[目的/意义]旨在了解辟谣平台中健康辟谣信息的内容特征和质量现状，并据此优化平台的信息质量，提
升辟谣效果。[方法/过程]以“科学辟谣平台”为例，遵循“是什么-怎么样-如何做”的逻辑链条对平台中的健康辟
谣信息展开系统分析。首先，采用LDA主题模型挖掘健康辟谣信息的内容主题及演化趋势。之后，结合DIS?
CERN与Michigan Checklist质量评价框架，从归源性、及时性等8个维度评价健康辟谣信息质量，同时利用方差分
析探究各内容要素同质量评价结果间的关系。最后，提出优化辟谣平台质量及信息质量的管理措施。[结果/结
论]营养价值、食品添加等5个主题是辟谣平台中健康辟谣信息的关注重点，平台信息内容呈现明显的主题演化与
强度变化特征。平台中健康辟谣信息质量整体不高，信息质量与内容主题、专家信息、参考文献等要素密切相
关。未来，平台可据此提升健康辟谣信息质量，助力营造良好的网络健康信息生态。

[Purpose/significance] The aim is to understand the content characteristics and quality of health rumor-refuting information in the platform, and optimize the platform’s service and rumor refuting effectiveness accordingly.
[Method/process] Taking the "Scientific rumor refuting platform" as an example, we follow the logical chain of "whathow
is it-how to do" to carry out a systematic analysis of the health rumor-refuting information on the platform. First of
all, the LDA topic model is applied to mine the content topics and evolutionary trends of health rumor-refuting information. Secondly, combined with the quality evaluation framework of DISCERN and Michigan Checklist, we evaluate the quality of health rumor- refuting information from multiple dimensions such as attribution, timeliness. And we use ANOVA to explore the relationship between content elements and the quality evaluation results. Finally, we propose the management measures to optimize the platform construction. [Result/conclusion] Five topics such as nutritional value and food additives are the main topics of health rumor-refuting information. The information contents of the platform present obvious thematic evolution and intensity changes. The overall quality of health rumor-refuting information on the platform is not high, and the information quality is closely related to the content theme, expert information, reference literature and other content elements. In the future, the platform can optimize the health rumor-refuting services based on the above conclusions, so as to help create a good online health information ecology.