Mentioning the Optimal Users in the Appropriate Time on Twitter

Abstract

Nowadays, Twitter has become an important platform to expand the diffusion of information or advertisement. Mention is a new feature on Twitter. By mentioning users in a tweet, they will receive notifications and their possible retweets may help to initiate large cascade diffusion of the tweet. In order to maximize the cascade diffusion, two important factors need to be considered: (1) The mentioned users will be interested the tweet; (2) The mentioned users should be online. The second factor was mainly studied in this paper. If we mention users when they are online, they will receive notifications immediately and their possible retweets may help to maximize the cascade diffusion as quickly as possible. In this paper, an unbalance assignment problem was proposed to ensure that we mentioned the optimal users in the appropriate time. In the assignment problem, constraints were modeled to overcome the overload problems on Twitter. Further, the unbalance assignment problem was converted to a balance assignment problem, and the Hungarian algorithm was took to solve the above problem. Experiments were conducted on a real dataset from Twitter containing about 2 thousand users and 5 million tweets in a target community, and results showed that our method was consistently better than mentioning users randomly.

This work was supported by National Natural Science Foundation of China (No.71331008).