The application relates to the technical field of internet
big data analysis, and discloses an article reach person-
time estimation algorithm based on a propagation path, which comprises the following steps: step 1, collecting a propagation event log; step 2, data cleaning and filtering; step 3, constructing a propagation path tree and obtaining node attributes; step 4, estimating independent
exposure person-time brought by sharing; step 5, merging and calculating total article reach person-time; and step 6, correction and periodic parameter updating. The article reach person-
time estimation algorithm based on the propagation path sets effective
exposure rate parameters for different sharing channels, for example, based on platform sampling statistics, the effective
exposure rate of a friend circle share is 0.12, the effective exposure rate of a microblog share is 0.18, and the effective exposure rate of other channels is 0.10, so that the differences in actual visible probability of information flow of various channels can be reflected, the single sharer basic exposure amount is calculated on this basis, rough
processing methods of adopting the same exposure proportion for different channels are avoided, and the exposure
estimation is more in line with the actual distribution mechanism of the platform.