Personality Predictions Based on User Behavior on the Facebook Social Media Platform
Personality Predictions Based on User Behavior on the Facebook Social Media Platform
复制标题
基于 Facebook 社交媒体平台上的用户行为的性格预测
DOI：
10.1109/access.2018.2876502
复制
发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Yang, Liang
中科院分区：
文献类型：
--
作者：
Tadesse, Michael M.;Lin, Hongfei;Yang, Liang
随着社交网络的发展，已经开发出各种各样的方法来根据用户的社交活动和语言使用习惯来定义用户的个性。不同的机器学习算法、数据源和特征集的具体方法有所不同。本文的目的是基于 Big 5 模型的不同特征和度量来研究 Facebook 用户个性特征的可预测性。我们使用 myPersonality 项目数据集检查与人格交互相关的社交网络结构和语言特征的存在。我们分析和比较了四种机器学习模型，并执行每个特征集与人格特质之间的相关性。预测精度结果表明，即使在相同数据集下进行测试，基于 XGBoost 分类器构建的性格预测系统在所有特征集上的表现均优于平均基线，最高预测精度为 74.2%。利用个体社交网络分析特征集对外向性特征进行了最佳预测，达到了78.6%的较高性格预测准确率。
With the development of social networks, a large variety of approaches have been developed to define users' personalities based on their social activities and language use habits. Particular approaches differ with regard to different machine learning algorithms, data sources, and feature sets. The goal of this paper is to investigate the predictability of the personality traits of Facebook users based on different features and measures of the Big 5 model. We examine the presence of structures of social networks and linguistic features relative to personality interactions using the myPersonality project data set. We analyze and compare four machine learning models and perform the correlation between each of the feature sets and personality traits. The results for the prediction accuracy show that even if tested under the same data set, the personality prediction system built on the XGBoost classifier outperforms the average baseline for all the feature sets, with a highest prediction accuracy of 74.2%. The best prediction performance was reached for the extraversion trait by using the individual social network analysis features set, which achieved a higher personality prediction accuracy of 78.6%.