Neutral Emotions Research Articles

Influences of Emotion on Driving Decisions at Different Risk Levels: An Eye Movement Study.

To explore the influences of traffic-related negative emotions on driving decisions, we induced drivers’ three emotions (neutral emotion, traffic-related negative emotion, and traffic-unrelated negative emotion) by videos, then the drivers were shown traffic pictures at different risk levels and made decisions about whether to slow down, while their eye movements were recorded. We found that traffic-related negative emotion influenced driving decisions. Compared with neutral emotion, traffic-related negative emotion led to an increase in the number of decelerations, and the higher the risk, the more the number of decelerations. The visual processing time of the risk area was shorter in the traffic-related negative emotional state than that in the neutral emotional state. The less time drivers spend looking at the risk area, the faster they make their driving decisions. The results suggest that traffic-related negative emotions lead drivers to make more conservative decisions. This study supports the rationality of using traffic accident materials to conduct safety education for drivers. This article also discussed the significance of traffic-related negative emotions to social security.

Adaptive Rationality in Strategic Interaction: Do Emotions Regulate Thinking About Others?

Forming beliefs or expectations about others’ behavior is fundamental to strategy as it codetermines the outcomes of interactions in and across organizations. In the game-theoretic conception of rationality, agents reason iteratively about each other to form expectations about behavior. According to prior scholarship, actual strategists fall short of this ideal, and attempts to understand the underlying cognitive processes of forming expectations about others are in their infancy. We propose that emotions help regulate iterative reasoning, that is, their tendency to not only reflect on what others think, but also on what others think about their thinking. Drawing on a controlled experiment, we find that a negative emotion (fear) deepens the tendency to engage in iterative reasoning compared with a positive emotion (amusement). Moreover, neutral emotions yield even deeper levels of iterative reasoning. We tentatively interpret these early findings and speculate about the broader link of emotions and expectations in the context of strategic management. Extending the view of emotional regulation as a capability, emotions may be building blocks of rational heuristics for strategic interaction and enable interactive decision making when strategists have little experience with the environment. History: This paper has been accepted for the Special Issue on Bounded Rationality and Strategic Interaction.

Induced negative arousal modulates the speed of visual working memory consolidation.

This study examines how induced negative arousal influences the consolidation of fragile sensory inputs into durable working memory (WM) representations. Participants performed a visual WM change detection task with different amounts of encoding time manipulated by random pattern masks inserted at different levels of memory-and-mask Stimulus Onset Asynchrony (SOA). Prior to the WM task, negative or neutral emotion was induced using audio clips from the International Affective Digital Sounds (IADS). Pupillometry was simultaneously recorded to provide an objective measure of induced arousal. Self-report measures of early-life stress (i.e., adverse childhood experiences) and current mood states (i.e., depressed mood and anxious feeling) were also collected as covariates. We find that participants initially remember a comparable number of WM items under a short memory-and-mask SOA of 100 ms across emotion conditions, but then encode more items into WM at a longer memory-and-mask SOA of 333 ms under induced negative arousal. These findings suggest that induced negative arousal speeds up WM consolidation. Yet, induced negative arousal does not seem to significantly affect participants' WM storage capacity estimated from a separate no mask condition. Furthermore, this emotional effect on WM consolidation speed is moderated by key affect-related individual differences. Participants who have greater pupil responses to negative IADS sounds or have more early-life stress show faster WM consolidation under induced negative arousal. Collectively, our findings reveal a critical role of phasic adrenergic responses in the rapid consolidation of visual WM content and identify potential moderators of this association. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Ire and punishment: Incidental anger and costly punishment in children, adolescents, and adults

Why do children, adolescents, and adults engage in costly punishment to sanction fairness violations? Two studies investigated the differential impact of incidental anger on the costly punishment of 8-year-olds, 13-year-olds, and adults. Focusing on experimentally manipulated incidental anger allows for a causal investigation as to whether and how anger affects costly punishment in these age groups in addition to other motives such as inequity aversion. Study 1 (N = 210) assessed the effect of incidental anger (vs. a neutral emotion) on second-party punishment, where punishers were direct victims of fairness violations. Study 2 (N = 208) examined third-party punishment, where the punisher was an observer unaffected by the violation. Across ages, incidental anger increased the second-party punishment of unequal offers but not equal offers. Thus, anger seems to play a causal role in the punishment of unfairness when fairness violations are self-relevant. As predicted, adults’ third-party punishment of unequal offers was higher in the incidental anger condition than in the neutral emotion condition. Children’s third-party punishment of unfairness was not affected by the emotion condition, but incidental anger increased adolescents’ third-party punishment across offers. Overall, our data suggest that the association between anger and costly punishment is based on the self-relevance of the violation. In third-party situations, where unfairness does not affect the self, social-cognitive processes that develop well into adulthood, such as emotional appraisals, might be necessary for third parties to engage in costly punishment.

EMOTIONAL CONTEXT OF THE ACCURATE AND INACCURATE METACOGNITIVE MONITORING LEVELS

Purpose. The aim of the paper is to identify what emotions arise after obtaining information about the correct or incorrect estimations of the performance in a short knowledge test. To do this a short pilot experimental research was conducted outlining some peculiarities of the accurate and inaccurate metacognitive monitoring levels in university students’ learning activity. Methods and procedure. The theoretical and comparative practical methods of studying the accurate and inaccurate metacognitive monitoring levels of university students have been used in the study. The participants (n = 77, Mage = 17.62) answered knowledge information questions of three types (multiple-choice, open-answer, and ‘Yes’/‘No’) set up both in Ukrainian and in English and performed prospective and retrospective metacognitive judgements of learning. Calibration procedure helped to define average indicators of both the illusion of knowing and the illusion of not knowing, as well as adequate levels of metacognitive monitoring. After the announcement of the correct answers the participants noted their emotions that arose when the answer were correct / incorrect. Results. The results showed the occurrence of emotions with positive and negative affect both in the accurate (the adequate level) and inaccurate metacognitive monitoring levels (the illusions of knowing and not knowing). According to the data, in total, the examples of the emotional status of the highest frequency were mainly with the positive affect. The illusion of not knowing was expressed by 5 main samples of emotional status of negative affect (‘disappointed’, ‘surprised’, ‘offended’, ‘inattentive’, and ‘sad’), 8 samples of emotions of positive affect, though the value of them was not very high in almost all the cases (‘normal’, ‘good’, ‘glad’, ‘calm’, ‘nice’, ‘proud’, ‘satisfied’, and ‘unsurprised’), and 2 of neutral affect as well. Students with the adequate level of their RCJs showed only positive and neutral emotions of different value. Conclusions. The current results continue to expand an investigation of metacognitive monitoring reliability factors. Still the presented ideas serve more as a framework for future study and their pure implications still need thorough further researches.

Prone to food in bad mood-Emotion-potentiated food-cue reactivity in patients with binge-eating disorder.

ObjectiveTheories on emotional eating are central to our understanding of etiology, maintenance, and treatment of binge eating. Yet, findings on eating changes under induced negative emotions in binge‐eating disorder (BED) are equivocal. Thus, we studied whether food‐cue reactivity is potentiated under negative emotions in BED, which would point toward a causal role of emotional eating in this disorder.MethodsPatients with BED (n = 24) and a control group without eating disorders (CG; n = 69) completed a food picture reactivity task after induction of negative versus neutral emotions. Food‐cue reactivity (self‐reported food pleasantness, desire to eat [DTE], and corrugator supercilii muscle response, electromyogram [EMG]) was measured for low‐ and high‐caloric food pictures.ResultsPatients with BED showed emotion‐potentiated food‐cue reactivity compared to controls: Pleasantness and DTE ratings and EMG response were increased in BED during negative emotions. This was independent of caloric content of the images.ConclusionsFood‐cue reactivity in BED was consistent with emotional eating theories and points to a heightened response to all foods regardless of calorie content. The discrepancy of appetitive ratings with the aversive corrugator response points to ambivalent food responses under negative emotions in individuals with BED.

The consistency of the influence of pride and shame on cognitive flexibility: Evidence from ERP

This study uses simple tasks to induce self-conscious emotions and event-related potentials to investigate the effects of pride, neutral, and shame emotions on cognitive flexibility. The behavior results revealed that the switching tasks had a longer reaction time and a lower accuracy rate than the repetitive tasks. Furthermore, the reaction time was longer, and the accuracy rate was lower for individuals in the shame and pride emotions group than the neutral group. ERP results revealed that the switching task induced a larger P3 wave amplitude than the repetitive task. In addition, the P3 difference wave for pride and shame emotions was significantly smaller than that of neutral emotions. However, there were no significant differences in N2 wave. These findings imply that pride and shame may inhibit conversion ability and consistently affect cognitive flexibility. This consistency may be manifested in late decision-making tasks. Our finds provide a theoretical basis for management of students' pride and shame.

Childhood maltreatment experiences and emotion perception in young Chinese adults: Sex as a moderator.

Men and women seem to perceive and react differently to emotional stimuli and have different susceptibilities to childhood trauma. With a cross-sectional design, this study aimed to investigate whether specific patterns of childhood-maltreatment experiences are associated with specific patterns of emotion perception and the sex differences in this relationship. A total of 173 adults rated valence, arousal, and dominance for 60 pictures (varying in pleasantness, unpleasantness, and neutral emotion) from the International Affective Picture System and completed the Childhood Trauma Questionnaire-Short Form. Using a partial least squares (PLS) regression analysis, after controlling for depressive and anxious states, recent stressful life events, personality, and cognitive reappraisal strategy, we identified a profile (linear combination) of childhood-maltreatment experiences (emotional neglect, physical neglect, and physical abuse) that was associated with a profile of emotion-perception dimensions (perceiving negative visual stimuli as more unpleasant and subservient, positive stimuli as more pleasant and dominant, and neutral stimuli as more arousing). This association pattern was significant only for the male participants. Hence, our findings suggest that childhood maltreatment might make men more "emotional" in their adulthood. The impact of this childhood-maltreatment-associated alteration in emotion perception on male mental health needs further investigation.

A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis

Coronavirus (COVID-19) has impacted nearly every person across the globe either in terms of losses of life or as of lockdown. The current coronavirus (COVID-19) pandemic is a rare/special situation where people can express their feelings on Internet-based social networks. Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions. This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown. The research is based on the logic expressed by people in this perspective and emotions for the suffering of COVID-19 and lockdown. In this research, we have used a Long Short-Term Memory (LSTM) network model with Convolutional Neural Network using Keras python deep-learning library to determine whether social media platform users are depressed in terms of positive, negative, or neutral emotional out bust based on their Twitter posts. The results showed that the model has 88.14% accuracy (representation of the correct prediction over the test dataset) after 10 epochs which most tweets showed had neutral polarity. The evaluation shows interesting results in positive (1), negative (–1), and neutral (0) emotions through different visualization.

Empathic Responses of Behavioral-Synchronization in Human-Agent Interaction

Artificial entities, such as virtual agents, have become more pervasive. Their long-term presence among humans requires the virtual agent's ability to express appropriate emotions to elicit the necessary empathy from the users. Affective empathy involves behavioral mimicry, a synchronized co-movement between dyadic pairs. However, the characteristics of such synchrony between humans and virtual agents remain unclear in empathic interactions. Our study evaluates the participant's behavioral synchronization when a virtual agent exhibits an emotional expression congruent with the emotional context through facial expressions, behavioral gestures, and voice. Participants viewed an emotion-eliciting video stimulus (negative or positive) with a virtual agent. The participants then conversed with the virtual agent about the video, such as how the participant felt about the content. The virtual agent expressed emotions congruent with the video or neutral emotion during the dialog. The participants’ facial expressions, such as the facial expressive intensity and facial muscle movement, were measured during the dialog using a camera. The results showed the participants’ significant behavioral synchronization (i.e., cosine similarity ≥ .05) in both the negative and positive emotion conditions, evident in the participant's facial mimicry with the virtual agent. Additionally, the participants’ facial expressions, both movement and intensity, were significantly stronger in the emotional virtual agent than in the neutral virtual agent. In particular, we found that the facial muscle intensity of AU45 (Blink) is an effective index to assess the participant's synchronization that differs by the individual's empathic capability (low, mid, high). Based on the results, we suggest an appraisal criterion to provide empirical conditions to validate empathic interaction based on the facial expression measures.

Emotion Recognition With Audio, Video, EEG, and EMG: A Dataset and Baseline Approaches

This paper describes a new posed multimodal emotional dataset and compares human emotion classification based on four different modalities - audio, video, electromyography (EMG), and electroencephalography (EEG). The results are reported with several baseline approaches using various feature extraction techniques and machine-learning algorithms. First, we collected a dataset from 11 human subjects expressing six basic emotions and one neutral emotion. We then extracted features from each modality using principal component analysis, autoencoder, convolution network, and mel-frequency cepstral coefficient (MFCC), some unique to individual modalities. A number of baseline models have been applied to compare the classification performance in emotion recognition, including k-nearest neighbors (KNN), support vector machines (SVM), random forest, multilayer perceptron (MLP), long short-term memory (LSTM) model, and convolutional neural network (CNN). Our results show that bootstrapping the biosensor signals (i.e., EMG and EEG) can greatly increase emotion classification performance by reducing noise. In contrast, the best classification results were obtained by a traditional KNN, whereas audio and image sequences of human emotions could be better classified using LSTM.

Emotion Recognition and EEG Analysis Using ADMM-Based Sparse Group Lasso

This study presents an efficient sparse learning-based pattern recognition framework to recognize the discrete states of three emotions—happy, angry, and neutral emotion—using electroencephalogram (EEG) signals. In affective computing with massive spatiotemporal brainwave signals, a large number of features can be extracted to capture various information from multivariate brain data. However, it is often a challenge to model high-dimensional features efficiently in consideration of the intrinsic structure, such as channel location, feature group, time epoch, etc. In this study, features were extensively extracted from EEG signals and were applied on a structured sparse learning model to perform feature selection and classification simultaneously. An efficient ADMM-based algorithm with a closed-form solution was developed to solve the sparse group model. Experimental results show that the proposed method is capable of selecting a small number of important neural features to discriminate the three emotion states with high classification accuracy. With greatly enhanced interpretability and efficiency to learn neural signatures of brain activity from high-dimensional-feature, low-sample-size brain imaging data, the presented computational framework is promising for handling emotion recognition problems with high-dimensional brain imaging data.

Design of Service Robot Based on User Emotion Recognition and Environmental Monitoring.

Robots may be able to comprehend human emotions better by adding speech emotion recognition and environment monitoring functions to human-computer interaction systems. Robots can offer more humanized services by adapting to human emotions, resulting in a comfortable and cordial interaction between humans and robots. Improve the environment for communication and computer-human interaction and also interactive computer experience. In order for service robots to perform fluid human-computer interaction, this paper designs a sentiment analysis model based on CNN (convective neural network) to detect the feeling of interacting objects. It also builds a sentiment analysis model and an open domain dialogue system suitable for service robots. Examine the emotions experienced by the objects while they conversed. According to test results, the sentiment classification method used in this article performs more accurately on the dataset than the conventional model, and the final sentiment analysis model's F1 value can reach 0.931, which is better able to identify an emotional state. Using all voice samples as the input content of the network would eliminate the confusion between neutral emotions and other nonneutral emotions, boosting the accuracy of sentiment analysis in comparison to the fixed-length processing method of dividing or filling samples.

Emotion Analysis Using Multilayered Networks for Graphical Representation of Tweets

Anticipating audience reaction towards a certain piece of text is integral to several facets of society ranging from politics, research, and commercial industries. Sentiment analysis (SA) is a useful natural language processing (NLP) technique that utilizes both lexical/statistical and deep learning methods to determine whether different sized texts exhibit a positive, negative, or neutral emotion. However, there is currently a lack of tools that can be used to analyze groups of independent texts and extract the primary emotion from the whole set. Therefore, the current paper proposes a novel algorithm referred to as the Multi-Layered Tweet Analyzer (MLTA) that graphically models social media text using multi-layered networks (MLNs) in order to better encode relationships across independent sets of tweets. Graph structures are capable of capturing meaningful relationships in complex ecosystems compared to other representation methods. State of the art Graph Neural Networks (GNNs) are used to extract information from the Tweet-MLN and make predictions based on the extracted graph features. Results show that not only does the MLTA predict from a larger set of possible emotions, delivering a more accurate sentiment compared to the standard positive, negative or neutral, it also allows for accurate group-level predictions of Twitter data.

Emotion Recognition by Discriminating EEG Segments With High Affective Content From Automatically Selected Relevant Channels

In EEG-based emotion recognition, subject’s affective responses are captured by several channels (scalp level) by presenting target stimuli. However, the emotional responses are inconsistent throughout the acquired signal, and rather, it arises at certain duration with high prominence on certain channels. However, existing studies ignored this vital issue and considered the entire acquired signal for processing leading to inaccurate results. Therefore, this work proposes emotion recognition by identifying highly affective EEG segments from automatically chosen relevant channels based on instantaneous phase synchronization measurement among all channels involving Hilbert transform and phase locking values followed by a majority voting algorithm. Next, a random matrix theory discriminates the appropriate segments whose power spectral density has been used in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -NN-based classification. Experimental validation has been performed using the EEG dataset from SEED (with positive, neutral, and negative emotions). Five EEG subbands ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gamma$ </tex-math></inline-formula> ) are exclusively examined for their high correlation with emotions. The analyses include distinguishing proper segment lengths and their locations, minimum dominant channels while achieving the best classification for individual subband. The proposed method achieved the highest classification accuracy of up to 95% for higher frequency subbands ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gamma$ </tex-math></inline-formula> ) with 15 channels. Furthermore, the segment locations for positive and neutral emotions lie from start till 75% of the entire experiment time, whereas, for negative emotion, it is 25%–75%. Moreover, channels from the left frontal, central, and temporal regions are found very active, which is steady. In a comparative study, the proposed idea demonstrates its superiority by displaying the highest efficiency considering less data from minimum channels.

When it’s okay to hit: How Turkish and U.S. preschoolers and adults make judgments about permissible and impermissible acts of force

To navigate their complex social worlds, children need to make judgments about when, if ever, it is okay to use force against others. By adulthood, most come to condemn violence in most situations yet accept or even encourage force in other situations, such as self-defense or contact sports. This research examined key factors expected to guide how preschoolers and adults make judgments about permissible and impermissible force. These factors included the antecedent events—preceding the force act—and the emotion attributed to the victim. In four studies with Turkish and U.S. preschoolers and adults, an interviewer presented participants with vignettes in which one child used force against another. The antecedent event significantly affected children’s and, to an even greater extent, adults’ judgments about force. Participants were also more likely to judge force as permissible when they attributed positive or neutral emotions to the victim. Some cultural differences also emerged. The findings shed light on how children begin to draw moral distinctions between permissible and impermissible force.

The Influence of Emotional Awareness on Time Perception: Evidence From Event-Related Potentials

Prior studies found that participants overestimated both negative and positive emotional stimuli, compared with neutral emotion. This phenomenon can be explained by the “arousal mechanism.” Participants demonstrated individual differences in emotion perception. In other words, high emotional awareness resulted in high emotional arousal, and vice versa. This study extended existing findings by exploring the influence of emotional awareness on time perception in a temporal generalization task, while recording electroencephalographic (EEG) signals. The findings revealed that in the positive emotion condition, the high emotional awareness group made more overestimations, compared with the low emotional awareness group. However, no difference was observed in the neutral or negative emotion conditions. Moreover, the event-related potential (ERP) results showed that in the positive emotion condition, the high awareness group elicited larger vertex positive potential (VPP) amplitudes, compared with that of the low awareness group. However, no such differences were observed in the neutral and negative emotion conditions. Moreover, the contingent negative variation (CNV) (200–300, 300–490 ms) component showed that in the positive emotion, the amplitudes of the high awareness group were larger than that of the low awareness group; however, they did not show differences in the neutral condition. The findings of this study suggest that high emotional awareness produces higher physiological arousal; moreover, when participants were required to estimate the time duration of emotional pictures, they tended to make higher time overestimation. Thus, our results support the relationship between emotional awareness and time perception.

The effect of music-induced emotion on visual-spatial learning in people with Parkinson's disease: A pilot study

IntroductionEmotional states have been shown to influence cognitive processes including visual-spatial learning. Parkinson's Disease (PD), besides manifesting with the cardinal motor symptoms, presents cognitive and affective disturbances. Here we aimed at investigating whether manipulation of the emotional state by means of music was able to influence the performance of a visual-spatial learning task in a group of PD participants. MethodsTen PD patients and 11 healthy elderly (ELD) were asked to perform a visual-spatial learning task while listening two musical pieces evoking a neutral emotion or fear. Targets were presented on a screen in a preset order over four blocks and subjects were asked to learn the sequence order by attending to the display. At the end of each block, participants were asked to verbally recall the sequence and a score was assigned (Verbal Score, VS). ResultsAnalysis of variance-type statistic test on the VS disclosed a significant effect of Music and sequence Blocks (p = 0.01 and p < 0.001, respectively) and a significant interaction between Group and sequence Blocks. Sequence learning occurred across the training period in both groups, but PD patients were slower than ELD and at the end of the training period learning performance was worse in PD with respect to ELD. In PD patients, like in ELD, fear-inducing music has a detrimental effect on visual-spatial learning performances, which are slower and decreased. ConclusionThese findings confirm an impairment in visual-spatial learning in PD and indicates that the emotional state influences this learning ability similarly to healthy controls.

The Borderline Bias in Explicit Emotion Interpretation

Atypical emotion interpretation has been widely reported in individuals with borderline personality disorder (iBPD); however, empirical studies reported mixed results so far. We suggest that discrepancies in observations of emotion interpretation by iBPD can be explained by biases related to their fear of rejection and abandonment, i.e., the three moral emotions of anger, disgust, and contempt. In this study, we hypothesized that iBPD would show a higher tendency to correctly interpret these three displays of social rejection and attribute more negative valence. A total of 28 inpatient iBPDs and 28 healthy controls were asked to judge static and dynamic facial expressions in terms of emotions, valence, and self-reported arousal evoked by the observed faces. Our results partially confirmed our expectations. The iBPD correctly interpreted the three unambiguous moral emotions. Contempt, a complex emotion with a difficulty in recognizing facial expressions, was recognized better by iBPD than by healthy controls. All negative emotions were judged more negatively by iBPD than by controls, but no difference was observed in the neutral or positive emotion. Alexithymia and anxiety trait and state levels were controlled in all analyses.

The Influence of the Debunker's Identity and Emotional Expression on the Sharing Behavior of Debunking Information.

Owing to the proliferation of rumors on social media, it is necessary to disseminate debunking information to minimize the harm caused by them. Using content analysis, sentiment analysis, and regression analysis, this study examined the mediating role of follower count in the relationship between the debunker’s identity and sharing behavior, and it explored the relationship between the text sentiment of debunking information and sharing behavior based on data on the spread of three rumors that circulated extensively on social media. Using an ordinary account as a reference, we found that the mediating or suppression effect (i.e., when direct and indirect effects are significant and opposite) of follower count in the relationship between debunker’s identity (celebrity, media, or government) and sharing behavior was significant. The three test identities (celebrity, media, and government) had more followers than the ordinary account, which resulted in a significant positive effect on the number of reposts. The debunker’s identity did not have a positive effect on the sharing of debunking information when controlling for mediating variables. Debunking information with emotional overtones (positive or negative) was shared more widely compared with information with neutral emotions, and the dominant emotional polarity was different in the three different rumors. These findings can contribute to the generation of debunking information content, which can aid in the development of effective communication strategies and improvement in the efficiency of crisis management.