The prediction role of feeling of injustice on network social mobilization: The mediating role of anger and resentment

Jinghuan Zhang (Shandong Normal University, Jinan, China)
Shan Wang (Shandong Normal University, Jinan, China)
Wenfeng Zheng (Shandong Normal University, Jinan, China)
Lei Wang (Shandong Normal University, Jinan, China)

International Journal of Crowd Science

ISSN: 2398-7294

Article publication date: 16 July 2019

Issue publication date: 24 September 2019




By drawing on the research paradigm of collective action that occurs in physical space, the present study aims to explore the antecedent predictors of network social mobilization – feeling of injustice – and discuss the emotional mechanism of this prediction: mediating effect of anger and resentment.


Micro-blog postings about network social mobilization were collected to develop the dictionary of codes of fairness, anger and resentment. Then, according to the dictionary, postings on Sina Weibo were coded and analyzed.


The feeling of injustice predicted network social mobilization directly. The predictive value was 27% and 33%, respectively during two analyses. The feeling of injustice also predicted social mobilization indirectly via anger and resentment. In other words, anger and resentment account for the active mechanism in which the feeling of injustice predicts network social mobilization. Mediating effect value was 29.63% and 33.33% respectively.

Research limitations/implications

This study is our first exploration to use python language to collect data from human natural language pointing on micro-blog, a large number of comments of netizen about certain topic were crawled, but a small portion of the comments could be coded into analyzable data, which results in a doubt of the reliability of the study. Therefore, we should put the established model under further testing.

Practical implications

In the cyberspace, this study confirms the mechanism of network social mobilization, expands and enriches the research on social mobilization and deepens the understanding of social mobilization.

Social implications

This study provides an empirical evidence to understand the network social mobilization, and it gives us the clue to control the process of network social mobilization.


This study uses the Python language to write Web crawlers to obtain microblog data and analyze the microblog content for word segmentation and matching thesaurus. It has certain innovation.



Zhang, J., Wang, S., Zheng, W. and Wang, L. (2019), "The prediction role of feeling of injustice on network social mobilization: The mediating role of anger and resentment", International Journal of Crowd Science, Vol. 3 No. 2, pp. 155-167.



Emerald Publishing Limited

Copyright © 2019, Jinghuan Zhang, Shan Wang, Wenfeng Zheng and Lei Wang.


Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at

With the rapid development of internet technology, more and more persons are entering the cyberspace. Cyberspace, characterized by anonymity and rapid information dissemination, provides a new development field for social mobilization and makes social mobilization a new form of development. Social mobilization refers to the process in which social members are purposefully guided to participate actively in some major social activities (Wu, 2003), and it is also the process in which the society exerts influence on the thoughts and behaviors of its members (Gan and Luo, 2011). Network social mobilization is the social mobilization embodied in cyberspace (Liu, 2010). In addition to having the basic characteristics of physical social mobilization, network social mobilization has greater flexibility because of the relatively decentralized nature of cyberspace.

China is in her critical period of social transformation and rapid development. During this period, some people feel that they are unfairly treated and their interests are deprived because they are in disadvantaged groups and the rules are not so clear. In turn, a sense of deprivation and injustice is produced. This kind of sense in the real world would produce collective action under the condition that certain group has high collective efficacy (Xue et al., 2013; Yue et al., 2013; Ji and Cui, 2017), and would also produce some public topic in cyberspace, which in turn calls for the network social mobilization, through which the persons involved express their dissatisfaction and interest appeal.

Nowadays, network social mobilization events have occurred frequently, such as “the case of murder for humiliated mother”, “the case of murdered by roommate’s boyfriend in Japan”, “the Fuxi incident”, and “the case of photo-taken randomly for public welfare mobilization”. Considering the social influences of network social mobilization by observing these cases, they can be recognized as both positive and negative. On the one hand, network social mobilization makes the network event itself no longer just an individual concerns, but quickly gathers the voices and wishes of various network users from all sectors of society, which makes up for the defects of traditional media and makes the event more comprehensive and authentic. At the same time, individuals express their views and feelings from different perspectives, so that people have a more comprehensive understanding of the event. On the other hand, network social mobilization is often unpredictable or uncontrollable, as a news hotspot can be quickly commented and forwarded in a very short period of time, thus becoming a hot topic and being watched by more netizens. As news travels in the snowball-like speed, rationality sound may be amplified or overwhelmed.

Regarding the psychological role of netizens who participate in network social mobilization, it is also can be seen as social express in both of values and emotions. One is to express their pursuit of fairness and justice, which reflects the pursuit of their social values; The other is to express the resistance to injustice, which reflects their emotional expression. In other words, network social mobilization is not only a process of expressing opinions, but also a process of emotional expression and catharsis. Therefore, network social mobilization helps members of society to vent their emotions, to find their ideological ally and emotional resonance in broad cyberspace. As a result, network social mobilization may also spread certain unhealthy thoughts and opinions, or even just to vent one's own selfish and narrow-minded feelings, which intern pollute the network environment, cause the incidents that were originally small but becoming a major catastrophic events disrupting public security and shaking the foundation of social stability. Therefore, the study of network social mobilization may help us to monitor social emotions, understand the netizens' thinking methods, values, inner aspirations and expectations. Therefore, it will be a great help to establish a reasonable and effective early warning mechanism of network social mobilization, if we understand the mechanism of network social mobilization.

Network social mobilization

Different scholars have different views on the definition of network social mobilization. Some scholars believed that network social mobilization is a type of social mobilization that happened on the network. It is the process that the network users use the internet as a medium to publish and disseminate information, exerting influence on network participants and achieving their desired goals (Zhang and Zhou, 2007). Some scholars insisted that network social mobilization is the process through which netizens or political interest groups who are protected by the identity of netizens to manipulate other netizens to participate in the attention and evaluation of certain events, and its main characteristic is interaction and through which the netizens change their values and opinions (Li, 2010). Others stressed that network social mobilization is in-organization and non-leadership (Ding et al., 2006).

This study regarded that the network social mobilization is the process through which the network users were called for a particular issue, and participate in a process to interact each other to express their aspirations for justice and to exchange their emotions. The main characteristics is centered on a common theme – topic-centered rather than person or authority leader centered; The second characteristic is the convenience of internet connection, which makes social mobilization more extensive. Internet makes people far away from each other gather together to speak, inspire each other and even launch some campaign to against or support some viewpoint on a topic which calls common concerns. Therefore, it is very important to study the cause and mechanism of network social mobilization, and it will be a great help those who concern to guide the network social mobilization to a health direction.

Prediction of feeling of injustice on network social mobilization

Relative deprivation theory believes that when people perceive they themselves or similar persons are unequally treated or their rights or chances are deprived, they will be angary or even resentful and then will start some actions to change their situations (Crosby, 1976; Runciman, 1966). It was regarded that injustice is the cause of crowd behavior (van Zomeren, Postmes and Spears, 2008). Therefore, at the phenomenological level, negative social events cause widespread social mobilization. In fact, negative events can only lead to widespread social mobilization when accompanied with feeling of injustice.

Justice is a value judgment, also known as justice perception, including perceived justice and perceived injustice (Zhang et al., 2011). Studies have shown that the feelings of injustice and relative deprivation experienced by individuals in real life will promote their motivations of expression, pouring out, and venting. It is this kind of motivation that encourages people to participate in network social events or social mobilization (Yue and Xue, 2011). It is widely believed that when individuals encounter unfair events that threaten their belief in a just world, they are more proactive in participating in social mobilization that compensates for this injustice and upholds the belief of a just world. An empirical study shows that people with a just world belief are more inclined to set long-term goals. When the world's justice beliefs are threatened, they are more likely to invest in activities that restore the belief of a just world, for example, condemning person who undermined their justice world (Hafer, 2000). Therefore, we speculate that injustice can directly predict network social mobilization.

The mediating role of emotion in predicting network social mobilization

Social construction theory holds that people's dissatisfaction with social reality is more than a reaction to social reality, but a result of personal construction (Gergen, 1985; Harre, 1986). The later one is usually accompanied with certain emotions, just as commented by some researchers that perceive social injustice or unfairness is not only a cognitive concept, but also a kind of feeling or emotion (Kawakami and Dion, 1995; Leach et al., 2002). Then, emotional stimulation is often the premise and basis of social mobilization (Yang, 2013). Relative Deprivation Theorists suggests that it is the emotional response that drives people to act in crowed actions (van Zomeren et al., 2008).

Emotion is a basic psychological activity of human beings, which can be divided into positive and negative emotions according to their valence. Negative emotions have a more profound effect than positive ones (Baumeister et al., 2001), and negative emotions spread quicker than positive emotional (Joiner, 1994; Katz et al., 2011). This is because human beings have prioritized differences in resource allocation. A large number of studies have found that the organism shows preferential effects in psychological processing and behavioral response to unpleasant, especially threatening stimuli (e.g. violent, bloody, fierce animals and angry expressions) (Hansen and Hansen, 1988; Pourtois, et al., 2004; Pourtois, et al., 2006). That is to say, negative information is easy to receive priority attention, and negative emotions spread more easily and evoke resonance (Huang and Luo, 2009; Sui and Li, 2012; Xu, 2017; Zhou and Hu, 2015).

Anger is a basic emotion of human beings, and also a typical negative emotion. It is more easily perceived by people and more likely to resonate among individuals. Also, anger emotions are more likely to infect, promote people to form a common social cognition and emotional state, which in turn leads to group violations (Mackie et al., 2000). In the Double-pathway Model of crowded behavior, the widespread emotional pathway is the role of anger. Theoretic analysis and empirical studies of psychology have shown that, individual experience feelings of injustice and anger, which can encourage the individuals to choose to participate in the crowd behavior (Shi et al., 2014; Tausch et al., 2011; van Zomeren et al., 2004). The mediating role of anger in crowd behavior has been confirmed by various research scenarios (Shi and Cui, 2016; Xue et al., 2013). However, does anger play a mediating role in predicting network social mobilization by the feeling of injustice? This remains to be confirmed.

This study will further explore other emotions that are closely related to the feeling of injustice, such as the role of resentment in predicting network social mobilization. Resentment is a comparative survival experience based on social inequality. It is a “powerless” or “incompetent” that is caused by its own survivability after being compared with others. The responsibility for injury is attributed to others, and then an emotional experience of dissatisfaction, disappointment, hatred, pain, resentment, etc. is generated in the heart. This kind of experience is always seeking various irrational expressions under the stimulation of the outside world (Nietzsche, 1992; Scheler, 1997; Sun, 2012; Tenhouten, 2008). It is the accumulation of anger that is hidden in the heart. Resentment is primarily an individual emotional experience, but the performance and outcomes in social situations are often social, that is, behaviors driven by resentment are often group-based. Since resentment can promote the occurrence of group behavior, the feeling of injustice in the network situation also predict network social mobilization through the intermediary role of resentment? Which kind of emotion is more mediating than anger? This is the third problem to be solved in this study.

In summary, through the mining of real data of Weibo, this study explores:

  • feeling of injustice positive predicts the network social mobilization;

  • anger and resentment mediates the prediction of felling of injustice on network social mobilization.


Research procedure

The data of this study come from the comments of netizens on common topics in the natural state. The common topics include: “the case of murdered by roommate’s boyfriend in Japan” and “Nanny Arson in Hangzhou”. The evaluated materials are the comments, forwards and original Weibo text messages published under common topics. The text information is captured by the Python language. In the “the case of murdered by roommate’s boyfriend in Japan”, this study selects the comments, forwards, and comments + forwarding under the blog post published by Jiangge's mother on December 11, 2017 (“404 days, finally wait until the trial of the demon Shifeng Chen!”). As of the time when the researchers grabbed the data, there were 9614 comments and 2464 articles were forwarded. In addition, we selected about 1000 original micro-blogs under the Weibo super topic “Jiangge, an overseas student, was killed” and “the case of Jiangge was murdered”. In the “nanny arson case in Hangzhou”, 10,000 comments and forwarding + comments posted under the victim's family on January 8, 2018 are selected. In addition, we selected about 700 original micro-blogs on the micro-blogging super topic “Nanny arson”. All data is selected until the researcher grabs the data.

After grabbing the data, the researchers segment all text using the Jieba Segment (Python language), and then used the self-compiled or already compiled vocabulary to score the word segmentation results.

Finally, statistical analysis was performed on the data using SPSS 17.0.

Variable measurement

The feeling of injustice. This study uses the self-compiled “unfair vocabulary” to evaluate the unfairness of the collected speech.

This study selected two hot micro-blogs of CCTV official micro-blog on “the case of murder for humiliated mother”:

  1. tracking! Attention! The Supreme People’s Procuratorate (SPP) and the public security law of Shandong province have jointly responded to “the case of murder for humiliated mother” and Liaocheng where an official working group was set up.

  2. the latest news of the “the case of murder for humiliated mother”! The Supreme People's Procurator (SPP) investigated whether the police had failed in their duty and malfeasance and the Shandong high court accepted the appeal.

Use Python language to crawl all comments under the original micro-blog for analysis.

After preliminary cleaning of the invalid and duplicate data, the researchers received 7455 comments. Using Jieba Segment technology, 13,506 words were obtained from each comment content, and word frequency statistics were carried out, which were presented from high to low according to the frequency of occurrence.

The researchers first selected a large number of words that related to the case but not related to the feeling of injustice, such as “mother” “smile” “kill” and “channeling” and got 212 words that could express the feeling of justice or injustice. The 212 words were then screened by a PhD student in psychology and a psychologist who removed ambiguous words such as “suspect” and “unrighteous” resulting in 132 words expressing a feeling of justice or injustice. The 132 words were made up of words such as “just” and “all men are equal” that describe a feeling of justice, and neutral words such as “law” and “trial”, and words such as “unjust” and “officialdom” that describe a feeling of injustice.

Eight graduate students in psychology were then asked to rate on a scale of 1 to 4 for perceived feelings of justice/injustice. The assignment criteria for feeling of injustice are:

  • witness the subjective feelings brought by the occurrence of general unfair events (e. g. the feeling of injustice will be generated when seeing others' of bullied);

  • the individual who consciously performs the social contract condemns the behavior of the individual who violates the social contract and feels the subjective feeling of inner imbalance (for example the individual who consciously lines up will feel injustice when he/she is forced to jump the queue by others);

  • subjective feelings of deprivation arising from unequal distribution (e.g. unequal pay for the same work creates a feeling of injustice);

  • subjective feelings caused by the negation of the law or justice (e.g. the crime of indecency against children is ambiguous and the punishment is too light which makes people feel unfair).

Correspondingly the assignment criteria for the feeling of justice and neutral words are:

  • the subjective feelings brought by witnessing the occurrence of general justice events;

  • the subjective feelings of consciously performing the social contract or witnessing others to perform the social contract;

  • subjective feelings caused by the fairness of distribution;

  • subjective feelings triggered by affirmation of law or justice.

For words expressing feeling of injustice, raters were asked to directly rate the degree to which the words expressed feeling of injustice. For the words expressing the feeling of justice and neutral words, the raters will first rate the degree of the feeling of justice that the words can express and then the researchers will transcribe the results to obtain the score of these words in the expression of the feeling of injustice. Finally, the mean score of each word was calculated.

The list of unfair vocabulary with 132 vocabulary words. The vocabulary score ranged from 1.25 to 3.75, fairness = 1.25 and false/false case = 3.75. The internal consistency of the vocabulary is 0.789.

Anger. This study used the “anger vocabulary” to evaluate anger. The vocabulary is selected from Dong et al. (2015) Weibo Five Basic Mood Lexicon (Weibo-5BML) based on the theory of emotion-based classification structure. The vocabulary of anger was then scored by eight psychology masters. Finally, the average score of each word is calculated. The internal consistency of the scale is 0.693.

The word list ranges from 1.25 to 3.88 among which irritating = 1.25 and heartless = 3. 88. Calculate the total score of angry words in each Weibo user's blog posts the higher the score the stronger the anger.

Resentment. Resentment is a compound emotion, and its expression is more subtle than anger. Therefore, it cannot be measured simply by segmenting the captured Weibo content and then matching the vocabulary.

The researchers read and analyzed the micro-blog content one by one to determine whether it conveys resentment. Then the selected comment sentences were sent to 8 graduate students majoring in psychology and the resentment expressed in the sentences was scored on a scale of 1- 4. The scoring criteria are:

  1. strong dissatisfaction and disgust;

  2. dissatisfaction with the world has nowhere to vent disappointed in the world then hate;

  3. there is no place to express the individual's hatred, and he feels psychological pain, which leads to diffuse resentment;

  4. a strong hatred of everything that happens in the world a belief that everything is unfair a feeling of extreme disappointment then curse.

Finally calculate the average score of each sentence.

The resentment score ranged from 1.63-4, with an average score of “I hope there is some justice in the society, but not pollution” of 1.63, and “now the law does not punish the nine ethnic groups, or I really hope to punish the nine ethnic groups of murderers” of 4. The internal consistency reliability of the score is 0.893, In the study the higher the resentment score, the stronger the resentment.

Network social mobilization. This study borrows the classification of network crowd behavior by Bnmsting et al. (2002), and divides the network social mobilization into three types according to the degree of participation and willingness to work: Comment = 1; Forward + Comment = 2; Original micro-blog with topic = 3. The lower the score, the weaker the participation of the network society. The higher the score, the stronger the participation of the network society.


Multicollinearity diagnosis. VIF < 1.2 for all predictors was tested, so there was no serious multicollinearity problem.

Two measurements were taken on the collected data. The first measurement was a text that simultaneously expressed injustice-angry-network social mobilization, a total of 116 sample. The second measurement also expressed the feeling of injustice – resentment – the text information of the network social mobilization, a total of 151.

Anger mediated the relationship between the feeling of injustice and network social mobilization

The statistical results of the description of the feeling of injustice-angry-network social mobilization data are as follows:

As can be seen from Table I, the feeling of injustice and anger are positively related to network mobilization, and the feeling of injustice is positively related to anger. In accordance with the preconditions of the mediation effect test, the anger mediation test can be further tested.

To test whether anger mediated the relationship between the feeling of injustice and network social mobilization, we computed a series of regression analyses using three steps by Baron and Kenny (1986). First, it is necessary to test whether the direct path from the independent variable to the dependent variable is significant. Therefore, network mobilization should be taken as the dependent variable and the feeling of injustice as the independent variable for regression analysis. The results show that the feeling of injustice has a significant positive predictive effect on network social mobilization (β = 0.27, p <0.01), which is consistent with the conditions of regression and mediation test (Figure 1).

As can be seen from Figure 1, the feeling of injustice is predicting the network social mobilization, and the feeling of injustice is predicting anger. Anger is predicting the network social mobilization. After controlling for anger, the predictive effect of feeling of injustice on the network mobilization is still significant, indicating that the mediating role of anger is significant. The mediating effect value was 0.08, and the mediating effect accounted for 29.63 per cent of the total effect.

The mediating effect of resentment on the prediction of network mobilization in the feeling of injustice

The statistical results of the description of the feeling of injustice-resentment-network social mobilization data are as follows:

As can be seen from Table II, the feeling of injustice and resentment are positively related to network mobilization, and the feeling of injustice is positively related to resentment. In line with the precondition of mediation test, mediation analysis can be carried out.

The method of testing the mediating effect of resentment is the same as above, and the result is shown in Figure 2.

As can be seen from Figure 2, the feeling of injustice is predicting the network social mobilization, and the feeling of injustice is predicting resentment. Resentment is predicting the network social mobilization. After controlling for resentment, the predictive effect of the feeling of injustice on the network mobilization is still significant, indicating that the mediating role of resentment is significant. The mediating effect value was 0.11, and the mediating effect accounted for 33.33 per cent of the total effect.


The predictive effect of injustice on network social mobilization

This research focused on network social mobilization occurred in today’s Chinese society, explored the psychological mechanism that leads to the network social mobilization by analyzing the data from Sina Weibo – a wide used social network. The results showed that feeling of injustice significantly predicted network social mobilization, and the first hypothesis was supported.

The mediating role of anger and resentment in prediction of feeling of injustice on network social mobilization

This study examined the implications of anger and resentment for network social mobilization. The results showed that both anger and resentment significantly predicted network social mobilization and played a mediating role in the impact of injustice on network mobilization. These findings confirmed that anger plays a mediating role not only in the crowd behavior – in the physics space accompanied by the group efficacy (Double Pathway Model of crowd behavior), but also in the network social mobilization in the cyberspace This maybe because venting anger in group behavior is one of the important drivers of individual involvement (Leonard et al., 2011; Livingstone et al., 2011; Shepherd et al., 2013), designed to eliminate or reduce the feeling of deprivation caused by injustice. This result may suggest that anger plays the similar role both in physic space and in cyber space.

The present study also expended the emotional pathway by introducing resentment into the model, and showed that resentment played a mediating role in the relationship between feeling of injustice and network social mobilization, and the direct effect and mediating effect of resentment were greater than anger. Value-added theory argued that resentment is a catalyst for people to participate in collective action (Smelser, 2006), and the purpose of participating in social mobilization is to change the status quo of life (Xu, 2013). As regard to the higher prediction value (β number), it maybe the reasons that resentment is a compound emotion including anger, and is more likely to connect individuals who are not related, so that even the most heterogeneous components could quickly condense into a group of “sharing a bitter hatred of the enemy” (Wang, 2015) in the cyberspace. Is this a special role of resentment for network social mobilization? May it be generalized into social mobilization in physics space? Further empirical studies are needed.

Theoretical and practical significance

The present study is a pilot explore on the subject of network social mobilization, and speculated a potential mechanism of network social mobilization under the framework of crowed behavior, and found that feeling of injustice directly predicted network social mobilization, and predicted network social mobilization indirectly via the emotion of anger and resentment. These findings may expand and enrich the research on social mobilization, and may deepen the understanding of social mobilization.

Network social mobilization is a “double-edged sword”, which has certain positive effects, but the negative effects cannot be ignored. From the positive and optimistic perspectives, the sharing mechanism on cyberspace can improve the mutual cooperation, enlightenment and perception interaction among the members that concerns the same topic, the constantly actively participating in the discussion group and sharing their own experience, skills, knowledge and views, etc., will be particularly important for the formation of the group wisdom that is superior to any individual himself (Huang, 2011). Crowd intelligence has a great significance on suppressing the misinformation of social media (Ge, 2016). It can constrain and regulate the behavior of members within the group, thereby inhibiting the disorderly spread of resentment, especially controlling the concentrated outbreak of anger. Therefore, for the relevant departments, how to use crowd intelligence to guide public opinion correctly is the key to managing network social mobilization.

Limitations and future directions

In this study, Python language was used to write web crawler to obtain microblog data and conduct word segmentation and word matching operations on microblog contents, which is innovative to some extent. But the use of computer technology to obtain large amounts of data have its inherent drawbacks – accuracy is not high enough. In this study, the compilation of the feeling of injustice vocabulary is derived from the network social mobilization that has taken place, and the word segmentation technique was used to cut the words on Weibo comments. Due to the limitations of computer technology, the results of the word-cutting were not always satisfaction. Because of the difference in parts of word, it is not intelligently recognized when the word segmentation is presented. Although the researchers had dealt with and supplemented such problems, it is inevitable that some omissions will have an impact on the research. In addition, the comments of netizens in the network environment are completely subjective and free, and their expression of sentences were diverse, which also brings difficulties to the work of matching the vocabulary. Although a large number of comments have been drawn, the content of the research materials is still small. In the future, studies using the same or similar methods need to increase the amount of data collection as appropriate to ensure sufficient sample size.


  • The feeling of injustice predicted directly the network social mobilization.

  • Anger and resentment play a mediating role in the prediction of injustice on network social mobilization.


Results of a multiple regression analysis conducted with SPSS

Figure 1.

Results of a multiple regression analysis conducted with SPSS

Results of a multiple regression analysis conducted with SPSS

Figure 2.

Results of a multiple regression analysis conducted with SPSS

Descriptive and correlations among key variables

Variable M±SD 1 2
The feeling of injustice 3.27 ± 2.25
Anger 3.34 ± 2.67 0. 31**
Network social mobilization 1.90 ± 0.87 0. 26** 0. 28**

Notes: N =116;

*p <0. 05;

**p <0. 01;

***p <0. 001

Descriptive and correlations among key variables

Variable M ±SD 1 2
The feeling of injustice 2.95 ± 1.56
Resentment 2.76 ± 0.50 0. 41**
Network social mobilization 1.71 ± 0.81 0. 26** 0. 21**

Notes: N = 151;

*p < 0. 05;

**p < 0. 01;

***p < 0. 001


Baumeister, R.F., Bratslavsky, E., Finkenauer, C. and Vohs, K.D. (2001), “Bad is stronger than good”, Review of General Psychology, Vol. 5 No. 4, pp. 323-370.

Crosby, F.J. (1976), “A model of egotistical relative deprivation”, Psychological Review, Vol. 83 No. 2, pp. 85-113.

Ding, H.M., Wei, M. and Yang, L. (2006), “Network mobilization and its impact on college political stability”, Journal of Beijing Youth Politics College, No. 2, pp. 28-32.

Dong, Y.H., Chen, H., Lai, K.S. and Yue, G.A. (2015), “Weibo social moods measurement and validation”, Journal of Psychological Science, Vol. 38 No. 5, pp. 1141-1146.

Gan, Q. and Luo, Y.T. (2011), “The nature of social mobilization”, Academic Research, No. 6, pp. 24-28.

Ge, T. (2016), Research on the Mechanism of Group Wisdom Suppressing the False Information Dissemination of Social Media, Shanghai University of Engineering Science, Shanghai.

Gergen, K.J. (1985), “The social constructionist movement in modern psychology”, American Psychologist, Vol. 40 No. 3, pp. 266-275.

Hafer, C.L. (2000), “Investment in long-term goals and commitment to just means drive the need to believe in a just world”, Personality and Social Psychology Bulletin, Vol. 26 No. 9, pp. 1059-1073.

Hansen, C.H. and Hansen, R.D. (1988), “Finding the face in the crowd: an anger superiority effect”, Journal of Personality and Social Psychology, Vol. 54 No. 6, pp. 917-924.

Harre, J.R. (1986), The Social Construction of Emotion, Basil Blackwell, Oxford, pp. 1-14.

Huang, X.B. (2011), “Implementation of group wisdom in Web2.0 environment”, Document, Information and Knowledge, Vol. 6 No. 5, pp. 43-45.

Ji, S.L. and Cui, L.J. (2017), “In real and web situations: how collective action tendency is motivated by the dual pathway model”, Psychology: Techniques and Application, Vol. 5 No. 4, pp. 209-216.

Joiner, T.E. Jr. (1994), “Contagious depression: existence, specificity to depressed symptoms, and the role of reassurance seeking”, Journal of Personality and Social Psychology, Vol. 67 No. 2, pp. 287-296.

Kawakami, K. and Dion, K.L. (1995), “Social identity and affect as determinants of collective action: toward an integration of relative deprivation and social identity theories”, Theory and Psychology, Vol. 5 No. 4, pp. 551-577.

Leach, C.W., Snider, N. and Iyer, A. (2002), “Poisoning the consciences of the fortunate: the experience of relative advantage and support for social equality”, Relative Deprivation Specification Development and Integration, Vol. 663 No. 2, pp. 136-163.

Leonard, D.J., Moons, W.G., Mackie, D.M. and Smith, E.R. (2011), “We’re mad as hell and we’re not going to take it anymore”: anger self-stereotyping and collective action”, Group Processes and Intergroup Relations, Vol. 14 No. 1, pp. 99-111.

Li, B.N. (2010), “Resolve the negative action forces in network social mobilization”, Journal of Xi'an Politics Institute, No. 6, pp. 32-34.

Livingstone, A.G., Spears, R., Manstead, A.S., Bruder, M. and Shepherd, L. (2011), “We feel, therefore we are: emotion as a basis for self-categorization and social action”, Emotion, Vol. 11 No. 4, pp. 754-767.

Mackie, D.M., Devos, T. and Smith, E.R. (2000), “Intergroup emotions: explaining offensive action tendencies in an intergroup context”, Journal of Personality and Social Psychology, Vol. 79 No. 4, pp. 602-616.

Nietzsche (1992), On the Pedigree of Morality, Life Reading Xinzhi Sanlian Bookstore.

Pourtois, G., Grandjean, D., Sander, D. and Vuilleumier, P. (2004), “Electrophysiological correlates of rapid spatial orienting towards fearful faces”, Cerebral Cortex, Vol. 14 No. 6, pp. 619-633.

Pourtois, G., Schwartz, S., Seghier, M.L., Lazeyras, F. and Vuilleumier, P. (2006), “Neural systems for orienting attention to the location of threat signals: an event-related fMRI study”, Neuroimage, Vol. 31 No. 2, pp. 920-933.

Scheler (1997), Subversion of Value, Life, Reading, and New Knowledge Sanlian Bookstore.

Shepherd, L., Spears, R. and Manstead, A.S.R. (2013), “This will bring shame on our nation': the role of anticipated group-based emotions on collective action”, Journal of Experimental Social Psychology, Vol. 49 No. 1, pp. 42-57.

Shi, J. and Cui, L.J. (2016), “Opinion support and collective action: a mediated moderation model”, Psychological Research, Vol. 9 No. 1, pp. 72-78.

Shi, J., Hao, Z., Saeri, A.K. and Cui, L. (2014), “The dual-pathway model of collective action: impacts of types of collective action and social identity”, Group Processes and Intergroup Relations, Vol. 18 No. 1, pp. 45-65.

Smelser, N.J. (2006), Theory of Collective Behavior, Free Press, New York, NY.

Sun, H.Y. (2012), “An analysis of the new generation of emotional harm to the community of migrant workers”, Journal of Chongqing University of Arts and Sciences, Vol. 31 No. 1, pp. 69-73.

Tausch, N., Becker, J.C., Spears, R., Christ, O., Saab, R. and Singh, P. (2011), “Explaining radical group behavior: Developing emotion and efficacy routes to normative and non-normative collective action”, Journal of Personality and Social Psychology, Vol. 101 No. 1, pp. 129-148.

Tenhouten, W.D. (2008), “A general theory of emotions and social life”, Emotion Space and Society, Vol. 4 No. 1, pp. 63-64.

Van Zomeren, M., Postmes, T. and Spears, R. (2008), “Toward an integrative social identity model of collective action: a quantitative research synthesis of three socio-psychological perspectives”, Psychological Bulletin, Vol. 134 No. 4, pp. 504-535.

Van Zomeren, M., Spears, R., Fischer, A.H. and Leach, C.W. (2004), “Put your money where your mouth is! Explaining collective action tendencies through group-based anger and group efficacy”, Journal of Personality and Social Psychology, Vol. 87 No. 5, pp. 649-664.

Wang, L.P. (2015), “Responding to resentment: Emotional management in national governance”, Chinese Book Review, Vol. 4.

Wu, Z.M. (2003), “Rediscover social mobilization”, Theoretical frontier, Vol. 21.

Xu, Z.Y. (2013), “Study on the internet mobilization and it’s management”, Doctoral dissertation.

Xu, X. (2017), “Research on the effect and features of emotional bias in the communication of sina social news: an web mining and empirical analysis based on sina socoal news”, Journal of International Communication, Vol. 39 No. 4, pp. 76-94.

Xue, T., Chen, H., Yue, G.A. and Yao, Q. (2013), “Collective action participation: effects of multiple social identities on group-based emotions and efficacy paths”, Acta Psychologica Sinica, Vol. 45 No. 8, pp. 899-920.

Yang, G.B. (2013), “Online civil movement within multiple interaction”, News Spring and Autumn, Vol. 2, pp. 4-16.

Yue, G.A. and Xue, T. (2011), “The theoretical interpretation model of network collective behavior”, Journal of Nankai University (Philosophy and Social Sciences), Vol. 5, pp. 116-123.

Yue, G.A., Xue, T., Chen, H. and Yao, Q. (2013), “Reanalysis of the dual-path model of collective action: evidence from offline and online circumstance”, Journal of Psychological Science, Vol. 36 No. 2, pp. 424-428.

Zhang, L., Shen, J.L., Huang, R.M. and Luo, M.N. (2011), “The characteristics of children's feeling of justice and their relationship with subjective well-being under different time of stay”, Psychological Development and Education, Vol. 27 No. 5, pp. 484-490.

Zhang, Y.D. and Zhou, S.Q. (2007), “Resource mobilization and civil aid via the internet”, Chinese Journal of Sociology, Vol. 3, p. 3.

Further reading

Brunsting, S. and Postmes, T. (2002), “Social movement participation in the digital age – predicting offline and online collective action”, Small Group Research, Vol. 33 No. 5, pp. 225-554.

Chen, H., Xue, T. and Yue, A.G. (2012), “Instrumental, social identity and group-based anger: overview of collective action research”, Advances in Psychological Science, Vol. 20 No. 1, pp. 127-136.

Hafer, C.L., Bègue, L., Choma, B.L. and Dempsey, J.L. (2005), “Belief in a just world and commitment to long-term deserved outcomes”, Social Justice Research, Vol. 18 No. 4, pp. 429-444.

Katz, J., Beach, S.R.H. and Joiner, T.E. (1999), “Contagious depression in dating couples”, Journal of Social and Clinical Psychology, Vol. 18 No. 1, pp. 1-13.

Rundciman, W.G. (1966), Relative Deprivation and Social Justice: A Study of Attitudes to Social Inequality in Twentieth-Century England, University of CA Press, Berkeley.

Smith, H.J., Cronin, T. and Kelsser, T. (2008), “Anger, fear, or sadness: Faculty members' emotional reactions to collective pay disadvantage”, Political Psychology, Vol. 29 No. 2, pp. 221-246.

Wright, S.C. (2009), “The next generation of collective action research”, Journal of Social Issues, Vol. 65 No. 4, pp. 859-879.


Funding: Ministry of Finance of the People’s Republic of China 2017YFB1400102.

Corresponding author

Jinghuan Zhang can be contacted at:

Related articles