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This article explores the mediating role of social identity in the relationship between emotions—both positive and negative—and participation in collective action. The study sample included university students from Peru (N = 599; M = 24.5 years) and Chile (N = 592; M = 22.2 years). A structural equation modeling (SEM) approach was used to analyze the data. The findings reveal that, in both countries, social identity mediated the relationship between emotions and collective action, and positive emotions were the key drivers of students’ inclination to engage in collective action. The study concludes that emotions predict collective action through identification with a social movement—in this case, the student movement. Furthermore, the indirect effect of social identity was stronger among Chilean students compared to their Peruvian counterparts.

The media-based communication between social movements and other actors is relevant to achieve their goals. This article addresses the dynamic and contentious aspects of the media-based communication generated by the Magallanes Social Movement and the Government of Chile. A quantitative analysis was made of 217 news and 499 quotations published during the events. The results indicate that the parties adopted differing communication strategies like making calls for action, talking positively about the own group, among others. Furthermore, the content of communications varied over time, but the strong relation between the communications issued and the occurrence of particular protest events suggests that while a communicational contest is certainly an interactive process, both camps do not just argue through the words, but also through the events that each camp organizes.

Understanding human emotions and behavior in closed environments is essential for creating more empathetic and humane spaces. Environmental factors, such as temperature, noise, and light, play a crucial role in influencing behavior, but individuals' emotional states are equally important and often go unnoticed. Artificial Intelligence of Behavior (AIoB) offers a novel approach that integrates environmental measurements with human emotions to create spatially adaptive processes that can influence behavior. In this article, we present a new human emotion sensor developed using video cameras and implemented on a System on Chip (SoC) development board. Our approach uses Convolutional Neural Networks (CNNs) to recognize the presence of emotions in enclosed spaces and generate parameters that can influence emotional states and behavior within an AIoB system. The research successfully integrates advanced CNN technology into a System on Chip (SoC) platform, allowing for real-time processing of video data. The versatility of utilizing an energy-efficient SoC extends its application to smart environments aimed at improving mental health. By employing algorithms capable of detecting emotional states across various individuals, the study enhances its effectiveness. Additionally, it identifies the best CNN operations tailored to the technical specifications of the devices involved. Thus, The development involves a three-step process: (i) collecting enough data to build a robust model, (ii) training the model and evaluating its performance using test values, and (iii) applying the model on the development board. Our study demonstrates the feasibility of using AIoB to recognize and respond to human emotions in closed areas. By integrating emotional cues with environmental measurements, our system can create more personalized and empathetic spaces that cater to the needs of individuals. Our approach could have significant implications for designing public spaces to promote well-being and emotional satisfaction.