Abstract: [Purpose/Significance] The objective of this study is to explore how to integrate social sensing data ,containing rich individual emotion, behavior, and interaction information, with publisher's prior knowledge to enhance the accuracy of false health information recognition. [Design/Methodology] Based on the social sensing data and historical information text, this paper describes the prior knowledge of publishers about detection information. By integrating the publisher's prior knowledge, the study extracts health information features from three dimensions: publisher features, content features, and receiver behavior features. Concurrently, health information portraits are established and the Stacking ensemble learning models is used to build a False Health Information Recognition Model（FHIR_SSD&PPK）, a false health information recognition model that integrates social sensing data and publisher's prior knowledge. [Findings/Conclusion] FHIR_SSD&PPK model has the best effect in identifying false health information, with an accuracy of 92.35%. The total feature importance weight of the publisher features accounts for the highest proportion, at 51.59%, among which the feature importance weight of the publisher's prior knowledge features is 44.01%, and the F-Measure increases by 2.26% compared to the model without considering the publisher's prior knowledge, indicating that the publisher's prior knowledge proposed in this article is a key feature for building an identification model. [Originality/Value] The FHIR_SSD&PPK model integrates social sensing data and publisher's prior knowledge, identifies false health information based on the Stacking ensemble learning model, optimizing the research in fine granularity and depth.

Keywords: Health information portrait, False health information, Social sensing data, Prior knowledge, Stacking ensemble learning