Research on the authenticity evaluation and recognition of social media health communication information based on deep learning algorithms

In order to understand the authenticity evaluation and recognition of social media health communication information, a research on the authenticity evaluation and recognition of social media health communication information based on deep learning algorithms has been proposed. This paper first establishes a credibility evaluation index system for social media health communication information, which can be used for the evaluation and filtering of social media health communication information in search engines, and can also provide users with guidance and suggestions for credibility evaluation. Secondly, starting from the connotation of information credibility, multiple information characteristics were proposed in three dimensions: information content, information sources, and communication media, and a social media health communication information credibility evaluation index system was designed. Finally, the weights of the indicators are determined using the Analytic Hierarchy Process (AHP), and automatic measurement of each indicator is achieved using natural language processing techniques and assignment transformation methods. This enables the identification of health communication information on social media and provides assurance for social media users to obtain authentic and reliable health information.