[Purpose/significance] This study aimed to predict whether microblog users would retweet or comment on the microblog entries containing wanted information. We also evaluated the important features that affected the spread of wanted microblog entries to help the public security departments improve their operation performance and enhance the communication and cooperation between the police and the public. [Method/process] Based on the characteristics of the wanted microblogging, we combined user features, time features and structure features, and extracted event features in microblog entries, such as location keywords, time keywords, the wanted level and so on. The Xgboost algorithm was used to calculate the importance of different features in the retweet and comment prediction. In combination with the features of transmission network and node attributes, we trained and evaluated a prediction model based on heterogeneous information network embedding. [Result/conclusion] The values of the AUC in retweeting and commenting data sets are 0.737 and 0.799 respectively. As the model integrated network structure characteristics and different nodes' attributes, it was closer to the heterogeneous information network in reality and had higher accuracy than the traditional link prediction model. In addition, the result of features' importance showed that the keyword features of the proposed event features had the highest importance among all the features that affected the prediction of microblog entries retweeted and commented.
Sun Ran
,
An Lu
. Propagation Prediction of Police Microblog Entries Based on Heterogeneous Information Network[J]. Library and Information Service, 2020
, 64(21)
: 67
-76
.
DOI: 10.13266/j.issn.0252-3116.2020.21.010
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