Online social networks have become demanded ways for users to show themselves and connect and share information with each other among these social networks. Facebook is the most popular social network. Personality recognition is one of the new challenges between investigators in social networks. This paper presents a hypothesis that users by similar personality are expected to display mutual behavioral patterns when cooperating through social networks. With the goal of personality recognition in terms of analyzing user activity within Facebook, we collected information about the personality traits of users and their profiles on Facebook, hence we flourished an application using API Facebook. The participants of this study are 100 volunteers of Facebook users. We asked the participants to respond the NEO personality questionnaire in a period of 1 month in May 2012. At the end of this questionnaire, there was a link that asked the participants to permit the application to access their profiles. Based on all the collected data, classifiers were learned using different data mining techniques to recognize user personality by their profile and without filling out any questionnaire. With comparing classifiers’ results, the boosting-decision tree was our proposed model with 82.2% accuracy was more accurate than previous studies that were able to foresee personality according to the variables in their profiles in five factors for using it as a model for recognizing personality.

中文翻译：

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基于社交网络用户概况和人格五因素模型的人格分类

在线社交网络已成为用户在这些社交网络之间展示自己，相互联系和共享信息的必不可少的方式。Facebook是最受欢迎的社交网络。人格识别是社交网络中调查人员之间的新挑战之一。本文提出了一个假设，即具有相似个性的用户在通过社交网络进行合作时会表现出相互的行为模式。为了通过分析Facebook中的用户活动来识别个性，我们收集了有关用户的个性特征及其在Facebook上的个人资料的信息，因此我们开发了使用API​​ Facebook的应用程序。这项研究的参与者是Facebook用户的100名志愿者。我们要求参与者在2012年5月的1个月内回复NEO人格问卷。在该问卷的末尾，有一个链接，要求参与者允许应用程序访问其个人资料。根据收集到的所有数据，使用不同的数据挖掘技术学习分类器，以根据用户的个人资料识别用户个性，而无需填写任何调查表。通过比较分类器的结果，我们提出的提升决策树模型的准确度为82.2％，比以前的研究更为准确，以前的研究能够根据个人档案中的变量在五个因素中预见到人格，从而将其用作人格识别模型。有一个链接要求参与者允许应用程序访问其个人资料。根据收集到的所有数据，使用不同的数据挖掘技术学习分类器，以根据用户的个人资料识别用户个性，而无需填写任何调查表。通过比较分类器的结果，我们提出的提升决策树模型的准确度为82.2％，比以前的研究更为准确，以前的研究能够根据个人档案中的变量在五个因素中预见到人格，从而将其用作人格识别模型。有一个链接要求参与者允许应用程序访问其个人资料。根据收集到的所有数据，使用不同的数据挖掘技术学习分类器，以根据用户的个人资料识别用户个性，而无需填写任何调查表。通过比较分类器的结果，我们提出的提升决策树模型的准确度为82.2％，比以前的研究更为准确，以前的研究能够根据个人档案中的变量在五个因素中预见到人格，从而将其用作人格识别模型。