Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Who Watches (and Shares) What on YouTube? And When? Using Twitter to
  Understand YouTube Viewership   We combine user-centric Twitter data with video-centric YouTube data to
analyze who watches and shares what on YouTube. Combination of two data sets,
with 87k Twitter users, 5.6mln YouTube videos and 15mln video sharing events,
allows rich analysis going beyond what could be obtained with either of the two
data sets individually. For Twitter, we generate user features relating to
activity, interests and demographics. For YouTube, we obtain video features for
topic, popularity and polarization. These two feature sets are combined through
sharing events for YouTube URLs on Twitter. This combination is done both in a
user-, a video- and a sharing-event-centric manner. For the user-centric
analysis, we show how Twitter user features correlate both with YouTube
features and with sharing-related features. As two examples, we show urban
users are quicker to share than rural users and for some notions of "influence"
influential users on Twitter share videos with a higher number of views. For
the video-centric analysis, we find a superlinear relation between initial
Twitter shares and the final amounts of views, showing the correlated behavior
of Twitter. On user impact, we find the total amount of followers of users that
shared the video in the first week does not affect its final popularity.
However, aggregated user retweet rates serve as a better predictor for YouTube
video popularity. For the sharing-centric analysis, we reveal existence of
correlated behavior concerning the time between video creation and sharing
within certain timescales, showing the time onset for a coherent response, and
the time limit after which collective responses are extremely unlikely. We show
that response times depend on video category, revealing that Twitter sharing of
a video is highly dependent on its content. To the best of our knowledge this
is the first large-scale study combining YouTube and Twitter data.

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The categories of this paper are: q-fin
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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci
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The categories of this paper are: econ
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The categories of this paper are: chem-ph
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The categories of this paper are: supr-con
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The categories of this paper are: nlin
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph