## 5 Experimental Results and Discussion
### Experiments on Microblog Network  
Experimental data collection was obtained by the Octopus collector [3]. First of all, considering the timeliness of comments and the existence of a large amount of worthless information, we selected six popular Microblog posts published by Xiaomi Company's Microblog homepage, and collected \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index Name** & **r = 30** & **r = 40** & **r = 50** & **r = 60** & **r = 100** \\ \hline ACC (\%) & 73.97 & 75.28 & 80.86 & 66.75 & 66.70 \\ ARI & 0.4830 & 0.5063 & 0.6001 & 0.3763 & 0.4182 \\ Precision & 0.5481 & 0.5761 & 0.6599 & 0.4369 & 0.4829 \\ Recall & 0.6001 & 0.6056 & 0.6751 & 0.5672 & 0.5706 \\ NMI & 0.5278 & 0.5459 & 0.5871 & 0.4805 & 0.4920 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of the SCS algorithm with TTC-3600 data set. 200 popular comments under each Microblog. A total of 1200 popular followers' popular comments and corresponding user IDs were adopted. Then we randomly selected 100 follower users to get their following lists, and filtered out the 166 influencer accounts (related companies, digital bloggers, competing related products) related to this experiment. Finally, the user's ID link was entered into the Microblog homepage according to the comments obtained. The basic information of the users was collected, including gender, labels, number of follows, followers, and posts. And then we extracted the client terminals which had newly published five posts. The common follow list of the user's homepage refers to the current login account and the account that the user follows collectively. By obtaining a list of common follows of new accounts and ordinary users, we could indirectly get the following situation of ordinary users to 166 influencer accounts in specific areas. The Jieba word segmentation in Python supports three-word segmentation modes: precise mode, full mode, and search engine mode [4]. This work uses the precise mode of Jieba word segmentation for the textual information of user comments. The text contains a large number of function words such as modal particle, adverb, preposition, connective, etc. The stop word filtering operation is performed after the word segmentation. By integrating the \"Hagong University Stop Words\" [5] and \"Sichuan University Machine Learning Intelligence Lab Stop Words\" [6] and other stop words, we got a stop word list with 2079 stop words. Each comment was saved after the word segmenting into a separate document by word segmentation and filtering stop words operation. Then the data representation were transformed into structured data in a vector space model. Each user was represented as a vector in space. The elements of the vector were composed of user attribute features, behavior features, and text features. Different weight calculation methods were set for each feature item. The value of the user's numeric attribute represented the weight of the corresponding feature item, while the weight of the non-numeric attribute was calculated by using the Boolean function. The inconsistency of the dimensions of different features would affect the clustering results. Therefore, the weights of the attribute features (gender, client) were normalized by min-max according to the column. The value range of the feature elements was finally between [0, 1]. The CountVectorizer and TfidVectorizer in the sklearn package of Python were used to calculate the TF-IDF weights for the text comment feature words after the word segmentation. The similarity between two words of all feature words in vector space was calculated, and the feature word-feature word similarity matrix was obtained. The feature words that exceeded the similarity threshold were added to the corresponding documents. The new feature weights in the documents were calculated according to the similarity value and the maximum initial weight of the word in other documents. Table 4 is an example of the calculated similarity of some feature words.