近年来，随着互联网的快速发展和智能终端的日益普及，在线社交网络（Online Social Networks．OSN）已经成为人们获取信息、传播信息、交友和娱乐等的重要渠道。在线社交网络结构的复杂性、用户规模的庞大性、信息产生的海量性，以及传播的快速性和难以溯源等特点，使得在线社交网络中用户交流互动与信息创建传播等行为所产生的效用，不仅对人们的工作和生活方式，
the existing information diffusion models focus on analyzing the spatial distribution of certain pieces of messages in social networks. However, these conventional models ignored another important characteristic of diffusion: gradually changing of message contents due to the ‘new' and ‘comment' mechanisms. A novel genetic-algorithm-based information evolution model is proposed to reproduce both the diffusion and development process of information in social networks. This model firstly proposes a five-tuple to represent three types of topics: independent, competitive and mutually exclusive. Furthermore, it adopts mutation operator and forms new crossover and mutation rules to simulate four typical interactions between individuals, which bring the advantage of reproducing the information evolution process in both popularity and content.A series of experiments tested on public datasets demonstrate that: 1) independent and competitive topics of information rarely affect each other while mutually exclusive topics significantly suppress the diffusion processes of each other; 2) lower mutation probability leads to decreasing of final information amount. The experimental results show that our evolution model is more reasonable and feasible in demonstrating the evolution of information in social networks.
