  Understanding and predicting the popularity of online items is an important
open problem in social media analysis. Considerable progress has been made
recently in data-driven predictions, and in linking popularity to external
promotions. However, the existing methods typically focus on a single source of
external influence, whereas for many types of online content such as YouTube
videos or news articles, attention is driven by multiple heterogeneous sources
simultaneously - e.g. microblogs or traditional media coverage. Here, we
propose RNN-MAS, a recurrent neural network for modeling asynchronous streams.
It is a sequence generator that connects multiple streams of different
granularity via joint inference. We show RNN-MAS not only to outperform the
current state-of-the-art Youtube popularity prediction system by 17%, but also
to capture complex dynamics, such as seasonal trends of unseen influence. We
define two new metrics: promotion score quantifies the gain in popularity from
one unit of promotion for a Youtube video; the loudness level captures the
effects of a particular user tweeting about the video. We use the loudness
level to compare the effects of a video being promoted by a single
highly-followed user (in the top 1% most followed users) against being promoted
by a group of mid-followed users. We find that results depend on the type of
content being promoted: superusers are more successful in promoting Howto and
Gaming videos, whereas the cohort of regular users are more influential for
Activism videos. This work provides more accurate and explainable popularity
predictions, as well as computational tools for content producers and marketers
to allocate resources for promotion campaigns.
