首页> 外文会议>ICMLA 2012;International Conference on Machine Learning and Applications >Predicting Dark Triad Personality Traits from Twitter Usage and a Linguistic Analysis of Tweets
【24h】

Predicting Dark Triad Personality Traits from Twitter Usage and a Linguistic Analysis of Tweets

机译:从Twitter使用情况和推文的语言分析预测黑社会的人格特征

获取原文

摘要

Social media sites are now the most popular destination for Internet users, providing social scientists with a great opportunity to understand online behaviour. There are a growing number of research papers related to social media, a small number of which focus on personality prediction. To date, studies have typically focused on the Big Five traits of personality, but one area which is relatively unexplored is that of the anti-social traits of narcissism, Machiavellians and psychopathy, commonly referred to as the Dark Triad. This study explored the extent to which it is possible to determine anti-social personality traits based on Twitter use. This was performed by comparing the Dark Triad and Big Five personality traits of 2,927 Twitter users with their profile attributes and use of language. Analysis shows that there are some statistically significant relationships between these variables. Through the use of crowd sourced machine learning algorithms, we show that machine learning provides useful prediction rates, but is imperfect in predicting an individual's Dark Triad traits from Twitter activity. While predictive models may be unsuitable for predicting an individual's personality, they may still be of practical importance when models are applied to large groups of people, such as gaining the ability to see whether anti-social traits are increasing or decreasing over a population. Our results raise important questions related to the unregulated use of social media analysis for screening purposes. It is important that the practical and ethical implications of drawing conclusions about personal information embedded in social media sites are better understood.
机译:社交媒体网站现在是Internet用户最受欢迎的目的地,这为社会科学家提供了一个很好的机会来了解在线行为。与社交媒体相关的研究论文越来越多,其中有少数集中在个性预测上。迄今为止,研究通常集中在人格的五大特征上,但是相对较未探索的领域是自恋,反叛者和精神病的反社会特征,通常被称为“黑暗三合会”。这项研究探索了基于Twitter的使用来确定反社会人格特质的可能性。这是通过比较2927位Twitter用户的Dark Triad和Big Five人格特征与他们的个人资料属性和语言使用来进行的。分析表明,这些变量之间存在统计上显着的关系。通过使用众包的机器学习算法,我们证明了机器学习提供了有用的预测率,但是在通过Twitter活动预测个人的Dark Triad特征方面并不完善。尽管预测模型可能不适合预测个人的性格,但当将模型应用于大批人时,例如在了解人口中反社会特征是在增加还是在减少时,预测模型仍然具有实际重要性。我们的结果提出了与未经筛选使用社交媒体分析相关的重要问题。重要的是,必须更好地理解得出有关嵌入社交媒体网站中的个人信息的结论的实践和道德意义。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号