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Social networking services are not limited to person-to-person communication, but extend to a wider range involving person-to-thing communication and thing-to-thing communication. Therefore, it is also called big social networking services. In order to motivate users of online social networks to share information and communicate with each other frequently, we first analyzed the activity statuses of users in one of famous social networks, Weibo, and then proposed a lurker game model for accumulating big data. In addition to the features of the public goods game, this model also introduces the factor of individual incentive depending on his degree. We found that the individual strategy to be chosen was not relevant to the user's degree, but to an incentive constant of the entire network. The simulation results showed that individual strategies asymptotically followed three different behaviors according to the dynamic organization of the individuals. Active users will emerge during the evolutionary process with an incentive. Without an incentive, active central users can hardly affect the states of their neighbors and may even become lurkers due to the large number of lurking neighbors. Large noise decreases the influence of the high incentive and causes the chaos of networks. If the continuous chaos exists, active users will gradually lose interest and leave the network.