۱٫ Introduction

In the Big Data Era, the Internet has become an indispensable element in people’s daily life [1, 2, 3]. With the emergency of mobile Internet, the application of big data is exerting more and more influences on individuals. The big data prediction of individual behaviors and collective actions has become feasible, necessary and stable [1, 2, 3]. With cheaper costs of participation [3, 4, 5], launching or participating a collective action online has become quite convenient. As the cost is cheaper, the scale of participation is very huge, compared to traditional collective actions. For traditional collective action, the number of participants is from thousands to tens of thousands, but the number commonly is over millions for online collective actions. As the amount of participation is huge, cyberspace collective actions have more power, impact, or influence than traditional actions [3, 5, 6], especially on the politics and public policy in China. Internet users launch collective actions to express their positions, and other individuals take part in it responding to them both positively and negatively. Cyberactivism is the extreme aspect of cyber collective actions; it forms contentious politics and shapes social movements [5, 6, 7]. The spirit of online collective actions is hacktivism or cyberactivism, while the spirit of traditional ones is linked to the concept of collective identity [8, 9, 10]. Online collective actions built the ideal public sphere for individuals [11], which break the more tightly controlled offline media and political space to facilitate people’s participation and rights [11]. The parameters of individuals’ online behaviors have been inspected [5, 8, 11, 12, 13]. Based on the online browse, we predict the participation [13, 14, 15, 16]. It seems that the numbers of browse (millions) and participation (tens of thousands) are huge with irregular distributions. However, it is discovered by this work that the ratio between them takes on the regularity of 5‰ approximately. The ratio is defined as the participation propensity of Internet users, which is a new research field. Sometimes the participation propensity is slightly under 5‰ while sometimes slightly beyond 5‰. Besides of the overall propensity, propensities of subgroups will be investigated. The distribution of the participation propensity is checked, and the probability density function is obtained, based on which big data prediction is feasible.