A Dataset for Analyzing Post–Comment Sentiment Feedback and Dynamic Diffusion in Weibo Trending Topics

This dataset was developed for the study “Post–Comment Sentiment Feedback and the Dynamic Diffusion of Weibo Trending Topics.” It contains structured observations of Weibo trending topics and their dynamic diffusion patterns, with particular attention to the interaction between post sentiment and comment sentiment. The dataset includes topic-level and time-interval-level variables related to trending-topic diffusion, such as topic name, topic URL, hot-search rank, time interval, incremental view volume, and topic category. It also contains sentiment-related variables measuring post sentiment, comment sentiment, centered sentiment indicators, lagged sentiment indicators, and post–comment sentiment feedback terms. In addition, the dataset provides control variables describing post characteristics, comment characteristics, and author-level attributes, including reposts, comments, likes, content length, image/video presence, author followers, followings, influence, VIP status, and verification status. This dataset can be used to examine how emotional signals embedded in original posts and user comments jointly shape the diffusion of Weibo trending topics over time. It is particularly suitable for research on social media diffusion, public opinion dynamics, sentiment interaction, user engagement, and the temporal evolution of online attention.