The Role of Emotions in Mobilization of Society to Fight Climate Change: Constructing Emotional State to Inspire Mass Movement

How do emotions contribute to mobilizing the international community to join massive protests against climate change? Although it is common to superficially state that protests are full of various emotions, it remains unclear how emotions become collective on the international level and how they ensure the spread of mass mobilization. This research paper examines the process of collectivization of emotions and how it explains mass mobilization in the case of international climate change strikes. This paper raises the question of how the emotional environment was favourably constructed in Greta Thunberg’s case in order to mobilize international society to join climate change strikes, and it aims to reveal how group emotions play an important role in successful international mobilization. Based on Sarah Ahmed’s theory of cultural politics of emotions and James M. Jasper’s theory linking emotions and social movements, it is assumed that specific emotions were circulated to create a distinct emotional environment that inspired the international community to join Thunberg’s climate strike. An Emotional Discourse Analysis revealed that Thunberg’s speeches are full of emotional potential that provokes reactive emotions such as fear, anger and hope in the global society and establishes an injustice-based framing of the problem as well as the dichotomy between the political elite and the global society. This study contributes to the research field of emotions in international relations by exploring in more depth the collectivization of emotions and expands the theory of cultural politics of emotions to include explanations of international politics phenomena such as mass mobilization.

A Study on the Formation and Evolution Model of Social Psychology in Major Public Health Emergencies

The occurrence of local or regional major public health emergencies has seriously damaged human health and life safety, and has an impact on the psychological state of the public. The study introduces “social amplification of risk” effect and the mechanism of emotional infection, and constructs a process model of psychological prediction, psychological cognition and psychological behavior in the generation of social psychology of major public health emergencies, and from the level of individual emotions, group emotions and social emotions analyze the evolutionary model of social psychology; Research has found that information disclosure, government trust and social confidence are the main factors influencing the formation of social psychology of major public health emergencies.

The Red Mirror

This book inquires into Vladimir Putin’s leadership strategy and relies on social identity theory to explain Putin’s success as a leader. The author argues that Russia’s second president has been successful in promoting his image as an embodiment of the shared national identity of the Russian citizens. He has articulated the shared collective perspective and has built a social consensus by tapping into powerful group emotions of shame and humiliation derived from the painful experience of the transition in the 1990s. He was able to overturn these emotions into pride and patriotism by activating two central pillars of the Soviet collective identity: a sense of exceptionalism that the Soviet regime promoted to consolidate the Soviet nation, and a sense of a foreign threat to the state and its people that also was foundational for the Soviet Union. Putin’s assertive foreign policy decisions, culminating in the annexation of Crimea, appeared to have secured, in the eyes of the Russian citizens, their insecure national identity. The top-down leadership and bottom-up collective identity–driven processes coalesced to produce a newly revanchist Russia, with its current leader perceived by many citizens to be irreplaceable. Politics of national identity in Russia are promoted through a well-coordinated media machine that works to focus citizens’ attention on Putin’s foreign policy and on Russia’s international standing. Public fears are played out against the backdrop of Soviet legacies of national exceptionalism and the politics of victimhood associated with the 1990s to conjure a sense of collective dignity, self-righteousness, and national strength to keep the present political system intact.

Addition of an Emotionally Stable Node in the SOSa-SPSa Model for Group Emotional Contagion of Panic in Public Health Emergency: Implications for Epidemic Emergency Responses

Sentiment contagion is similar to an infectious disease that spreads in a crowd. In this study, we explore the law of emotional infection under sudden public events by SIR model. The paper adds an emotionally stable node and establishes a group emotional infection model of U-SOSPa-SPSOa model. Simulation results show that our model is reasonable and can better explain the entire contagion process by considering four groups (unsusceptible-susceptible-optimistic-pessimistic) of people. Our theoretical results show: When the pessimists were below the critical value of 0.34, the number of negative emotional groups first increased and then decreased. As the proportion increases, the emotional peak of pessimists increases. The cure probability θo has the least influence on the P(t), and at the same time, under the action of θp, the P(t) reaches the stable state first. The increase of the risk coefficient can promote the pessimist infection. When the degree of risk is low, the rate of emotional infection is increased. When the degree of risk is high, the rate of infection is slowed. Therefore, system customizers and related managers can improve the efficiency of stable groups, adjust the proportion of initial negative emotions, control the infection of the spontaneous infection process, and directly deal with negative emotions. They can carry out treatment and other means to stabilize group emotions and maintain social stability.

Psychosocial pathways of collective action participation in NIMBY conflict: A regulated double mediation model

Frequent NIMBY conflicts have seriously affected social stability and urban development. This paper aims to explore the psychosocial path of people participating in the collective action of NIMBY conflict, and to provide theoretical basis for effective governance of NIMBY conflict. By integrating the psychosocial explanatory variables related to collective action, we construct a regulated double mediation Model, which is empirically tested with 566 questionnaires from the NIMBY conflict in gaoling, China. The results show that: group relative deprivation, group emotions and group effectiveness have positive effects on people's NIMBY conflict participation tendency; group effectiveness and group emotions are important mediating variables of group relative deprivation affecting people's NIMBY conflict participation tendency; group identity has a positive adjustment effect on people's group emotions, group effectiveness, and the participation tendency of NIMBY conflict. The research indicates that group relative deprivation is the key precursor of NIMBY conflict, group emotion is the key factor driving the deterioration of NIMBY conflict, and group identity is the key factor catalyzing the occurrence of NIMBY conflict. This study helps to explain the psychological mechanism of people's participation in NIMBY conflict, and has certain implications for the prevention and governance of NIMBY conflict.

The Activity Approach as a Resource for the Development of the Psychological Theory of Loyalty to the Group of Belonging

Objectives. The article describes an attempt to overcome the contradictions in the theory of loyalty by applying the traditional Russian approaches which have been successfully used for solving of scientific problems which are contiguous with loyalty. Background. The study is necessary for the formation of a unified conceptual base for further systematizing of research of loyalty and comparison of their results. Methodology. Based on provisions of the activity approach (L.S. Vygotsky, S.L. Rubinstein, A.N. Leontiev, А.V. Petrovsky etc.), a conceptual solution of the loyalty problem is proposed. Integrative conception of loyalty as a person’s participation in group at interconnected levels of self-determination, group emotions, as well as motivation, orientation and processes of group activity was developed by implementation of category of “activity” into a theoretical analysis. It has allowed to consider the psychological and behavioral aspects of loyalty, as well as its individual and social levels, in a holistic way within the framework of the general loyalty concept. It allowed developing the general concept of loyalty with provisions concerning system-forming factors of loyalty, dynamics and mechanisms of its development, ways of its diagnostics and correction, and possibility of coexistence of loyalties. Conclusions. The paper argues the consideration of the leading activity of the group as a system-forming factor of loyalty. A general conclusion about possibility of applying the activity approach for research of loyalty is done.

What Drives Our Emotions When We Watch Sporting Events? An ESM Study on the Affective Experience of German Spectators During the 2018 FIFA World Cup

There is ample evidence that watching sports induces strong emotions that translate into manifold consequential behaviours. However, it is rather ill-understood how exactly spectators’ emotions unfold during soccer matches and what determines their intensity. To address these questions, we used the 2018 FIFA World Cup as a natural quasi-experiment to conduct a pre-registered study on spectators’ emotional experiences. Employing an app-based experience-sampling design, we tracked 251 German spectators during the tournament and assessed high-resolution changes in core affect (valence, activation) throughout soccer matches. Across the three German matches, multi-level models revealed that all spectators exhibited strong changes on both affective dimensions in response to Germany’s performance. Although fans experienced slightly more intense affect than non-fans, particularly during losses, this moderating effect was very small in comparison to the magnitude of the affective fluctuations that occurred independent of fan identity. Taken together, the findings suggest group emotions (collectively felt emotion irrespective of individual affiliation) rather than group-affiliation based emotions (individually felt emotion because of an affiliated group), as the dominant process underlying spectator affect during the 2018 FIFA World Cup.

Multimodal Decision-level Group Sentiment Prediction of Students in Classrooms

Sentiment analysis can be used to study an individual or a group’s emotions and attitudes towards other people and entities like products, services, or social events. With the advancements in the field of deep learning, the enormity of available information on internet, chiefly on social media, combined with powerful computing machines, it’s just a matter of time before artificial intelligence (AI) systems make their presence in every aspect of human life, making our lives more introspective. In this paper, we propose to implement a multimodal sentiment prediction system that can analyze the emotions predicted from different modal sources such as video, audio and text and integrate them to recognize the group emotions of the students in a classroom. Our experimental setup involves a digital video camera with microphones to capture the live video and audio feeds of the students during a lecture. The students are advised to provide their digital feedback on the lecture as ‘tweets’ on their twitter account addressed to the lecturer’s official twitter account. The audio and video frames are separated from the live streaming video using tools such as lame and ffmpeg. A twitter API was used to access and extract messages from twitter platform. The audio and video features are extracted using Mel-Frequency Cepstral Co-efficients (MFCC) and Haar Cascades classifier respectively. The extracted features are then passed to the Convolutional Neural Network (CNN) model trained on the FER2013 facial images database to generate the feature vector for classification of video-based emotions. A Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), trained on speech emotion corpus database was used to train on the audio features. A lexicon-based approach with senti-word dictionary and learning based approach with custom dataset trained by Support Vector Machines (SVM) was used in the twitter-texts based approach. A decision-level fusion algorithm was applied on these three different modal schemes to integrate the classification results and deduce the overall group emotions of the students. The use-case of this proposed system will be in student emotion recognition, employee performance feedback, monitoring or surveillance-based systems. The implemented system framework was tested in a classroom environment during a live lecture and the predicted emotions demonstrated the classification accuracy of our approach.