Analysis on disseminationeffect of information released in"Health Improvement"New media Popularization Competition
Objective To clarify the dissemination effect of information released in the"2023'Health Improve-ment'-New media Popularization Competition",and to provide reference for high-quality health science popularization ac-tivities in the future.Methods The Qingbo Big Data System(Public Opinion System V5.0)was used to collect informa-tion released by the"2023'Health Improvement'-New Media Popularization Competition"in Beijing.The collected informa-tion was classified and statistically analyzed according to owner,platform,and the dissemination effect data(reading vol-ume,liking volume,forwarding volume,comment volume).Data were statistically analyzed to evaluate the roles and contri-butions of different owners and platforms in the competition.Results A total of 991 relevant pieces of information were col-lected online by using keywords such as"health improvement".Among different publishing platforms,the Tiktok platform accounted for higher proportions of total reading(24.4％),total liking(34.3％),total comments(96.4％)and total forwarding(95.7％),followed by Kwai's total reading(35.6％)and total likes(63.3％).Among different publishing owners,health education institutions published the most content(43.4％),commercial media had the highest total liking(63.0％),and hospital science popularization had the highest total comments(55.1％).In addition,health education in-stitutions(48.6％)and hospital science popularization platforms(45.2％)had relatively high total forwarding vol-umes.Conclusion In the"2023'Health Improvement'-New Media Popularization Competition",different platforms and different owners showed different health communication characteristics and effect.In the future,related departments should establish multi-cooperation and targeted health communication mechanism according to the communication characteristics of different platforms and owners for improving residents'health literacy.
Health promotionQingbo Big dataNew media communicationDissemination effect
NETL
NSTL
万方数据
