative popularities of the videos within our
dataset are highly non-stationary,
owing primarily to large differences in the required
time since upload until peak popularity is finally achieved, and secondly to popularity oscillation.
We propose a model that can accurately capture the popularity
dynamics of collections of recently-uploaded videos as they age.
Another important aspect of our research is illustrating the biases that may
be introduced in the analysis for some choices of the sampling technique
used for collecting data.