DASFA-FCM proceed as follows:

① Initialization parameters: γ, Tmax, m, generate initial population X i ( i = 1 , 2 , , n ) , n indicates all micro-blog texts, k represents the number of initial cluster centers, initializing the position of each firefly.

② Calculating the influence value A(Xi) of each firefly according to Formula (9).

③ Calculating similarity between two texts(comparison of each micro-blog text and class center). when s i m ( i , j ) < ε , the value of β0, uij are 0; when s i m ( i , j ) ε , all are 1. In this moment, the mutual attraction between fireflies is calculated according to Formula (2).

④ According to Formula (7), calculating the dynamic adaptive step length under the current iteration.

⑤ Calculating fitness function F(Xi), F(Xj); if F(Xi) < F(Xj), it shows that the firefly i influence is bigger than j, firefly i is in a better position than j, so firefly j moves to i, update each firefly position according to Formula (8).

⑥ Repeating steps ③ to ⑤ until the maximum number of iteration is reached. We can get the center of the cluster with the most influential fireflies. The number of the cluster center is C.

⑦ Based on the initial class centers found above, calculating the cluster center and membership matrix.

⑧ Calculating the distance d i c = x i x c from the micro-blog text i to the cluster c, and classifying topics into the nearest cluster center.

⑨ Repeating steps ⑦ and steps ⑧. If the termination condition is reached, the location and influence of the most influential firefly will be output, and the result after clustering, otherwise continue.

⑩ We get hot topics based on the arrangement of influence values, output the top 50% topics.