Crowd Emotion Research Articles

Judging emotion in natural images of crowds.

It has been suggested that humans use summary statistics such as the average of the emotion of individual faces when they rapidly judge group emotion. Previous studies have mainly used faces of actors posing basic emotions, and morphed versions of these faces, against a plain background. In the present study, photographs taken in real-world settings were used to investigate the influence of mean facial emotion, maximal facial emotion, and background context on judgments of group emotion, assessed using dimensional ratings of valence, arousal, and dominance. Background context explained a significant amount of unique variance in group ratings for each dimension. Mean emotion explained additional unique variance for valence ratings, whereas maximal emotion explained additional unique variance for arousal, with dominance showing more mixed results. Removing background context and disrupting the contextual and spatial relationship between faces by randomly replacing faces with ones from other images within the stimulus set increased reliance on mean emotion. However, under all conditions, the maximally arousing face continued to exert an influence on ratings of group arousal, in line with theoretical accounts arguing for a unique bottom-up effect of emotional arousal on attentional competition and postattentive perceptual processing. Together these findings suggest that individuals' reliance on average emotion when judging crowd scenes differs as a function of the dimension of affect. In addition, the presence of background context both directly impacts judgments of crowd emotion and modulates the relative influence of maximal versus mean emotion on these judgments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Enhancing Place Emotion Analysis with Multi-View Emotion Recognition from Geo-Tagged Photos: A Global Tourist Attraction Perspective

User−generated geo−tagged photos (UGPs) have emerged as a valuable tool for analyzing large−scale tourist place emotions with unprecedented detail. This process involves extracting and analyzing human emotions associated with specific locations. However, previous studies have been limited to analyzing individual faces in the UGPs. This approach falls short of representing the contextual scene characteristics, such as environmental elements and overall scene context, which may contain implicit emotional knowledge. To address this issue, we propose an innovative computational framework for global tourist place emotion analysis leveraging UGPs. Specifically, we first introduce a Multi−view Graph Fusion Network (M−GFN) to effectively recognize multi−view emotions from UGPs, considering crowd emotions and scene implicit sentiment. After that, we designed an attraction−specific emotion index (AEI) to quantitatively measure place emotions based on the identified multi−view emotions at various tourist attractions with place types. Complementing the AEI, we employ the emotion intensity index (EII) and Pearson correlation coefficient (PCC) to deepen the exploration of the association between attraction types and place emotions. The synergy of AEI, EII, and PCC allows comprehensive attraction−specific place emotion extraction, enhancing the overall quality of tourist place emotion analysis. Extensive experiments demonstrate that our framework enhances existing place emotion analysis methods, and the M−GFN outperforms state−of−the−art emotion recognition methods. Our framework can be adapted for various geo−emotion analysis tasks, like recognizing and regulating workplace emotions, underscoring the intrinsic link between emotions and geographic contexts.

Implicit induction of expressive suppression in regulation of happy crowd emotions

ABSTRACT Implicit emotion regulation provides an effective means of controlling emotions triggered by a single face without conscious awareness and effort. Crowd emotion has been proposed to be perceived as more intense than it actually is, but it is still unclear how to regulate it implicitly. In this study, participants viewed sets of faces of varying emotionality (e.g. happy to angry) and estimated the mean emotion of each set after being primed with an expressive suppression goal, a cognitive reappraisal goal, or a neutral goal. Faster discrimination for happy than angry crowds was observed. After induction of the expressive suppression goal instead of the cognitive reappraisal goal, augmented N170 and early posterior negativity (EPN) amplitudes, as well as attenuated late positive potential (LPP) amplitudes, were observed in response to happy crowds compared to the neutral goal. Differential processing of angry crowds was not observed after the induction of both regulatory goals compared to the neutral goal. Our findings thus reveal the happy-superiority effect and that implicit induction of expressive suppression improves happy crowd emotion recognition, promotes selective coding, and successfully downregulates the neural response to happy crowds.

Create a crowd emotion detection framework with ecological validity

Create a crowd emotion detection framework with ecological validity

The Impact of Spatial Frequency on the Perception of Crowd Emotion: An fMRI Study.

Recognizing the emotions of faces in a crowd is crucial for understanding overall behavior and intention as well as for smooth and friendly social interactions. However, it is unclear whether the spatial frequency of faces affects the discrimination of crowd emotion. Although high- and low-spatial-frequency information for individual faces is processed by distinct neural channels, there is a lack of evidence on how this applies to crowd faces. Here, we used functional magnetic resonance imaging (fMRI) to investigate neural representations of crowd faces at different spatial frequencies. Thirty-three participants were asked to compare whether a test face was happy or more fearful than a crowd face that varied in high, low, and broad spatial frequencies. Our findings revealed that fearful faces with low spatial frequencies were easier to recognize in terms of accuracy (78.9%) and response time (927 ms). Brain regions, such as the fusiform gyrus, located in the ventral visual stream, were preferentially activated in high spatial frequency crowds, which, however, were the most difficult to recognize behaviorally (68.9%). Finally, the right inferior frontal gyrus was found to be better activated in the broad spatial frequency crowds. Our study suggests that people are more sensitive to fearful crowd faces with low spatial frequency and that high spatial frequency does not promote crowd face recognition.

Catching a smile from individuals and crowds: Evidence for distinct emotional contagion processes.

Research examining how crowd emotions impact observers usually requires participants to engage in an atypical mental process whereby (static) arrays of individuals are cognitively integrated to represent a crowd. The present work sought to extend our understanding of how crowd emotions may spread to individuals by assessing self-reported emotions, attention and muscle movement in response to emotions of dynamic, virtually modeled crowd stimuli. Self-reported emotions and attention from thirty-six participants were assessed when foreground and background crowd characters exhibited homogeneous (Study 1) or heterogeneous (Study 2) positive, neutral, or negative emotions. Results suggested that affective responses in observers are shaped by crowd emotions even in the absence of direct attention. Thirty-four participants supplied self-report and facial electromyography responses to the same homogeneous (Study 3) or heterogeneous (Study 4) crowd stimuli. Results indicated that positive crowd emotions appeared to exert greater attentional pull and objective responses, while negative crowd emotions also elicited affective responses. Study 5 (n = 67) introduced a control condition (stimuli containing an individual person) to examine if responses are unique to crowds and found that emotional contagion from crowds was more intense than from individuals. These studies present methodological advances in the study of crowd emotional contagion and have implications for our broader understanding of how people process, attend, and affectively respond to crowds. Advancing theory by suggesting that emotional contagion from crowds is distinct from that elicited by individuals, findings may have applications for refining crowd management approaches. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Crowd Emotion Prediction for Human-Vehicle Interaction Through Modified Transfer Learning and Fuzzy Logic Ranking

In metropolitan environments, unmanned aerial vehicles (UAVs) equipped with video surveillance equipment can monitor crowd behavior and maintain public safety. In high-traffic areas where humans are more likely to make mistakes, a smart city needs modern technology to forecast the behavior of its residents. In order to improve citywide traffic flow, urban transportation systems (UTSs) monitor and learn how people behave in crowds. Using UAVs for video surveillance in smart cities, our research describes a unique way to assess crowd condition, which expands the scope of human-vehicle interactions. Moreover, we use fuzzy logic ranking to improve the system’s ability to detect anomalies in crowds. In order to improve decision-making, a novel deep transfer learning (DTL) technique is applied to the UAV’s received frames. A 98.5% accuracy rate, satisfactory performance, and robustness to population behavior are all characteristics of the proposed integrated model. In UTSs and urban areas, our novel intelligent system analyzes human behavior based on vehicle-human interactions. In areas with low and high traffic congestion, the modified ResNet (mResNet) architecture predicts the crowd’s condition based on fuzzy logic (FLA). Through decision-making based on accurate crowd conditions, best general paths can be selected using the algorithm.

The effect of masks on the emotion perception of a facial crowd

The present study investigated the effect of facial masks on people’s ability to perceive emotions in crowds. We presented faces with the bottom halves occluded by masks or full faces without occlusion. In two sequentially presented crowds, we varied the number of faces, emotional valence, and intensity of facial expressions, examining the impact of masks on the perception of crowd emotion. Participants reported which of the two crowds they would avoid based on the crowds’ average emotions. The participants’ ability to judge the average emotion of a crowd, especially a crowd expressing happiness, was impaired when the crowd wore masks. For faces covered by masks, crowd emotion judgments were more negatively biased than those without masks. However, participants could still distinguish the emotional intensities of a crowd wearing masks above chance. Additionally, participants responded more quickly to a crowd with more people without compromising accuracy, despite the perceptual challenges imposed by facial masks. Our results suggest that under ambiguous social situations in which individuals’ emotions are partially hidden by masks, a large group may provide stronger social cues than a small group, thereby promoting communication and regulating social behaviors.

The relationship between ensemble coding and individual representation of crowd facial emotion

In recent years, the processing mechanism of group expression has gradually gained the attention of researchers owing to its high ecological validity. However, research on the relationship between ensemble coding and individual representation is still in the early stage of the investigation, with many studies remaining at the behavioral level and findings varying widely. Based on our behavioral research (Experiment 1), we used EEG measures (Experiments 2A and 2B) to investigate the relationship between summary and object representations by manipulating the exposure time of crowd emotions. The behavioral results indicated that participants performed better in judging emotions of multiple faces compared to a single face during the shorter exposure time, whereas the reverse occurred during the long exposure time. Furthermore, ERP results revealed that the N2pc effect was not affected by the number of faces in the short exposure time; however, as the exposure time increased, the N2pc increased as a function of the number of faces. The findings of the current investigation align with time-dependent assumption, indicating that during short time of visual processing, although individual representations may not be fully developed, ensemble representations are initially established. With longer processing times, detailed individual representations become complete and take precedence.

An influence maximization method based on crowd emotion under an emotion-based attribute social network

Most research on influence maximization focuses on the network structure features of the diffusion process but lacks the consideration of multi-dimensional characteristics. This paper proposes the attributed influence maximization based on the crowd emotion, aiming to apply the user’s emotion and group features to study the influence of multi-dimensional characteristics on information propagation. To measure the interaction effects of individual emotions, we define the user emotion power and the cluster credibility, and propose a potential influence user discovery algorithm based on the emotion aggregation mechanism to locate seed candidate sets. A two-factor information propagation model is then introduced, which considers the complexity of real networks. Experiments on real-world datasets demonstrate the effectiveness of the proposed algorithm. The results outperform the heuristic methods and are almost consistent with the greedy methods yet with improved time performance.

System dynamics research of non-adaptive evacuation psychology in toxic gas leakage emergencies of chemical park

Chemical industry park plays an important role in optimizing the allocation of resources, but an emergency may make a great deal of personnel casualty and property loss. Many casualties are not the result of accidents but are caused by extreme behavior because of the non-adaptive psychology of the evacuees. Panic is one of the non-adaptive psychological behaviors during an evacuation, which is influenced by a variety of factors. Based on the consideration of the disaster environment and the evolution of crowd emotion, a system dynamics model of panic spread is established by using Anylogic software, and simulation experiments are carried out for different disaster severity, visibility, and groups. The results show that the number of people with severe panic increase when visibility decreases or disaster diffuses. Besides, the appropriate proportion of groups can effectively reduce the cooling time of crowd and ease the fears. However, continue to increase the number of groups has no significant effect on the panic control. This work can provide some reasonable guidance for regional emergency evacuation in chemical park.

Differential neurodynamics and connectivity in the dorsal and ventral visual pathways during perception of emotional crowds and individuals: a MEG study

Reading the prevailing emotion of groups of people ("crowd emotion") is critical to understanding their overall intention and disposition. It alerts us to potential dangers, such as angry mobs or panicked crowds, giving us time to escape. A critical aspect of processing crowd emotion is that it must occur rapidly, because delays often are costly. Although knowing the timing of neural events is crucial for understanding how the brain guides behaviors using coherent signals from a glimpse of multiple faces, this information is currently lacking in the literature on face ensemble coding. Therefore, we used magnetoencephalography to examine the neurodynamics in the dorsal and ventral visual streams and the periamygdaloid cortex to compare perception of groups of faces versus individual faces. Forty-six participants compared two groups of four faces or two individual faces with varying emotional expressions and chose which group or individual they would avoid. We found that the dorsal stream was activated as early as 68 msec after the onset of stimuli containing groups of faces. In contrast, the ventral stream was activated later and predominantly for individual face stimuli. The latencies of the dorsal stream activation peaks correlated with participants' response times for facial crowds. We also found enhanced connectivity earlier between the periamygdaloid cortex and the dorsal stream regions for crowd emotion perception. Our findings suggest that ensemble coding of facial crowds proceeds rapidly and in parallel by engaging the dorsal stream to mediate adaptive social behaviors, via a distinct route from single face perception.

Crowd emotion evaluation based on fuzzy inference of arousal and valence

Crowd behavior analysis is an important research topic in the field of video surveillance and public safety management. Crowd emotion has a strong relation with the crowd behavior. However, it is very challenging to predict crowd emotion using conventional emotion clues of humans from the video surveillance data, e.g. facial expression or body gesture. To tackle this challenge, this paper presents a crowd emotion evaluation method using fuzzy inference according to the arousal-valence model of the crowd movement. Specifically, the enthalpy, magnitude variance, confusion index and crowd density are extracted to describe crowd emotion. The enthalpy value and magnitude variance are taken as the input of the fuzzy inference system of arousal. And the confusion index and crowd density are used as the input of the fuzzy system of valence. The arousal value and valence value are the output respectively. Through establishing the relationship between arousal, valence and crowd features, the fuzzy rules are constructed to infer the emotion in the crowd scene. Experimental results show that the proposed method can effectively evaluate the arousal and valence in crowd emotion.

Modeling crowd emotion from emergent event video

AbstractIn emergency situation, mass panic often causes more causalities than the disaster itself. The crowd emotional model could be used to simulate how crowd behavior in emergency scenarios and be helpful for developing crowd evacuation plans in emergency situations. However, existing crowd emotional models usually set model parameters in an empirical manner and are not validated by real cases. In this paper, a crowd emotional model is proposed to simulate the crowd movement in outdoor emergency situations. First of all, the crowd entropy and the movement difference are proposed to describe the emotional impact of the crowd scene on the agents. The perception of vision and hearing are considered, and the calculation formulas of the agent's emotional intensity and crowd emotional contagion are proposed. By calculating individual trajectories in the real video, the cumulative differences between the movements of the real crowd and the corresponding virtual crowd are analyzed. At last, a multi‐parameter optimization method is implemented by the differential evolution algorithm. To verify the parameters in models, three videos which are generated from three real cases, including explosion attack, shooting incident, and crowd disturbance are selected for experimental verification. The results showed that the proposed model could be a feasible method for optimizing parameters to simulate the emergency scenario.

Using Deep Learning Models Combined with Crowd Emotion Models to Identify Abnormal Behaviors in Crowds

Aiming at the problem that the definition of crowd abnormal behavior detection is ambiguous and difficult to combine with context semantics, an algorithm using OCC human emotion model combined with crowd entropy is proposed. First calculate the crowd entropy for the crowd, and determine whether the entropy value is abnormal, if it is abnormal, further extract the optical flow OF and HOG. Then project it into two-dimensional vector data, send it to CNN for local feature extraction and combine with OCC model to achieve the description of crowd emotions. Finally, predict whether the abnormality occurs according to the judgment factor. Verified on the data set, this method shows a high accuracy.

Emotional sounds of crowds: spectrogram-based analysis using deep learning

Crowds express emotions as a collective individual, which is evident from the sounds that a crowd produces in particular events, e.g., collective booing, laughing or cheering in sports matches, movies, theaters, concerts, political demonstrations, and riots. A critical question concerning the innovative concept of crowd emotions is whether the emotional content of crowd sounds can be characterized by frequency-amplitude features, using analysis techniques similar to those applied on individual voices, where deep learning classification is applied to spectrogram images derived by sound transformations. In this work, we present a technique based on the generation of sound spectrograms from fragments of fixed length, extracted from original audio clips recorded in high-attendance events, where the crowd acts as a collective individual. Transfer learning techniques are used on a convolutional neural network, pre-trained on low-level features using the well-known ImageNet extensive dataset of visual knowledge. The original sound clips are filtered and normalized in amplitude for a correct spectrogram generation, on which we fine-tune the domain-specific features. Experiments held on the finally trained Convolutional Neural Network show promising performances of the proposed model to classify the emotions of the crowd.

Revisiting crowd behaviour analysis through deep learning: Taxonomy, anomaly detection, crowd emotions, datasets, opportunities and prospects

Crowd behaviour analysis is an emerging research area. Due to its novelty, a proper taxonomy to organise its different sub-tasks is still missing. This paper proposes a taxonomic organisation of existing works following a pipeline, where sub-problems in last stages benefit from the results in previous ones. Models that employ Deep Learning to solve crowd anomaly detection, one of the proposed stages, are reviewed in depth, and the few works that address emotional aspects of crowds are outlined. The importance of bringing emotional aspects into the study of crowd behaviour is remarked, together with the necessity of producing real-world, challenging datasets in order to improve the current solutions. Opportunities for fusing these models into already functioning video analytics systems are proposed.

Stochastic Gradient Descent–Whale Optimization Algorithm-Based Deep Convolutional Neural Network To Crowd Emotion Understanding

AbstractCrowd emotion understanding is an interesting research area that assists the security personnel to read the emotion/activity of the crowd in the locality. Most of the traditional methods utilize the low-level visual features to understand the crowd emotions that extend the gap between the low- and the high-level features. With the aim to develop an automatic method for emotion recognition, this paper utilizes the deep convolutional neural network (deep CNN). For the effective emotion recognition, it is essential to select the key frames of the video using the wavelet-based Bhattacharya distance. The key frames are fed to the space-time interest points descriptor that extracts the features and forms the input vector to the classifier. Deep CNN is trained using the proposed Stochastic Gradient Descent–Whale Optimization Algorithm, which is the unification of the standard stochastic gradient descent algorithm with whale optimization algorithm. The proposed classifier recognizes the emotions of the crowd, such as angry, escape, fight, happy, normal, running/walking and violence. The analysis of the method proved that the proposed approaches acquired a maximal accuracy, specificity and sensitivity of 0.9693, 0.9936 and 0.9675, respectively.

Dynamics of emotional contagion in dense pedestrian crowds

In places with high-density pedestrian movements, irrational emotions can quickly spread out under emergency, which may eventually lead to asphyxiation and crushing. It was noticed that a pedestrian's emotion in crowd would change as a result of the influence from other pedestrians. Thus, to explore the dynamics of emotion contagion process in dense pedestrians, two types of pedestrian emotions, i.e., negative and positive have been identified. Taking into account the emotional transit of a pedestrian, a crowd movement model is established in the present paper. We simulate pedestrian movement in a region with periodic boundary condition to study the dynamics of emotional contagion in dense crowds. Influences of the initial negative pedestrian proportion, pedestrian crowd density, emotion influence radius, and dose factor on the transition of overall crowd emotion state have been investigated. We expect this study could provide theoretical suggestions for crowd management.

Analyzing The Emotions of Crowd For Improving The Emergency Response Services

Twitter is an extremely popular micro-blogging social platform with millions of users, generating thousands of tweets per second. The huge amount of Twitter data inspire the researchers to explore the trending topics, event detection and event tracking which help to postulate the fine-grained details and situation awareness. Obtaining situational awareness of any event is crucial in various application domains such as natural calamities, man-made disaster and emergency responses. In this paper, we advocate that data analytics on Twitter feeds can help improve the planning and rescue operations and services as provided by the emergency personnel in the event of unusual circumstances. We take an emotional change detection approach and focus on the users’ emotions, concerns and feelings expressed in tweets during the emergency situations, and analyze those feelings and perceptions in the community involved during the events to provide appropriate feedback to emergency responders and local authorities. We employ improved emotion analysis and change point detection techniques to process, discover and infer the spatiotemporal sentiments of the users. We analyze the tweets from recent Las Vegas shooting (Oct. 2017) and note that the changes in the polarity of the sentiments and articulation of the emotional expressions, if captured successfully can be employed as an informative tool for providing feedback to EMS.