DASFA-FCM proceed as follows:

① Initialization parameters: γ, T_max, m, generate initial population $X_i\left(i=1,2,\cdots ,n\right)$ , 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(X_i) of each firefly according to Formula (9).

③ Calculating similarity between two texts(comparison of each micro-blog text and class center). when $sim\left(i,j\right)<\epsilon$ , the value of β₀, u_ij are 0; when $sim\left(i,j\right)\ge \epsilon$ , 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(X_i), F(X_j); if F(X_i) < F(X_j), 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 $d_{ic}=\Vert x_i-x_c\Vert$ 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.