标题:Psychological Gender Express via Mobile Social Network Activities: An Experimental Study on a Gay Network Data
作者:Guo, Ziyu; Liu, Shijun; Pan, Li; Guo, Shanqing; Niu, Tingting
作者机构:[Guo, Ziyu; Liu, Shijun; Pan, Li; Guo, Shanqing] Shandong Univ, Sch Software, Jinan 250101, Shandong, Peoples R China.; [Niu, Tingting] Jinan Vocat 更多
通讯作者:Liu, Shijun;Liu, SJ;Pan, L
通讯作者地址:[Liu, SJ; Pan, L]Shandong Univ, Sch Software, Jinan 250101, Shandong, Peoples R China.
来源:IEEE ACCESS
出版年:2019
卷:7
页码:12696-12704
DOI:10.1109/ACCESS.2019.2892949
关键词:Classifier; ego network; mobile social network; gender identity; triad
摘要:It is well known that the social network users' behavior can reflect their personal mental profiles. Therefore, in this paper, we conducted an experiment to study users' gender identity through a mobile social network. Gender identity is the personal sense of one's own gender, which can be the same or different from their sex assigned at birth, that is, one's innermost concept of self as male, female, a blend of both, or neither. In general, a person's psychological gender cannot be observed from the external characteristics of people. And even if some user's attributes such as physical gender and age can be found in social networks, it is difficult to find out the psychological gender of users. Apart from the fact that it will be hidden as the user's privacy, the main reason is that users' behaviors in social networks have no direct relationship with psychological gender. In this paper, we choose a special mobile social network to get around these limitations. Specifically, this paper is based on a real-world large gay social network of more than 10 000 000 users. In this mobile social network, we can indirectly mark the psychological gender of users by their sexual orientation. We collect 1000 users, including their characters, followers, followees and the same data for their followers and followees. Then, we examine an extensive set of features that can be extracted from the data set and train some classifiers to automatically identify the user's psychological gender. In the special mobile social network, a user's psychological gender is related to his characteristics, which include his personal characteristics, dating preference, and so on. This paper mainly analyzes users' personal characteristics, triads, and triadic evolution to infer their psychological gender. The experiments show that all these features are indeed effective in the classification, and even the features considered in previous studies have different implications with gender identity on other social networks. And it turns out that psychological gender as a kind of privacy can be leaked by these unintentional behaviors, and so we give some practical suggestions at the end of this paper to protect users' privacy.
收录类别:EI;SCOPUS;SCIE;SSCI
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061330797&doi=10.1109%2fACCESS.2019.2892949&partnerID=40&md5=58cf2c58227f25554393f796fc4e3e19
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