Abstract

Public service media (PSM) are in crisis throughout Europe. As a scenario for research, the two extremes of the background have been chosen: the first, BBC, is at the heart of media system, has become a model for the others and has just finished the particularly delicate challenge of renewal the fundamental law that is to rule over the next ten years; the second is on the periphery of public television, and in the extreme circumstances in Spain that have driven some of the television corporations to extinction, such as Canal 9 -Radiotelevisió Valenciana (RTVV). Using a method based on the use of semi-structured interviews and Delphi, the conception of these media models among their respective audiences is explored. 

Abstract

Public television has a mission to promote debate around topics of public interest. Twitter is the social metamedia used by TV programs to fulfil this task online. This research analyzes the capability of public television to generate an effective debate through Twitter and also looks at the relationship between physical audience and social audience. We apply a quantitative methodology that employs our own capture and treatment technique to examine the activity generated by the hashtags proposed by six television programs on BBC One, TV3, and La 1. The main finding shows that these programs fail in their mission to generate a robust public debate in Twitter; instead the discussion is generated by a recurrent group of users that control the debate flow around their comments. Finally, the physical audience is not a predictive factor of the social audience.

Abstract

Today’s TV context is defined by new services like view-on-demand, “extra” content, and conversations with the audience. The public has more power to decide what to view and can influence the programming with their comments. Because of this, TV channels have created strategies to improve the online products and encourage public participation. Success also depends on social TV and VoD. The objective of this study was to analyze how Spanish prime time has adapted to new media and how programs are managing interactions with the audience. In order to do the analysis, the three main TV groups –RTVE, Atresmedia, Mediaset- were chosen and all the programs were categorized into three genres. The results revealed a weakness in the interactivity strategy of television programs, especially in the informative genres.

Abstract

The results of a project on news segmentation at Radio Nacional de España (RNE) carried out by the RTVE Technological Innovation and Media Management areas is presented. The aim of this project is to apply artificial intelligence to automatically transcribe and cut the news items that make up a radio news program. The main goals of this project are to increase the accessibility of the content and to allow its reusability on various platforms and social media. The project was planned in two phases, covering system configuration and service delivery. The minimum quality criteria required were defined in advance, both for automatic voice transcription and for news segmentation. For the speech-to-text process, the highest word error rate (WER) allowed was 10%, while the precision rate for the news segmentation was 85%. System performance in both transcription and segmentation was considered to be sufficient, although a higher degree of accuracy in news cutting is expected in the coming months. The results show that, despite using these quite mature technologies, adjustment and learning processes and human intervention are still necessary.