Identification of emotions triggered by different sourced stimuli can be applied to automatic systems that help, relieve or protect vulnerable groups of population. The selection of the best stimuli allows to train these artificial intelligence-based systems in a more efficient and precise manner in order to discern different risky situations, characterized either by panic or fear emotions, in a clear and accurate way. The presented research study has produced a dataset of audiovisual stimuli (UC3M4Safety database) that triggers a complete range of emotions, with a high level of agreement and with a discrete emotional categorization, as well as quantitative categorization in the Pleasure-Arousal-Dominance Affective space. This database is adequate for the machine learning algorithms contained in these automatic systems. Furthermore, this work analyses the effects of gender in the emotion elicitation under audiovisual stimuli, which can help to better design the final solution. Particularly, the focus is set on emotional responses to audiovisual stimuli reproducing situations experienced by women, such as gender-based violence. A statistical study of gender differences in emotional response was carried out on 1332 participants (811 women and 521 men). The average responses per video is around 84 (SD = 22). Data analysis was carried out with RStudio®.

Gender stereotypes influence boys’ and girls’ self-perception, with the differential treatment received by sports figures in the media being one of the main factors in the perpetuation of stereotypes about sports. The objective of this research is to analyze if the new communication channels, such as Twitter, maintain gender stereotypes when reporting sports news. For this purpose, the four most followed media in Spain were analyzed: (@ElPais_Deportes, @ABC_Deportes, @Marca and @MundoDeportivo) over a period of five months, from March to June 2016. Our sample was composed of 6544 tweets, with 96.19% about sportsmen compared to 3.81% that portrayed women. The sport with the most media coverage was football (72.11%), for men as well as for women, followed by basketball (6.63%). It is clear that despite the growing international triumphs of Spanish women athletes in recent years, the latter continue to be underrepresented in the media. Female athletes receive more media coverage according to the sport which they engage in (“gender-appropriate” sports), with the exception of football, and not in accordance with their accomplishments. Twitter remains at the service of traditional media replicating the same gender biases and even augmenting them.

YouTubers are idols, and influence millions of people who follow their channels every day. These new media stars, born in transmedia, create their own content outside the major corporations. Across the millions of YouTube channels, Lesbian, Gay, Bisexual, Transsexual, Intersexual and Queer (LGBTIQ or LGBT+) YouTubers are gaining more young followers who identify with their content and, also, are able to have direct contact with these YouTube "stars". The objective of this research is to explore and analyse the content of the YouTube channels of the most popular LGBT+ YouTubers among Spanish young people and to assess whether there are gender biases in their content. The results show that content created by these YouTubers is more diverse than mainstream channels, with content ranging from personal stories to bullying. In spite of this, these channels continue to reproduce some gender stereotypes.