关键词: 微博舆情热度分析预测, BERT模型, X-means算法, 水军识别, 社交网络分析

Abstract: In public opinion discovery and prediction on social media platforms such as Weibo, "fake hotspots" created by internet trolls were found to affect analysis accuracy. To accurately reflect Weibo public opinion trends, a Weibo public opinion heat analysis and prediction model, BXpre, which integrates BERT and the X-means algorithm, was proposed. First, Weibo posts and interaction user data were preprocessed. The fine-tuned StructBERT model was used to classify these data, determining the relevance between interactive users and the original posts. This relevance was used as a reference value for calculating users' contribution weights to the heat growth of the posts. Second, based on the X-means algorithm, interaction user characteristics were clustered, and trolls were filtered according to the homogeneity features of the clusters. A weight penalty mechanism targeting troll samples was introduced, and a heat index model was further constructed by combining label relevance. Finally, the second derivative of the prior heat value over time and its cosine similarity with real data were calculated to predict future changes in Weibo heat. BXpre was designed to integrate the attribute features of participating users and the temporal features of heat changes, thereby improving prediction accuracy. The experimental results showed that BXpre integrated the attribute features of participating Weibo users and the temporal features of heat changes. Under different user scale conditions, the Weibo public opinion heat rankings produced by the model were closer to the real data. Under mixed-scale test conditions, the prediction correlation index reached 90.88%. Compared with three traditional methods, the average improvement was 12.94 percentage points. Additionally, compared with ChatGPT and Wenxin Yiyan, the prediction correlation index was improved by 9.76 and 11.95 percentage points, respectively.

Key words: Weibo public opinion heat analysis and prediction, BERT model, X-means algorithm, troll detection, social network analysis