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首页> 外文期刊>Aslib journal of information management: New information perspectives >Predicting user personality with social interactions in Weibo
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Predicting user personality with social interactions in Weibo

机译：预测用户的个性与社会在微博互动

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Purpose The purposes of this paper are to (1) explore how personality traits pertaining to the dominance influence steadiness compliance model manifest themselves in terms of user interaction behavior on social media and (2) examine whether social interaction data on social media platforms can predict user personality. Design/methodology/approach Social interaction data was collected from 198 users of Sina Weibo, a popular social media platform in China. Their personality traits were also measured via questionnaire. Machine learning techniques were applied to predict the personality traits based on the social interaction data. Findings The results demonstrated that the proposed classifiers had high prediction accuracy, indicating that our approach is reliable and can be used with social interaction data on social media platforms to predict user personality. "Reposting," "being reposted," "commenting" and "being commented on" were found to be the key interaction features that reflected Weibo users' personalities, whereas "liking" was not found to be a key feature. Originality/value The findings of this study are expected to enrich personality prediction research based on social media data and to provide insights into the potential of employing social media data for the purpose of personality prediction in the context of the Weibo social media platform in China.
机译：目的本文的目的是(1)探讨人格特质有关主导地位影响稳定合规模式表现的用户交互行为对社会媒体和(2)检查是否社会互动社交媒体平台的数据可以预测用户的个性。设计/方法/方法社会互动数据收集从198年新浪微博的用户,一个在中国广受欢迎的社交媒体平台。人格特质也通过测量问卷调查。应用于预测为基础的人格特质在社会交互数据。结果表明,该分类器有较高的预测精度,表明我们的方法是可靠的,可以被用于社会交往社会数据媒体平台来预测用户的个性。“新任命”、“转发”和“评论”“评论”是关键交互特性,反映了微博用户的个性,而“喜欢”不存在是一个关键特性。本研究将丰富的个性基于社交媒体数据的预测研究并提供见解的潜力运用社会化媒体数据的目的人格预测中在中国的微博社交媒体平台。

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