[Purpose/significance] The effective identification of high-impact users in online health communities is helpful for demanders to find valuable health information, which is of great significance for reducing the cost of health information search and improving the effectiveness of health behavior decision-making. [Method/process] This study was from the perspective of interactivity of users and emotional tendency of comments, using PageRank and SVM algorithm to build a method to measure the users’ influence in online health community, and took the medical network as experimental object, from the angle of content use value, further calculated the comprehensive influence of users in the community, and in case the user is analyzed. [Result/conclusion] The results show that the algorithm is reasonable and can optimize the calculation results of PageRank algorithm. At the same time, the TF-IDF and Mutual Information algorithm are used to reveal that the information content published by high comprehensive influence users is basically consistent with content topics of other user groups in the community, and such users play a certain role in guiding the theme direction of the community. Therefore, the method constructed in this study can effectively and reasonably identify high-impact users, which is helpful for health demanders to find the required information timely and accurately, improving the effect of using health information, so as to enrich the theoretical and practical research on the information behavior of users in online health communities.
Dong Wei
,
Tao Jinhu
. Research on the User’s Influence in Online Health Community Based on PageRank and Emotional Tendency[J]. Library and Information Service, 2021
, 65(11)
: 14
-23
.
DOI: 10.13266/j.issn.0252-3116.2021.11.002
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