中文社交媒体用户性别预测研究——以新浪微博短文本内容为例

刘雅琦; 李得志; 王瑞雪

doi:10.13266/j.issn.2095-5472.2021.021

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知识管理论坛 ›› 2021, Vol. 6 ›› Issue (4) : 213-227. DOI: 10.13266/j.issn.2095-5472.2021.021

学术探索

中文社交媒体用户性别预测研究——以新浪微博短文本内容为例

作者信息 +

Research on Gender Prediction of Chinese Social Media Users——Taking Sina Weibo Short Text Content as an Example

Author information +

文章历史 +

摘要

[目的/意义] 与互联网的高速发展不同，个人信息安全保护的发展相对滞后，通过预测社交媒体用户的性别，能够更好地针对不同性别用户提供隐私保护。[方法/过程] 以新浪微博这一社交媒体中用户发布的短文本为研究对象，从中抽取语言特征和主题特征，为每一个用户构建基于语言特征、主题特征以及两个特征叠加的特征表达向量，利用SVM机器学习算法构建性别预测的分类器。[结果/结论] 实验表明，从微博短文本中抽取的语言特征和主题特征能够准确预测用户性别，其效果在主要评价指标中均有大幅提升。

Abstract

[Purpose/significance] Different from the rapid development of the Internet, the development of personal information security protection is relatively lagging. By predicting the gender of social media users, it can better provide privacy protection for the users. [Method/process] The short texts posted by users in social media, Sina Weibo, were taken as the research object. The experiment extracted linguistic features and topic features from the short texts. For each user, we constructed features vector based on linguistic features, topic features, and the superposition of two features, then used SVM Machine learning algorithms built a classifier for gender prediction. [Result/conclusion] Experiments show that the linguistic features and topic features can predict the gender of the users accurately, and the effect is superior to other features used in gender prediction.

导出引用

刘雅琦 , 李得志 , 王瑞雪. 中文社交媒体用户性别预测研究——以新浪微博短文本内容为例[J]. 知识管理论坛. 2021, 6(4): 213-227 https://doi.org/10.13266/j.issn.2095-5472.2021.021

Liu Yaqi , Li Dezhi , Wang Ruixue. Research on Gender Prediction of Chinese Social Media Users——Taking Sina Weibo Short Text Content as an Example[J]. Knowledge Management Forum. 2021, 6(4): 213-227 https://doi.org/10.13266/j.issn.2095-5472.2021.021

中图分类号： TP391.1

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