Research on Weibo Opinion Leaders Identification and Analysis in Medical Public Opinion Incidents

[Objective] This paper aims to identify Weibo opinion leaders and study their influence in medical public opinion incidents. [Methods] This article integrates user personal attributes, network characteristics, behavioral characteristics and text features to construct a comprehensive index system to identify opinion leaders in different periods of medical public opinion incidents, and also use time difference correlation analysis to study the impact of the emotional tendency of opinion leaders on the public sentiment. [Results] Taking the 2018 vaccine event as a case, this paper verifies the effectiveness of the proposed opinion leader identification model. The results show that the medical public opinion hotspots and the types of opinion leaders differ in different periods, and the attitudes of opinion leaders have a guiding effect on the emotions of the general public. [Limitations] We only examined the performance on the proposed methods with the vaccine event data and the model generalization ability remains underdeveloped. [Conclusions] The multi-feature opinion leader identification method proposed in this paper can better discover potential opinion leaders among grassroots users compared with traditional evaluation indicators. © 2019 The Author(s).