The paper focuses on the problem of new digital generation’s participation
in the media consumption process and first of all in television watching under
conditions when the contemporary television audience transforms due to the
emergence of mobile digital technologies. The digital generation is the most vivid
segment of the society in terms of diverse interests and active media consumption;
it possesses new selection opportunities and influences the elder generation.
This article aims to define the digital generation’s role in contemporary media
processes; identify its current functions and current attitude to traditional media,
particularly television, as well as Russian telecontent.
Methods of researching the media audience also change. The main object of
mediametry measurements is now the process, not the result of media consumption.
In practice, however, the audience is still viewed as a homogenous mass, not a
dynamic system. That is why “mass” calculations cannot be used to judge specific
audience needs. It is important not simply to measure views but also to study the
audience, taking into account the content and formats of media texts consumed by
it within the telecommunication process.
The research applies the expert survey method within homogenous groups of young
people and focuses on qualitative properties of media consumption, specifically its
motivation structure and audience needs. Results of three expert surveys conducted
among 17–27 years old journalism students of the Ural Federal University at various
times are presented. The motives of telecontent consumption are defined.
The paper reveals that the new digital generation relies on the telecontent posted
on various online platforms. The youth have a critical attitude towards broadcast
television not only because of competition from the new media but also due to low
quality of professional media products. Additionally, representatives of the young
media audience participate in mass communication processes not only as consumers
but also as creators of their own video content.
Unsupervised Detection of Abnormal Electricity Consumption Behavior Based on Feature Engineering
