Impact of Mobility Restrictions Due to COVID-19 on TV Consumption in Spain in 2020

On March 14, 2020, a state of alarm was decreed in Spain, including restrictive mobility and economic activity measures. These restrictions caused changes in life habits, entertainment, and media consumption. The constant need for information promoted television as a trusted channel to bring current affairs closer to citizens. The interruption of programming with news flashes marked the program schedule and triggered TV consumption, especially during strict lockdown. There was a drop in television advertising investment of -18.4 % compared to 2019 and -50 % in April and May. Two reasons can explain the increase in TV consumption: the need for information on the epidemiological context and the increased time that citizens spent in their residence due to mobility restrictions. The first point can be contrasted with the significant increase in the audience of newscasts and the second with the correlation between citizens’ consumption and mobility. This analysis makes it possible to predict TV consumption based on mobility and design better media planning strategies tailored to the possible scenarios caused by COVID-19. © 2022 Universidad de La Sabana. All rights reserved.

Design of a visual attention model for communication campaigns: The case of COVID-19

Health is one of the main concerns of society. Empirical evidence underscores the growing importance of prevention and health education as a fundamental instrument to improve the quality of public health. Recent health crises, such as Ebola, influenza A, SARS, and Covid-19, have highlighted the importance of communication. When designing communication campaigns during a crisis, the speed of the creation of messages and their effectiveness have relevant social consequences. The objective of this work is to design and develop a mathematical tool, based on Machine Learning techniques, to enable predictions of areas of visual attention quickly and accurately without the use of eye-tracking technology. The methodology combines deep learning algorithms, to extract the characteristics of the images, and supervised modeling mathematical techniques, to predict the areas of attention. Validation is carried out by analyzing various institutional communications from the Covid-19 campaign, comparing the results with the areas of attention obtained using an eye-tracking solution with proven accuracy. The results obtained using the tool in the investigated Covid-19 communication pieces are analyzed, resulting in conclusions of interest for the development of new campaigns. © 2020, El Profesional de la Informacion. All rights reserved.