test text sumy point bug

Testing text / paper abstract
taken from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10505

Viral videos have become a staple of the social Web. The
term refers to videos that are uploaded to video sharing sites
such as YouTube, Vimeo, or Blip.tv and more or less quickly
gain the attention of millions of people.
Viral videos mainly contain humorous content such as
bloopers in television shows (eg. boom goes the dynamite)
or quirky Web productions (eg. nyan cat). Others show extraordinary events caught on video (eg. battle at Kruger)
or contain political messages (eg. kony 2012). The arguably
most prominent example, however, is the music video Gangnam style
by PSY which, as of January 2015, has been
viewed over 2 billion times on YouTube. Yet, while the recent surge in viral videos has been attributed to the availability of affordable digital cameras and video sharing sites
(Grossman 2006), viral Web videos predate modern social media. An example is the dancing baby which appeared in
1996 and was mainly shared via email.
The fact that videos became Internet phenomena already
before the first video sharing sites appeared suggests that
collective attention to viral videos may spread in form of a
contact process. Put differently, it seems reasonable to surmise that attention to viral videos spreads through the Web
very much as viruses spread through the world. Indeed, the
times series shown in Fig. 1 support this intuition. They
show exemplary developments of YouTube view counts and
Google searches related to recent viral videos and closely
resemble the progress of infection counts often observed in
epidemic outbreaks. However, although viral videos attract
growing research efforts, the suitability of the viral metaphor
was apparently not studied systematically yet. In this paper,
we therefore ask to what extend the dynamics in Fig. 1 can
be explained in terms of the dynamics of epidemics?
This question extends existing viral video research which,
so far, can be distinguished into two broad categories: On
the one hand, researchers especially in the humanities and
in marketing, ask for what it is that draws attention to viral
videos (Burgess 2008; Southgate, Westoby, and Page 2010).
In a recent study, Shifman (2012) looked at attributes common to viral videos and, based on a corpus of 30 prominent
examples, identified six predominant features, namely: focus on ordinary people, flawed masculinity, humor, simplicity, repetitiveness, and whimsical content. However, while
he argues that these attributes mark a video as incomplete
or flawed and therefore invoke further attention or creative
dialogue, the presence of these key signifiers does not im-
ply virality. After all, there are millions of videos that show
these attributes but never attract significant viewership
Another popular line of research, especially among data
scientists, therefore consists in analyzing viewing patterns of
viral videos. For instance, Figueiredo et al. (2011) found that
the temporal dynamics of view counts of YouTube videos
seem to depend on whether or not the material is copyrighted. While copyrighted videos (typically music videos)
were observed to reach peak popularity early in their lifetime, other viral videos had been available for quite some
time  before  they  experienced  sudden  significant  bursts  in
popularity. In addition, the authors observed that these bursts
depended  on  external  factors  such  as  being  listed  on  the
YouTube front page. The importance of external effects for
the viral success of a video was also noted by Broxton et al. (2013) who found that viewership patterns of YouTube
videos strongly depend on referrals from sites such as Face-
book or Twitter. In particular, they observed that ‘social’
videos with many outside referrals rise to and fall from peak
popularity much quicker than ‘less social’ ones.
Sudden bursts in view counts seem to be suitable predictors of a video’s future popularity (Crane and Sornette 2008;
Pinto, Almeida, and Goncalves 2013; Jiang et al. 2014). In
fact, it appears that initial view count statistics combined
with additional information as to, say, video related sharing
activities in other social media, allow for predicting whether
or not a video will ’go viral’ soon (Shamma et al. 2011; Jain, Manweiler, and Choudhury 2014). Yet, Broxton et
al. (2013) point out that not all ‘social’ videos go viral and
not all viral videos are indeed ‘social’.
Given this interest in video related time series analysis,
it is surprising that the viral metaphor has not been scrutinized from this angle. To the best of our knowledge, the most
closely related work is found in a recent report by CintroArias (2014) who attempted to match an intricate infectious
disease model to view count data for the video Gangnam Style. We, too, investigate the attention dynamics of viral
videos from the point of view of mathematical epidemiology and present results based on a data set of more than 800
time series. Our contributions are of theoretical and empirical nature, namely:
1)
we introduce a simple yet expressive probabilistic
model of the dynamics of epidemics; in contrast to traditional approaches, our model admits a closed form expression for the evolution of infected counts and we show that it
amounts to the convolution of two geometric distributions
2)
we introduce a time continuous characterization of this
result; major advantages of this continuous model are that
it is analytically tractable and allows for the use of highly
robust maximum likelihood techniques in model fitting as
well as for easily interpretable results
3)
we fit our model to YouTube view count data and
Google Trends time series which reflect collective attention
to prominent viral videos and find it to fit well.
Our work therefore constitutes a data scientific approach
towards viral video research. However, it is model- rather
than data driven. This way, we follow arguments brought
forth, for instance, by Bauckhage et al. (2013) or Lazer et
al. (2014) who criticized the lack of interpretability and the
‘big data hubris’ of purely data driven approaches for their
potential of over-fitting and misleading results.
Our presentation proceeds as follows: Next, we review
concepts from mathematical epidemiology, briefly discuss
approaches based on systems of differential equations, and
introduce the probabilistic model that forms the basis for our
study; mathematical details behind this model are deferred
to the Appendix. Then, we present the data we analyzed and
discuss our empirical results. We conclude by summarizing
our approach, results, and implications of our findings.