Computational Intelligence - August 2016 - 11
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pricing of the spot ads and program sponsorship are based on
the way the TV audience is measured. There is no doubt that
traditional TV ratings still rank as an important measurement
that advertisers pay attention to when buying advertising
inventory, however, it is possible that in the upcoming years
social TV data will shift attention away from traditional audience ratings.
Traditionally, measures about people's habits and reactions
are gathered in two ways: firstly, by viewing habits of panels of
TV viewers and parsing the results of network surveys on the
opinion (e.g. the Nielsen ratings); secondly, by generating traditional live broadcast audience figures with the so-called set
meters (small devices connected to TVs in a small number of
selected homes) on a daily basis. However, this approach misses
the explosive growth and increasing diversity of comments and
opinions in real time from an expanding number of online
social platforms. In the typical offline scenario, audience profiles
are obtained manually by gathering a set of predefined sociodemographics characteristics obtained from a statistically significant sample of the possible consumers. By contrast, in an online
scenario, audience profiles and impact of TV programs might be
obtained by tracking social media sites (e.g. Twitter, Facebook)
and applying Natural Language Processing (NLP) technologies
and data mining techniques on their contents. This might
enable TV reprogramming and media planning strategies, such
as contextual advertisement or behavioral targeting.
In this paper, we observe that, although different social media
are characterized by different users' activity styles, they all carry
useful information (i.e., non-redundant with respect to each
other). While Twitter activities have the peculiarity of being very
timely and immediate-usually users tweet in real time, while
watching the program they are commenting about-a good
portion of the activities on YouTube and Facebook happens with
some time shift with respect to the on-air show. Users post fragments of videos, which potentially trigger comments and discussions for days, in some cases even weeks or months. Thus, we
propose a concept-level integration framework in which users'
activities on different social media are collectively represented by
means of conceptual abstractions, possibly enriched with external knowledge, such as information extracted from the EPGs, or
available ontological domain knowledge.
The framework has a knowledge graph as its core data
model, which keeps track of active users, the television events
they talk about, the concepts they mention in their activities, as
well as different relationships existing among them, including
temporal relationships which enable temporal analysis of the
observed activity. The data model allows different types of analysis and the definition of global metrics in which the activity
on different media concurs with the measure of success.
Note that, although we concentrate our cross-media analysis
on the study of the popularity of topics tackled in television programs, we do believe that the concept-level integration platform
has the property of being very general. As such, it has the potential
of being populated and enriched with information of interest in
different domains (such as tracking political dynamics, tracking the
correlation between users' social activities and economic patterns).
Thus, in the rest of the paper, we first define the general concept,
and then show how they are instantiated in the TV domain.
The paper is organized as follows: after presenting a survey of
related literature in Section II, we introduce our integration framework in Section III. We formally define the graph integration
AUGUST 2016 | IEEE Computational intelligence magazine
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Table of Contents for the Digital Edition of Computational Intelligence - August 2016
Computational Intelligence - August 2016 - Cover1
Computational Intelligence - August 2016 - Cover2
Computational Intelligence - August 2016 - 1
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