Online marketing exploits social influence to trigger chain-like cascades. However, recent practices actively employ agents to collaboratively inflate the spreading of influences. Through supporting structures, they help each other with false feedback and signals to attract other users in the spreading process and thus alter the spontaneous social dynamics. In this paper, we proposed a modeling framework to explain the mechanism of such operations and characterize the spreading dynamics. Model analytics and numerical simulations both showed a lifting in overall spreading influence. As empirical evidence, experiments on a large Weibo network revealed well-structured advertising groups that prominently amplified the influences of promoted commercials via meticulous cooperation in a core-peripheral structure. The inflation effect also brings new considerations into influence maximization problems. Based on our models, we solved the problem of maximizing inflated influence by optimizing the selection of agents under KKT conditions and their supporting structure using its submodular property.

Team collaboration in multi-player online games provides opportunities for players to interact with each other. Facilitating teams has become one of the main design principles to increase social activities. However, there is no research evidence that collaborating on tasks in game teams can produce the desired relational outcome. This paper examines more than half a million solo and team activities during a week in Dragon Nest, an MMO game. We measure the degree of team engagement using the percentage of time played in teams and the percentage of play with repeated teammates, and we then identify different types of players using this. The results show that solo players and team players are two distinct populations and they are highly predictable based on players' in-game status. Moreover, we find that spending more time in teams does not always lead to more social interactions. The interviews with players are conducted to validate the findings.

This study investigates unfollow behavior in Twitter, i.e. people removing others from their Twitter following lists. Considering the interdependency and dynamics of unfollow decisions, we use actor-oriented modeling (SIENA) to examine the impacts of reciprocity, status, embeddedness, homophily, and informativeness on tie dissolution. Focusing on ordinary users in tightly-knitted user groups, the results show that relational properties play key roles in the emergence of unfollow behavior: mutual following relations and common followees reduce the likelihood of unfollowing. And unfollow tends to be reciprocal: when a user is unfollowed by someone, he or she will unfollow back. However, there is no evidence of the impacts of homophily based on common interests and informativeness of interactions. The findings suggest that Twitter has many heterogeneous user groups and relational and informational factors may not be applicable universally.

Cooperative technological solutions for Distributed Denial-of-Service (DDoS) attacks are already available, yet organizations in the best position to implement them lack incentive to do so, and the victims of DDoS attacks cannot find effective methods to motivate them. In this article we discuss two components of the technological solutions to DDoS attacks: cooperative filtering and cooperative traffic smoothing by caching. We then analyze the broken incentive chain in each of these technological solutions. As a remedy, we propose usage-based pricing and Capacity Provision Networks, which enable victims to disseminate enough incentive along attack paths to stimulate cooperation against DDoS attacks.