The rapid growth of the Internet use has allowed social networks to become the most effective means for marketing, leading to the emergence of "viral marketing" as a business model. The biggest challenge that is facing "viral marketing" is selecting seed users from the whole user set to form a "seed-set" to spread the influence and maximize the number of influenced users. This is known as the classic influence maximization problem. Based on the background provided, the paper focus on the fairness of information Maximization in social media and try to explain how the homophily effect, rich get richer mechanism and the duration of top users impact users in temporal social network. The paper also aims at developing a time-Awareness disparity seeding framework based on Disparity seeding framework, which is proved by experiments to slove the absolute error problem that exist between the target ratio and influential ratio. Furthermore, the unequal seeding disperse algorithm (USD), equal seeding disperse algorithm(ESD) and origin seeding disperse algorithm(OSD) have been developed to improve the influence maximization in temporal social network. The purpose is to find the most cost-effective user seed-set in any given period of time to maximize the influence of target users and increase the number of influenced users. According to the experimental test result, it is found that unequal seeding disperse algorithm perform better than the other two algorithms. In this paper, many experiments are carried out to verify the effectiveness of all the three algorithms using real social network data sets. As a result, the effectiveness and efficiency of the proposed algorithm was proven.