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- W4377726488 abstract "By analyzing Sina Weibo content helps to better guide internet public opinion. This paper selects the Weibo trending topic of '11.3 COVID-19 Epidemic in Dalian City' and uses K-means clustering algorithm to construct Weibo User Characteristics Model from three aspects: basic attributes, behavior attributes and value attributes. Then, based on the emotional dictionary, this paper also examines the emotional types of Weibo users and their dissemination trends. The study carries out that the user groups under this topic are mainly divided into hot spot generating users, high-frequency interactive users and ordinary social users. During the outbreak of the epidemic, Weibo users' emotions are mainly positive emotions of 'good' and 'happy', showing a trend from pessimistic to optimistic emotions." @default.
- W4377726488 created "2023-05-24" @default.
- W4377726488 creator A5048160589 @default.
- W4377726488 date "2022-10-01" @default.
- W4377726488 modified "2023-09-26" @default.
- W4377726488 title "Analysis of Weibo User Characteristics and Emotional Tendency in COVID-19 Scenario Based on K-means Clustering Algorithm" @default.
- W4377726488 cites W2011430131 @default.
- W4377726488 cites W342133811 @default.
- W4377726488 doi "https://doi.org/10.1109/icdsba57203.2022.00062" @default.
- W4377726488 hasPublicationYear "2022" @default.
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