The longterm effect of media violence exposure on aggression of youngsters Abstract   The effect of media violence on aggression has always been a trending issue, and a better understanding of the psychological mechanism of the impact of media violence on youth aggression is an extremely important research topic for preventing the negative impacts of media violence and juvenile delinquency. From the perspective of anger, this study explored the longterm effect of different degrees of media violence exposure on the aggression of youngsters, as well as the role of aggressive emotions. The studies found that individuals with a high degree of media violence exposure (HMVE) exhibited higher levels of proactive aggression in both irritation situations and higher levels of reactive aggression in lowirritation situations than did participants with a low degree of media violence exposure (LMVE). After being provoked, the anger of all participants was significantly increased, and the anger and proactive aggression levels of the HMVE group were significantly higher than those of the LMVE group. Additionally, rumination and anger played a mediating role in the relationship between media violence exposure and aggression. Overall, this study enriches the theoretical understanding of the longterm effect of media violence exposure on individual aggression. Second, this study deepens our understanding of the relatively new and relevant phenomenon of the mechanism between media violence exposure and individual aggression.

Link between Facial Expressions and Emotional States Induced by Exposure to Multimedia Content The explosive growth of digital videos has created new challenges for computer science. While many advances on video indexing, retrieval and summarization based on general, subjectindependent, objective descriptors have been made in the past years, research on the use of individual subjective preferences and affective states is at the forefront of research and poses great challenges. In this article, we study the relationship between emotional states reported by viewers and their facial physiological changes observed during the display of different video genres. A dataset of twenty videos was created from YouTube video sharing platform. During the exhibition of the videos, the viewerxe2x80x99s facial activities have been recorded and analyzed by means of Action Units (AUs). After that, emotional states selfreported by the viewers were assigned to video shots. Labels were divided into four categories, defined according to a discrete version of Russelxe2x80x99s Circumplex emotion model. Different machine learning models were trained to test the relationship between the measured facial features and the selfreported emotional categories. We obtained kfold cross validation accuracies that were above chance for the best learned models. As a result of this study, we concluded that AUs can indeed be used as an valuable tool to estimate emotional categories during exposure to audiovisual stimuli, and, therefore, should be used in further studies that take advantage of those categories to devise personalized multimedia retrieval and summarization approaches.