Cyberbullying has caused serious consequences especially for social network users in recent years. However, the challenge is how to fight against the cyberbullying effectively from the algorithmic perspective. In this article, we study the fighting against the cyberbullying problem, i.e., identify an initial witness set with a budget to spread the positive influence to protect the users in a specific target set such that the number of cybervictim users in the target set being activated by the seed set of cyberbullying is minimized. We first formulate this problem and show its NP-hardness. We further prove that the objective function is submodular with respect to the size of witnesses set when we convert the original problem into the maximal version. Then we propose a stochastic approach to solve this maximal version problem based on the Reverse Sampling Technique with a constant factor guarantee. In addition, we provide theoretical analysis and discuss the relationship between the optimal value and the value returned by the proposed algorithm. To evaluate the proposed approach, we implement extensive experiments on synthetic and real datasets. The experimental results show our approach is superior to the comparison methods.

Social influence has been widely studied in areas of viral marketing, information diffusion and health care. Currently, most influence models only deal with a single influence without the interference of other influences. Also, the influence spreading in previous models must be triggered by individuals who have been activated by the influence. In this paper, we argue that it is the attraction from a specific influence makes an individual choose to spread it among multiple influences. Inspired by charged system theory in physics, a new influence model is proposed, considering individual features and social structure features. It also gives a natural description about how individuals make decisions among multiple influences. Then a novel algorithm based on this model is provided to predict human behavior. Extensive experiments on three real-world datasets demonstrate that our model and algorithm statistically outperform the state-of-the-art methods in terms of prediction accuracy.

Influence maximization problem is to find a set of seeds in social networks such that the cascade influence is maximized. Traditional models assume all nodes are willing to spread the influence once they are influenced, and they ignore the disparity between influence and profit of a product. In this paper by considering the role that price plays in viral marketing, we propose price related (PR) frame that contains PR-I and PR-L models for classic IC and LT models respectively, which is a pioneer work. We find that influence and profit are like two sides of the coin, high price hinders the influence propagation and to enlarge the influence some sacrifice on profit is inevitable. We propose Balanced Influence and Profit (BIP) maximization problem. We prove the NP-hardness of BIP maximization under PR-I and PR-L model. Unlike influence maximization, the BIP objective function is not monotone. Despite the non-monotony, we show BIP objective function is sub modular under certain conditions. Two unbudgeted greedy algorithms separately are devised. We conduct simulations on real-world datasets and evaluate the superiority of our algorithms over existing ones.