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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.

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.

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.

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.

The purpose of this paper is to explain the difference and connection between the network big data analysis technology and the psychological empirical research method.

This study analyzed the data from laboratory setting first, then the online sales data from Taobao.com to explore how the influential factors, such as online reviews (positive vs negative mainly), risk perception (higher vs lower) and product types (experiencing vs searching), interacted on the online purchase intention or online purchase behavior.

Compared with traditional research methods, such as questionnaire and behavioral experiment, network big data analysis has significant advantages in terms of sample size, data objectivity, timeliness and ecological validity.

Future study may consider the strategy of using complementary methods and combining both data-driven and theory-driven approaches in research design to provide suggestions for the development of e-commence in the era of big data.

This chapter reviews the status of Education For All (EFA) in China and identifies four gaps: between rural and urban residents, between residents of geographic regions, between ethnicity groups, and between the genders. It turns to examine the educational situation and interests of girls weighed down by the crushing burden of multiple disadvantages in “left-behind” Western China. Based on analysis of macro-level socio-economic and educational conditions, along with rich micro-level data on girls’ vigorous pursuit of education, the authors argue that the changing conditions of rural girls’ lives and their education can best be understood from a critical empowerment perspective. Summarizing the global discourse and cross national evidence on the benefits of girls’ education, the chapter and looks beyond a utilitarian perspective and argues for the cogency of a critical empowerment framework. Filled with telling stories and case studies of Han Chinese, Tibetan, and Muslim girls, this chapter proposes that prioritizing girls’ education in Western China is crucial and required for achieving the MDG of gender parity. Even though girls are often stranded by family financial conditions, their actions and ideas seeing education as their future reflect a changing gender identity and role in the family and society. The fieldwork suggests that educating girls promotes localized development, reduces dangerous levels of economic gaps and social instability, but also advances hard to measure effects: personal and civil empowerment, and sustainable, harmonious cultural change – as well as MDG.