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. 2020 Aug 7;15(8):e0236953. doi: 10.1371/journal.pone.0236953

Exploring collective emotion transmission in face-to-face interactions

Wen Zheng 1,2,*,#, Ailin Yu 1, Ping Fang 3,#, Kaiping Peng 1,*
Editor: Zezhi Li4
PMCID: PMC7413751  PMID: 32764830

Abstract

Collective emotion is the synchronous convergence of an effective response across individuals toward a specific event or object. Previous studies have focused on the transmission of cyber collective emotion; however, little attention has been paid to the transmission of collective emotion in face-to-face interactions. Using an experimental design, we examined how emotions are transmitted from some members to the whole group in face-to-face situations. We used a news report of a social event as an emotion stimulus to induce anger and disgust in 158 middle school students aged 12 to 15, with an average age of 13.20 years (SD = 0.651) We randomly assigned one-third of the participants to be “transmitters,” while the others were “receivers.” Transmitters shared their feelings with receivers; then, receivers communicated with other group members. The results indicated that negative collective emotions were transmitted from high- to low-intensity members, which converged through the effect of emotional contagion. It accumulated through the effect of an emotional circle, during which the feedback reinforced emotion intensity. The collective emotion transmission model comprised emotion diffusion, contagion, and accumulation. This model elucidates the intrinsic features of collective emotion transmission, enriches the research on collective emotion, and provides theoretical references for monitoring and managing future public events.

Introduction

Human emotions are individual, one-way, and unrepeatable phenomena [1,2]. Researchers have increasingly realized that emotions at the collective level play a key role in our daily lives. Collective emotion is the synchronous convergence of an effective response across individuals toward a specific event or object [2,3]. Collective emotions constitute a wide range, such as global panic concerning the coronavirus or the public’s excitement after their country’s win at the Olympics. Ample research has examined cyber collective emotions [4,5]; however, studies on collective emotional transmission in face-to-face situations mainly focus on the dyad interactions [6,7]. A face-to-face situation refers to a condition in which many people gather together in the same spot: namely, the formation of an “offline” event. How do people transmit emotions at the collective level? Does this transmission in face-to-face interactions follow the same rules as those of cyber collective emotions? Exploring how collective emotion is transmitted in face-to-face interactions can provide empirical and theoretical support to the understanding of collective emotions and provide further guidance for addressing public events.

Researchers have focused on cyber collective emotions using computational simulation or big data [4–10]. The essence of collective emotion transmission is emotional information transmission among group members [11]. Emotion transmission follows the general pattern of information transmission; however, the special features of emotion make emotion transmission different from information transmission. Many studies have addressed the area of information transmission, wherein the epidemic model and the heat transfer model are the most commonly used [9]. However, few studies have considered the psychological process of how emotions are transmitted from one person to another based on the theory of emotion social sharing [12,13] and emotional contagion [20]. This study considered three aspects of emotion: diffusion, convergence, and accumulation.

Emotional arousal is the reason for sharing stories, news, and information. High emotional arousal strengthens an emotional experience and has high emotional intensity [14]. Emotions with the same valence that are transmitted with a higher level of intensity will cause broader transmission. When people fiercely express their emotions, they are easily noticed by others and have an increased level of exposure, which enables their emotions to be transmitted easily among group members [15]. Thus, emotions within a group are always transmitted from high-intensity members to low- intensity ones, in line with the heat transfer model [10].

In face-to-face situations, collective emotion is mainly transmitted by emotional contagion [5,16–18]. Individuals in a crowd will automatically imitate others’ facial expressions, intonations, gestures, actions, and more to acquire the emotions of others because of the activation of mirror neurons [19]. The emotional contagion of a pair of individuals has been widely studied [20,21]; however, little attention has been paid to emotion transmission among group members, which is more complicated than transmission between two individuals.

When a person joins a group, he/she is influenced by other members’ emotions. This process happens interactively among many group members [22]. Transmitters express their emotions via their expressions, voice, tones, and gestures [23]. In turn, receivers’ emotional feedback affects transmitters’ emotional state. Consequently, an emotion cycle is formed between transmitters and receivers [24]. This emotion cycle enables repetition and intensification of emotion within the group. The end result is that the emotion cycle drives the collective emotion to homogenization [25]. Therefore, we proposed the following hypotheses:

  • H1: When the homogeneity of collective emotion is low, collective emotion will be transmitted from members with strong negative emotions to members with weak negative emotions, and it will be gradually distributed.

  • H2-a: When negative collective emotion has low intensity and low homogenization, group members will achieve emotional convergence through emotional contagion.

  • H2-b: When negative collective emotion has low intensity and low homogenization, negative responses from others will induce collective emotion in oneself, which means group members will achieve emotional convergence through an emotion cycle among the group members.

  • H3: When negative collective emotion has high intensity and high homogenization, emotional contagion and an emotion cycle will not continuously strengthen the collective emotion; however, the emotion intensity will not be weakened.

In sum, we examined how emotions are transmitted from some members to the whole group in face-to-face situations. We induced negative emotions in groups to conduct an exploratory investigation of the emotional transmission of collective emotions. This exploration provides novel insights into the understanding of collective emotions.

Materials and methods

Participants

Eighty junior grade-one students and 78 junior grade-two students were randomly recruited from a middle school in Beijing, China; of these, 78 were boys and 80 were girls. Their ages ranged from 12 to 15 years, with a mean age of 13.20 years (SD = 0.651).

All participants provided written informed consent in accordance with the Declaration of Helsinki. This study was also conducted in accordance with the recommendations of the guidelines of the Human Research Ethics Committee of Capital Normal University, and written informed consent was obtained from all participants’ parent/guardian. The protocol was approved by the Human Research Ethics Committee of Capital Normal University.

Material

Emotion stimuli

The material was based on a real event—a public press release: “An Asian country’s media insists that movable-type printing was invented by them.” This collective event is not personally relevant; however, it had symbolic meaning for collective self-esteem. Researchers collected and synthesized relevant reports on this issue from national and international media. They then distilled these reports into a news item consisting of about 1,000 Chinese characters (S1 File).

Emotion ratings

Participants were presented with eight emotions (sad, happy, angry, disgusted, satisfied, surprised, excited, and calm) [26]. They were asked, “As a Chinese person, to what extent do you feel each of the following emotions?” They responded on seven-point scales, ranging from not at all to very much. Factor analyses revealed that angry and disgusted formed the negative emotion dimension (Cronbach’s α of .804), while happy, satisfied, and excited formed the positive emotion dimension (Cronbach’s α of .842).

Emotional contagion ratings

Participants were asked to rate the extent to which they perceived anger and disgust from transmitters’ language, facial expressions, actions, and intonations when communicating the news material, from 1 (not at all) to 7 (very much). After the factor analysis, angry and disgusted were combined into one dimension of negative emotional contagion. The Cronbach’s αs of language, facial expression, and actions ranged from .894 to .935.

Transmission feedback ratings

One item with two possible emotions was used to test participants’ emotion perception of their counterparts when they transmitted emotions as a transmitter and when they received emotions as a receiver. They were asked, “When you expressed your emotions of this news to him/her, what was his/her emotional reaction?” They were asked to rate this perception from 1 (not angry/disgusted at all) to 7 (very angry/disgusted). The Cronbach’s α of this item was .902.

Procedure

Experiments were conducted in four groups, with 35–40 participants in each group. The procedure is shown in Fig 1. First, all participants evaluated their emotional baseline. The pilot study showed that, in classroom conditions, participants tended to transmit their emotions to 2.33 people (SD = 0.637), ranging from 0–8 people. The experimenter randomly assigned one-third of participants to be transmitters. Then, the transmitters read the emotion induction material, while the other members of the group—the receivers—quietly waited. After reading, transmitters were asked to complete self-evaluations of their emotions. The experimenter informed the participants that he/she will leave the room; then, they would have 10 minutes to communicate with each other. The transmitters communicated both the information and their feelings about the reading material freely to the receivers for a maximum of 10 minutes. The receivers also expressed their opinions and feelings to the transmitters. Finally, the experimenter returned to the room, and all participants rated their emotions.

Fig 1. Collective emotion transmission procedure of group members.

Fig 1

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After the evaluation, participants were debriefed and their cooperation acknowledged with a compensation equivalent to 1.5 USD.

Data analysis

There were 57 transmitters and 101 receivers. Because four receivers provided less than 50% of response items, we deleted their data. Thus, the data of 57 transmitters and 97 receivers were considered in further analyses. We conducted all the data analyses with SPSS 16.0 (IBM, Armonk, NY, USA).

The average of angry and disgusted ratings was computed as the indicator of negative emotion, and the average of happy, excited, and satisfied ratings was computed as the indicator of positive emotion. We compared the emotional changes in the receivers after receiving the emotion and the emotional convergence of the receivers after receiving the emotion using paired sample t-tests.

The collective emotional convergence of receivers before and after transmission needed to be compared. Previous studies mostly adopted correlation coefficients as an indicator of emotional convergence—where a positive correlation refers to convergence and a negative correlation refers to divergence [27–29]. However, we adopted the coefficient of variation as the indicator of collective emotional convergence. The mean reflects the intensity of collective emotion, while the standard deviation reflects dispersion. Thus, the coefficient of variation (standard deviation/mean) can reflect collective emotional convergence—with a smaller coefficient indicating high homogeneity and, therefore, high convergence, and a larger coefficient indicating low homogeneity and, thus, low convergence [30]. We compared receivers’ emotional baselines and their collective emotion after transmission. To determine the effect of the emotional contagion on collective emotion transmission, we tested group members’ perceived emotional contagion of other members, using both verbal and nonverbal cues (language, facial expression, action, and intonation); then, we conducted correlation analyses with their emotion intensity changes after transmission.

Results

Manipulation check

The results of the paired sample t-tests indicated that negative emotion after inducement was significantly higher than at baseline (t(56) = 13.453, p < .05) and positive emotion after inducement was significantly lower than at baseline (t(56) = 8.866, p < .05). The emotion levels at baseline, after inducement, and after transmission are shown in Table 1. Emotion-inducing material significantly induced the negative emotions of transmitters and reduced their positive emotions.

Table 1. Participants’ emotion levels at baseline, after inducement, and after transmission (N = 154).

Transmitters Receivers
Mean SD COV Mean SD COV
Baseline Negative emotion 2.105 1.546 .734 1.799 1.357 .754
Positive emotion 3.661 1.768 .483 3.570 1.541 .432
After inducement Negative emotion 5.983 1.620 .271 - - -
Positive emotion 1.310 .757 .578 - - -
After transmission Negative emotion 5.600 1.752 .313 4.933 2.236 .453
Positive emotion 1.599 1.334 .834 1.701 1.256 .738
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COV: coefficient of variation, SD: standard deviation.

Collective emotion transmission

Emotion diffusion

The results showed that receivers’ negative emotions after transmission were significantly more intense than at baseline (t(96) = 11.947, p < .05; Table 1). Transmission of negative emotion from transmitters to receivers significantly induced receivers’ negative emotion. As shown in Fig 2, after transmission, receivers’ positive emotion significantly decreased and negative emotion significantly increased.

Fig 2. Transmitters’ and receivers’ changes in emotion before and after transmission.

Fig 2

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Furthermore, the coefficient of variation of receivers’ negative emotion after transmission was smaller than at baseline. As shown in Fig 3, after transmission, receivers’ emotions indicated obvious convergence compared to baseline levels. This implies that emotion transmission promotes emotional convergence within group members.

Fig 3. Emotional convergence of transmitters and receivers before and after transmission.

Fig 3

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In conclusion, following emotion transmission from transmitters to receivers, negative emotion flowed from high- to low-intensity members—enabling negative emotion diffusion in the whole group and leading to reaction convergence of group members.

Influence of emotional contagion on emotion after transmission

The results indicated that the participants’ emotion intensity changes were significantly and positively correlated with their perception of their counterparts’ language, actions, facial expressions, and intonations (rlanguage = .472, p < .01, raction = .522, p < .01, rfacial expression = .509, p < .01, and rintonation = .572, p < .01).

Taking receivers’ negative emotion after transmission as the dependent variable, the transmitters’ language, actions, facial expressions, and intonation contagion were used as predictor variables. The results showed that equation of transmitters’ emotional contagion on receivers’ negative emotion after transmission was significant (R2 = .352, F(4,78) = 10.587, p < .001). During transmission, language contagion negatively predicted emotion after transmission (β = —.120, p < .05); action contagion positively predicted emotion after transmission (β = .105, p < .05); facial expression contagion positively predicted emotion after transmission, but not significantly (β = .07, p > .05); and intonation contagion significantly positively predicted emotion intensity after transmission (β = .171, p < .05). To summarize, when transmitters transmit emotion to receivers, the stronger the emotion as expressed by the transmitters’ intonation, the stronger the emotion for the receivers after transmission.

In contrast, emotion changes of transmitters after transmission were not significantly related to emotional contagion (p > .05). This means that, among transmitters, emotional contagion had no influence on their emotion changes after transmission.

Influence of emotion feedback on emotion after transmission

Participants’ feedback was significantly positively correlated with receivers’ emotion changes after transmission (r = .525, p < .01), indicating that the stronger the emotion intensity of feedback, the stronger the negative emotion of receivers after transmission. However, such a correlation was not significant for transmitters (p > .05).

We developed a regression equation using participants’ feedback as the predictor variable and receivers’ emotion after transmission as the dependent variable. The results indicated that the equation of participants’ negative feedback and negative emotions of receivers after transmission was significant (R2 = .327, F(1, 87) = 42.288, p < .001). Transmitters’ negative emotional feedback significantly positively predicted the negative emotion of receivers after transmission (β = .981, p < .001). This indicates that the more negatively the counterparts expressed their emotions, the stronger the negative emotion of the receivers after transmission (Fig 4).

Fig 4. Influence of emotion feedback on transmitters’ and receivers’ negative emotion after transmission.

Fig 4

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Discussion

This study created an offline, face-to-face situation of middle school students’ collective emotion transmission and induced negative collective emotion to explore the collective emotion transmission model. The results showed that, after emotional induction, transmitters’ negative emotions were significantly higher than at baseline and compared to those of receivers. After transmission, the negative emotions of receivers were significantly higher than at baseline. The emotional contagion of transmitters, including language, facial expressions, intonations, and actions, positively predicted receivers’ emotional intensity. Transmitters’ feedback positively predicted receivers’ emotion intensity after transmission, indicating that stronger feedback was associated with more intense negative emotions among the receivers.

The results also revealed that in collective emotion transmission, emotion first flowed from high-intensity members (transmitters) to low-intensity members (receivers). In this procedure, transmitters’ verbal and nonverbal emotional cues significantly influenced receivers’ emotional intensity after transmission. The fiercer the participants’ expression of their emotions, the more intense the negative emotions of the receivers. Emotion diffusion and contagion both promoted the negative emotion intensity in the group, which eventually led to convergence. Further, the more negative the feedback that participants received during transmission, the stronger the receivers’ negative emotions. This indicates that emotion feedback played an emotion-strengthening role during this transmission.

Collective emotion transmission model

After emotional induction, transmitters’ negative emotions were significantly higher than at baseline and compared to those of receivers. After transmission, receivers’ negative emotions were significantly higher than at baseline. These results support Hypothesis 1, indicating that collective emotion gradually transmits from high-intensity members (transmitters) to low-intensity members (receivers). Although receivers’ negative emotions are induced, the emotion intensity of transmitters shows no obvious change after transmission. Emotional energy flows from transmitters to receivers until the whole group’s emotion converges.

Rime’s social sharing theory of emotion supports our results [31]. When an intense emotional event affects a given individual, numerous members of this person’s group are informed of it [32]. This emotion diffusion procedure is called the flow effect. Collective emotion transmission in offline situations not only demonstrates this flow effect, but emotional contagion also promotes emotion homogeneity.

The emotional contagion of transmitters, including language, facial expressions, intonations, and actions, positively predicted receivers’ emotional intensity. This result supports Hypothesis 2. In addition to the direct verbal transmission from transmitters, nonverbal emotion information also significantly influenced receivers’ emotional intensity. Transmitters use both verbal (speech and words) and nonverbal (facial expression, actions, intonations, etc.) means to transmit emotion [33]. People can be unconsciously influenced by others’ nonverbal information [34] and express emotions similar to those of others [35]. This effect is especially obvious in face-to-face situations [26]. In conversations and in face-to-face interactions, people automatically and continuously mimic and synchronize their movements with the facial expressions, voices, postures, movements, and instrumental behaviors of others [36]. Thus, when both verbal and nonverbal means of expression are used, group members continuously observe and feel other members’ emotions, which leads to collective emotional homogeneity.

People’s emotional experiences are affected by others’ feedback [25,37,38]. Our results indicated that transmitters’ negative feedback positively predicted receivers’ emotion intensity after transmission, which is consistent with Hypothesis 3. The more negative the emotion embodied in the feedback, the more negative the receivers’ emotion after transmission. When receivers transmit negative emotion to others, the more negative the emotion in the feedback, the stronger the negative emotion of the receivers. This cycle drives receivers to express stronger negative emotion to others. This cycle also coincides with Lishner and colleagues’ viewpoint of emotional contagion [39]. Under the influence of such interactions, group members’ negative emotions are continuously transmitted within groups and strengthened repeatedly during this procedure, thus forming collective emotion with certain intensity [25,39].

During negative collective emotion transmission, only receivers were influenced by emotion flow, emotional contagion, and emotion cycle effects, and these three effects did not have a significant effect on transmitters. Transmitters’ emotion intensity did not demonstrate a significant change after transmission, and there was no obvious change regarding emotional convergence. This result shows that, when these effects gradually fade and the collective emotion within a group has high intensity and homogeneity, the transmission of emotion will stop. When transmitters’ emotion has high intensity and convergence, the effects of emotion flow and contagion are non-significant. In contrast, when receivers’ emotion initially has low intensity and convergence, emotion flow and contagion can continuously influence emotional intensity and gradually lead to emotional convergence.

Limitations and further research

First, this study adopted a self-designed questionnaire to measure the dependent variables. More approaches can be applied to verify our results, such as behavioral observations and coding emotion transmission. Second, some researchers believe that emotional convergence has two paths: emotion-contagion-based and perception-analysis-based [20]. This study only controlled for group members’ cognition homogeneity, without controlling the emotional contagion influence during transmission. Further studies should consider both emotional and cognitive factors to clarify collective emotion transmission: for example, the interaction effects of emotional contagion and group identification on emotion transmission. Third, we explored the transmission model in a cluster environment; however, transmission of collective emotion in real-life situations does not always happen in such an environment. When group members cannot interact continuously with other group members, the effect of emotional contagion will be weakened. Other transmission features and their effects will also change. Further studies should examine the actual situations in which collective emotions occur, such as cyber collective emotions.

Conclusions

This study explored middle school students’ negative collective emotion transmission models in face-to-face situations by creating an offline transmission environment. The results revealed that the negative collective emotion transmission model consisted of emotion diffusion, contagion, and accumulation. Negative emotion was transmitted from high- to low-intensity members. Collective emotion achieved homogeneity through emotional contagion and accumulated power from a continuous emotion cycle. With the strengthening effect of feedback, it finally took form of a collective emotion with certain behavioral drives. This model elucidates collective emotion transmission and enriches the research on collective emotion.

Supporting information

S1 File. Emotion assessments and pilot study.

(DOCX)

Click here for additional data file. (14.9KB, docx)
S1 Fig. Participants’ mean emotional intensity among four emotion-eliciting materials.

(TIF)

Click here for additional data file. (149.3KB, tif)

Acknowledgments

The authors thank Ms. Xiru Liu and Ms. Anting Lai for their assistance with the experiments and valuable discussion.

Data Availability

The datasets generated and analysed during the current study are available on the Open Science Framework (osf.io/xhjyf/).

Funding Statement

National Social Science Foundation Grant number: BBA160046.

References

  • 1.Ellsworth PC, Scherer KR. Appraisal processes in emotion In: Davidson RJ, Scherer KR, Goldsmith HH, editors. Handbook of affective sciences. New York, NY: Oxford University Press; 2003. pp. 572–595. [Google Scholar]
  • 2.Goldenberg A, Halperin E, Zomeren MV, Gross JJ. The process model of group-based emotion: integrating intergroup emotion and emotion regulation perspectives. Pers Soc Psychol Rev. 2016;20(2):118–141. 10.1177/1088868315581263 [DOI] [PubMed] [Google Scholar]
  • 3.von Scheve C, Ismer S. Towards a theory of collective emotions. Emo Rev. 2013;5(4):406–413. 10.1177/1754073913484170 [DOI] [Google Scholar]
  • 4.Schweitzer F, Garcia D. An agent-based model of collective emotions in online communities. Eur Phys J B. 2013;77(4):533–545. [Google Scholar]
  • 5.Kramer AD. The spread of emotion via Facebook. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2012;767–770. ACM. 10.1145/2207676.2207787 [DOI]
  • 6.Overbeck JR, Neale MA, Govan CL. I feel, therefore you act: intrapersonal and interpersonal effects of emotion on negotiation as a function of social power. Organ Behav Hum Decis Process. 2010;112(2):126–139. [Google Scholar]
  • 7.Peters BJ, Overall NC, Jamieson JP. Physiological and cognitive consequences of suppressing and expressing emotion in dyadic interactions. Int J Psychophysiol. 2014;94(1):100–107. 10.1016/j.ijpsycho.2014.07.015 [DOI] [PubMed] [Google Scholar]
  • 8.Tsai J, Bowring E, Marsella S, Tambe M. Empirical evaluation of computational fear contagion models in crowd dispersions. Auton Agent Multi Agent Syst. 2013;27(2):200–217. 10.1007/s10458-013-9220-6 [DOI] [Google Scholar]
  • 9.Yin Y, Tang W, Li W. Emotion modeling of crowd based on social network. App Res Comput. 2015;1:80–84. 10.3969/j.issn.1001-3695.2015.01.019 [DOI] [Google Scholar]
  • 10.Bosse T, Duell R, Memon ZA, Treur J, Wal CN. Agent-based modeling of emotion contagion in groups. Cog Computation. 2015;7(1):111–136. 10.1007/s12559-014-9277-9 [DOI] [Google Scholar]
  • 11.Zhao J, Dong L, Wu J, Xu K. Moodlens: an emoticon-based sentiment analysis system for Chinese tweets. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012;1528–1531. ACM. 10.1145/2339530.233972 [DOI]
  • 12.Rimé B. Emotion elicits the social sharing of emotion: theory and empirical review. Emot Rev. 2009;1(1):60–85. [Google Scholar]
  • 13.Rimé B, Mesquita B, Boca S, Philippot P. Beyond the emotional event: six studies on the social sharing of emotion. Cogn Emot. 199;5(5–6):435–465. [Google Scholar]
  • 14.Berger J. Arousal increases social transmission of information. Psychol Sci. 2011;22(7):891–893. 10.1177/0956797611413294 [DOI] [PubMed] [Google Scholar]
  • 15.Sullins ES. Emotional contagion revisited: effects of social comparison and expressive style on mood convergence. Pers Soc Psychol Rev. 1991;17(2):166–174. [Google Scholar]
  • 16.Barsade SG. The ripple effect: emotional contagion and its influence on group behavior. Admin Sci Q. 2002;47(4):644–675. 10.2307/3094912 [DOI] [Google Scholar]
  • 17.Sy T, Côté S, Saavedra R. The contagious leader: impact of the leader's mood on the mood of group members, group affective tone, and group processes. J Appl Psychol. 2005;90(2):295–305. 10.1037/0021-9010.90.2.295 [DOI] [PubMed] [Google Scholar]
  • 18.Smith-Crowe K, Warren DE. The emotion-evoked collective corruption model: the role of emotion in the spread of corruption within organizations. Org Sci. 2014;25:1154–1171. 10.1287/orsc.2014.0896 [DOI] [Google Scholar]
  • 19.Dapretto M, Davies MS, Pfeifer JH, Scott AA, Sigman M, Bookheimer SY, et al. Understanding emotions in others: mirror neuron dysfunction in children with autism spectrum disorders. Nature Neurosci. 2005;9(1):28–30. 10.1038/nn1611 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Parkinson B, Simons G. Affecting others: social appraisal and emotion contagion in everyday decision making. Pers Soc Psychol Bull. 2009;35(8):1071–1084. 10.1177/0146167209336611 [DOI] [PubMed] [Google Scholar]
  • 21.Bruder M, Hosmukhambetova D, Nerb J, Manstead AS. Emotional signals in nonverbal interaction: dyadic facilitation and convergence in expressions, appraisals, and feelings. Cog Emo. 2012;26(3):480–502. 10.1080/02699931.2011.645280 [DOI] [PubMed] [Google Scholar]
  • 22.Liu Z, Jin W, Huang Z, Chai Y. An emotion contagion simulation model for crowd events. J Comput Res. Devel. 2013;50(12):2578–2589 [Google Scholar]
  • 23.Ekman P. Facial expression and emotion. Am Psychol. 1993;48(4):384–392. 10.1037//0003-066x.48.4.384 [DOI] [PubMed] [Google Scholar]
  • 24.Falkenberg I, Bartels M, Wild B. Keep smiling! Eur Arch Psychiatry Clin Neurosci. 2008;258(4):245–253. 10.1007/s00406-007-0792-5 [DOI] [PubMed] [Google Scholar]
  • 25.Smith ER, Conrey FR. Agent-based modeling: a new approach for theory building in social psychology. Pers Soc Psychol Rev. 2007;11:87–104. 10.1177/1088868306294789 [DOI] [PubMed] [Google Scholar]
  • 26.Smith ER, Seger CR, Mackie DM. Can emotions be truly group level? Evidence regarding four conceptual criteria. J Pers Soc Psychol. 2007;93(3):431–446. 10.1037/0022-3514.93.3.431 [DOI] [PubMed] [Google Scholar]
  • 27.Sun J, Lu J. The relationship of social sharing of emotion and emotional convergence. Psychol Sci. 2009;32(4):843–845. [Google Scholar]
  • 28.Anderson C, Keltner D, John OP. Emotional convergence between people over time. J Pers Soc Psychol. 2003;84:1054–1068. 10.1037/0022-3514.84.5.1054 [DOI] [PubMed] [Google Scholar]
  • 29.Anderson C, Keltner D. The emotional convergence hypothesis: implications for individuals, relationships and cultures In: Tiedens LZ, Leach CW, editors. The social life of emotion. New York: Cambridge University Press; 2004. pp. 144–163. 10.1017/CBO9780511819568.009 [DOI] [Google Scholar]
  • 30.Brewer BB, Carley KM, Benham-Hutchins M, Effken JA, Reminga J. Exploring the stability of communication network metrics in a dynamic nursing context. Soc Netw. 2020;61:11–19. 10.1016/j.socnet.2019.08.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Rimé B, Paez D, Kanyangara P, Yzerbyt V. The social sharing of emotions in interpersonal and in collective situations: Common psychosocial consequences Emotion regulation and well-being. New York: Springer; 2011. [Google Scholar]
  • 32.Harber KD, Cohen DJ. The emotional broadcaster theory of social sharing. J Lang Soc Psychol. 2005;24(4):382–400. 10.1177/0261927x05281426 [DOI] [Google Scholar]
  • 33.Hoogendoorn M, Treur J, Van Der Wal CN, Van Wissen A. Modelling the interplay of emotions, beliefs and intentions within collective decision making based on insights from social neuroscience. In International Conference on Neural Information Processing 2010 Nov 22 (pp. 196–206). Springer, Berlin, Heidelberg.
  • 34.Neumann R., Strack F. “Mood Contagion”: the automatic transfer of mood. J Personal Soc Psychol. 2000;79(2):211–223. 10.1037//0022-3514.79.2.211 [DOI] [PubMed] [Google Scholar]
  • 35.Hoffman ML. How automatic and representational is empathy, and why. Behav Brain Sci. 2002;25:38–39. 10.1017/S0140525X02410011 [DOI] [Google Scholar]
  • 36.Hatfield E, Carpenter M, Rapson RL. Emotional contagion as a precursor to collective emotions In: von Scheve C, Salmela M, editors. Series in affective science. Collective emotions: Perspectives from psychology, philosophy, and sociology. New York, NY: Oxford University Press; 2014. pp. 108–122. 10.1093/acprof:oso/9780199659180.003.0008 [DOI] [Google Scholar]
  • 37.Scarduzio JA, Tracy SJ. Sensegiving and sensebreaking via emotion cycles and emotional buffering: how collective communication creates order in the courtroom. Manag Commun Q. 2015;29(3):331–357. [Google Scholar]
  • 38.Hareli S, Rafaeli A. Emotion cycles: On the social influence of emotion in organizations. Res Organ Behav. 2008;28:35–59. 10.1016/j.riob.2008.04.007 [DOI] [Google Scholar]
  • 39.Lishner DA, Cooter AB, Zald DH. Rapid emotional contagion and expressive congruence under strong test conditions. J Nonverbal Beh. 2008;32:225–239. 10.1007/s10919-008-0053-y [DOI] [Google Scholar]

Decision Letter 0

Zezhi Li

22 Apr 2020

PONE-D-20-06106

Exploring collective emotion transmission in face-to-face interactions

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study explored the collective emotion transmission in face-to-face interactions. The results show that the collective emotion transmission model consisted of emotion diffusion, contagion, and accumulation. Collective emotion was transmitted from high arousal members to low arousal members. There were a few elements of the manuscript the derail from its overall clarity and impact. These concerns and others are detailed below. A revision of these sections would improve the quality of this study.

1. In Conclusions line 357-358, the authors claimed that the collective emotion was transmitted from high arousal members to low arousal members. What kind of collective emotions? Should be very specific, negative? However, I don’t see any data to show how the arousal level was measured and which subject, how many subjects have a high or low arousal?

2. In the Introduction, the authors have 3 hypotheses, but in the Discussion section the authors didn’t talk about how their results support or reject each of hypothesis. It would be beneficial to the reader if the authors can discuss each of their hypothesis based on their findings.

3. This study adopted the coefficient of variation as the indicator of collective emotion convergence. In line 193-194, The author claimed, the coefficient of variation can better reflect collective emotion convergence. Why the coefficient of variation can better reflect collective emotion convergence? Any refs to support this?

4. Regarding the experiment procedure, how many people do transmitter talk to? Are the transmitters award of or were told they were transmitters?

5. In Results section line 206-207, t (56), t (55). Why the df of the two paired sample t-test is different?

6. In line 133-134, the author claimed, “The pilot study showed that this material can induce negative collective emotion of the participants.” However, there is no data support this. It would be great if the authors can include their data from the pilot study to back their claim.

7. In line 179 after deleting the missing data, what do you mean deleting missing data? I suppose it should be excluding subject who has missing data. What kind of data were missing? How many?

8. Regarding the subject, are the subject middle school student or high school student? In the Abstract it says middle school, however in other places it says high school student. Need to be accurate and consistent.

9. The figure legend should put under nether each figure and it is better to put figure within the main text so that it is easier to read.

10. There are some text formatting issues in line 142-143 (Cronbach’sα). Please correct.

Reviewer #2: COMMENTS

This study aimed to explore how emotions are transmitted from some members to the whole group in a face-to-face environment. Through a social event, 158 middle school students were induced to feel anger and disgust. The present study randomly assigned 1/3 of them as senders and others as receivers. Transmitters shared their feelings with the receivers, and the receivers then communicate with other group members. The results show that negative collective emotions are transmitted through the flow from high arousal members to low arousal members. It is converged through the role of emotional contagion. It is accumulated through the role of an emotional cycle, and in the process, feedback strengthens the intensity of emotion. This study shows that the mode of collective emotional transmission consists of three parts: emotional diffusion, contagion and accumulation. This model helps to understand the inherent characteristics of collective emotion transmission, enriches the study of collective emotion, and provides a theoretical reference for the monitoring and management of group events in the future.

Although this study is interesting, the following issues need to be addressed before it is accepted and published.

1. There are some typos and Chinglish expression in the whole manuscript. Please ask native speaker to help modify the language. For example, in line 54, “Collective emotqqions” is a very obvious spelling mistake. In line 142, “with Cronbach’sαof .804” is also a typo. There are still a lot of problems like this, please revise them carefully. In line 211 to 212,“Negative and positive emotion mean, standard deviation, and coefficient of variance before and after collective emotion transmission” is a very typical Chinglish expression. There are many similar expressions in the text, please modify them one by one.

2. “Much research has been performed on cyber collective emotions.” There is a lack of references here. Please add the related references.

3. The structure of the introduction is a little loose. In this part, the present study puts forward relevant hypotheses based on previous studies. But the two hypotheses are separated by several paragraphs, and at the end of reading, you will forget the previous hypothesis. It is suggested that a paragraph should be added at the end of the introduction to explain the purpose of this study and the scientific problems to be solved, which will be clearer.

4. With regard to the collective emotion induction, I am wondering whether it is beyond their cognition to ask middle school students between the ages of 12 and 14 to rate events with complex political and historical backgrounds?

5. In the part of data analysis, what are the criteria and basis for deleting data? This paper does not elaborate on this in detail.

6. Negative emotions include only anger and disgust, while positive emotions include happiness, excitement and satisfaction. Do you consider the problem of quantity mismatch? In addition, are there corresponding criteria and references for dividing positive and negative emotions?

7. The discussion section is not enough to discuss in detail whether the results are consistent or inconsistent with previous studies, and the relevant explanations. After a brief summary of the research results, the study gives the collective emotion transmission model, and then detailed the current results, but did not explain the relationship between this model and the results of this study. What I am very curious about is whether the results of this study support this model, or do not support this model, or can it be modified? I hope to see the corresponding discussion in the revised draft.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

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PLoS One. 2020 Aug 7;15(8):e0236953. doi: 10.1371/journal.pone.0236953.r002

Author response to Decision Letter 0

5 Jun 2020

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

________________________________________

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

________________________________________

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study explored the collective emotion transmission in face-to-face interactions. The results show that the collective emotion transmission model consisted of emotion diffusion, contagion, and accumulation. Collective emotion was transmitted from high arousal members to low arousal members. There were a few elements of the manuscript the derail from its overall clarity and impact. These concerns and others are detailed below. A revision of these sections would improve the quality of this study.

1. In Conclusions line 357-358, the authors claimed that the collective emotion was transmitted from high arousal members to low arousal members. What kind of collective emotions? Should be very specific, negative? However, I don’t see any data to show how the arousal level was measured and which subject, how many subjects have a high or low arousal?

Response: Thank you for your comment. We did not measure emotional arousal; rather, we measured emotional intensity. We have revised this throughout for additional clarity.

2. In the Introduction, the authors have 3 hypotheses, but in the Discussion section the authors didn’t talk about how their results support or reject each of hypothesis. It would be beneficial to the reader if the authors can discuss each of their hypothesis based on their findings.

Response: Thank you for your comment. We have revised the Discussion section accordingly; specifically, we included a summary of the results and the relationship between each result and our hypotheses (pages 15–17, lines 275–316).

3. This study adopted the coefficient of variation as the indicator of collective emotion convergence. In line 193-194, The author claimed, the coefficient of variation can better reflect collective emotion convergence. Why the coefficient of variation can better reflect collective emotion convergence? Any refs to support this?

Response: We appreciate your feedback. Collective emotion is the synchronous convergence of an emotion response across individuals toward a specific event or object. When group emotions arise, the emotional intensity of the whole group increases, and when group emotions spread, the homogeneity of the whole group rises. We measured both the intensity of negative group emotions using eight emotion items and the coefficient of variation as an indicator of emotional convergence. If two datasets have great dispersion, or if their scales differ, it is not appropriate to use standard deviation directly for comparison. In this case, the effect of measurement scale should be eliminated, which the coefficient of variation—the ratio of the standard deviation to the average of the original data—can accomplish. It is an absolute value that reflects the degree of discrete data. It is possible to directly compare the degree of emotion convergence of individuals in different groups. We have added a reference to support this point (Brewer et al., 2020; pages 9–10, lines 168–178).

4. Regarding the experiment procedure, how many people do transmitter talk to? Are the transmitters award of or were told they were transmitters?

Response: We measured the number of subjects that transmitters talked to in the pilot study; on average, participants talked to 2.33 people (SD = 0.637), ranging from 0 to 8 people. Since the pilot study was conducted with students from the same grade and school as the main study (but different classes), we considered the pre-experimental sample to be homogeneous with the formal sample. In addition, since they share a classroom, the transmitters have the potential to directly affect other groups members though both verbal and non-verbal communication, and there are many potential receivers. We also informed all participants that they could feel free to communicate with each other for a maximum of ten minutes after the transmitters had read the emotion-inducing material and completed their self-report. We did not emphasize the direction of the transmission, and the transmission was unrestricted. We have included some additional information in our Procedure section for clarity (starting on page 8, line 141).

5. In Results section line 206-207, t (56), t (55). Why the df of the two paired sample t-test is different?

Response: Thank you for noting this error. We have corrected it (page 10, line 188).

6. In line 133-134, the author claimed, “The pilot study showed that this material can induce negative collective emotion of the participants.” However, there is no data support this. It would be great if the authors can include their data from the pilot study to back their claim.

Response: We appreciate your feedback. For brevity, we decided not to include much information about the pilot study. Regardless, the manipulation check showed the material to be valid. Please note some additional information about our pilot study below:

In our pilot study, 40 participants (age range = 11 to 13 years) were enrolled in the pre-experiment. We used four materials to elicit emotions. All participants read all four materials and rated their emotions on the Positive Affect and Negative Affect Scale (five-point Likert scale). The results are shown in Supplemental Figure 1, which indicates that the material about movable type printing better evoked negative emotions than did the other materials (F(3, 111) = 5.837, p < .01).

Supplemental Figure 1. Participants’ mean emotional intensity among four emotion-eliciting materials

7. In line 179 after deleting the missing data, what do you mean deleting missing data? I suppose it should be excluding subject who has missing data. What kind of data were missing? How many?

Response: Thank you for your comment. Because four receivers provided less than 50% of all the response items, we deleted their data. We added this fact to our Data analysis section (page 9, lines 160–161).

8. Regarding the subject, are the subject middle school student or high school student? In the Abstract it says middle school, however in other places it says high school student. Need to be accurate and consistent.

Response: Thank you for noting this error. All participants were middle school students; thus, we revised the error (page 19, line 345).

9. The figure legend should put under nether each figure and it is better to put figure within the main text so that it is easier to read.

Response: We originally provided the figures in separate files owing to PLoS One’s guidelines. However, per your wishes, we have now included all four figures in the main text.

10. There are some text formatting issues in line 142-143 (Cronbach’sα). Please correct.

Response: Thank you for noting this error. We have revised this throughout.

Reviewer #2: COMMENTS

This study aimed to explore how emotions are transmitted from some members to the whole group in a face-to-face environment. Through a social event, 158 middle school students were induced to feel anger and disgust. The present study randomly assigned 1/3 of them as senders and others as receivers. Transmitters shared their feelings with the receivers, and the receivers then communicate with other group members. The results show that negative collective emotions are transmitted through the flow from high arousal members to low arousal members. It is converged through the role of emotional contagion. It is accumulated through the role of an emotional cycle, and in the process, feedback strengthens the intensity of emotion. This study shows that the mode of collective emotional transmission consists of three parts: emotional diffusion, contagion and accumulation. This model helps to understand the inherent characteristics of collective emotion transmission, enriches the study of collective emotion, and provides a theoretical reference for the monitoring and management of group events in the future.

Although this study is interesting, the following issues need to be addressed before it is accepted and published.

1. There are some typos and Chinglish expression in the whole manuscript. Please ask native speaker to help modify the language. For example, in line 54, “Collective emotqqions” is a very obvious spelling mistake. In line 142, “with Cronbach’sαof .804” is also a typo. There are still a lot of problems like this, please revise them carefully. In line 211 to 212, “Negative and positive emotion mean, standard deviation, and coefficient of variance before and after collective emotion transmission” is a very typical Chinglish expression. There are many similar expressions in the text, please modify them one by one.

Response: Thank you for your feedback. We have proofread our article thoroughly and sought the services of a professional English-language editing company.

2. “Much research has been performed on cyber collective emotions.” There is a lack of references here. Please add the related references.

Response: Thank you for pointing this out. We have added two relevant references: [4,5] (page 3, line 40).

3. The structure of the introduction is a little loose. In this part, the present study puts forward relevant hypotheses based on previous studies. But the two hypotheses are separated by several paragraphs, and at the end of reading, you will forget the previous hypothesis. It is suggested that a paragraph should be added at the end of the introduction to explain the purpose of this study and the scientific problems to be solved, which will be clearer.

Response: Thank you for your comment. We have added a paragraph at the end of the Introduction to enhance the flow of the paper and to clarify our hypotheses.

4. With regard to the collective emotion induction, I am wondering whether it is beyond their cognition to ask middle school students between the ages of 12 and 14 to rate events with complex political and historical backgrounds?

Response: We completely agree with your comments, and we considered students’ cognitive ability. The art of movable type printing is one of the four major inventions of ancient China, which is common knowledge among middle school students and even elementary school students in China. If any other country officially claimed that movable type printing was an invention of their country, it would likely anger or disgust Chinese individuals. Further, our manipulation check confirmed the validity of this material.

5. In the part of data analysis, what are the criteria and basis for deleting data? This paper does not elaborate on this in detail.

Response: Thank you for your comments. Because four receivers provided less than 50% of all the response items, we deleted their data. We have added this explanation in the Data analysis section (page 9, lines 160–161).

6. Negative emotions include only anger and disgust, while positive emotions include happiness, excitement and satisfaction. Do you consider the problem of quantity mismatch? In addition, are there corresponding criteria and references for dividing positive and negative emotions?

Response: We completely agree with your comments. We used eight emotion words to measure students’ emotional states: sad, happy, angry, disgusted, satisfied, surprised, excited, and calm. Per the results of the factor analysis, angry and disgusted formed the negative dimension, while happy, satisfied, and excited formed the positive emotion dimension. Ideally, the two would have been matched in the number of states.

7. The discussion section is not enough to discuss in detail whether the results are consistent or inconsistent with previous studies, and the relevant explanations. After a brief summary of the research results, the study gives the collective emotion transmission model, and then detailed the current results, but did not explain the relationship between this model and the results of this study. What I am very curious about is whether the results of this study support this model, or do not support this model, or can it be modified? I hope to see the corresponding discussion in the revised draft.

Response: We appreciate your comments and have revised the Discussion accordingly. Specifically, we have included a summary of the results, more thoroughly interpreted the meaning of the results, and compared our findings to those of several other theoretical and empirical studies (please see our revised Discussion starting on page 14, line 254).

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Attachment

Submitted filename: Response_to_Reviewers.docx

Click here for additional data file. (43.6KB, docx)

Decision Letter 1

Zezhi Li

17 Jul 2020

Exploring collective emotion transmission in face-to-face interactions

PONE-D-20-06106R1

Dear Dr. Zheng,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Zezhi Li, Ph.D., M.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have satisfactorily responded to all my questions and made the necessary changes to the manuscript. The revised version of the manuscript appears to be good.

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Acceptance letter

Zezhi Li

27 Jul 2020

PONE-D-20-06106R1

Exploring collective emotion transmission in face-to-face interactions

Dear Dr. Zheng:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Zezhi Li

Academic Editor

PLOS ONE

Associated Data

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    Supplementary Materials

    S1 File. Emotion assessments and pilot study.

    (DOCX)

    Click here for additional data file. (14.9KB, docx)
    S1 Fig. Participants’ mean emotional intensity among four emotion-eliciting materials.

    (TIF)

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    Attachment

    Submitted filename: Response_to_Reviewers.docx

    Click here for additional data file. (43.6KB, docx)

    Data Availability Statement

    The datasets generated and analysed during the current study are available on the Open Science Framework (osf.io/xhjyf/).

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