背景:从各种非传统资源收集的患者健康数据,通常被称为真实世界数据,可以成为健康和社会科学研究的关键信息来源。社交媒体平台,如Twitter(Twitter,Inc),提供大量的真实世界数据。将社交媒体数据纳入科学研究的一个重要方面是确定发布这些数据的用户的人口特征。年龄和性别被认为是评估样本代表性的关键人口统计学,并使研究人员能够有效研究亚组和差异。然而,破译社交媒体用户的年龄和性别带来了挑战。
目的:本范围界定综述旨在总结有关Twitter用户年龄和性别预测的现有文献,并提供所使用方法的概述。
方法:我们搜索了15个电子数据库并进行了参考检查,以确定符合我们纳入标准的相关研究:使用计算方法预测Twitter用户年龄或性别的研究。筛选过程由2名研究人员独立进行,以确保纳入研究的准确性和可靠性。
结果:在检索到的最初684项研究中,74项(10.8%)研究符合我们的纳入标准。在这74项研究中,42(57%)专注于预测性别,8(11%)专注于预测年龄,和24(32%)预测年龄和性别的组合。性别预测主要是作为二元分类任务进行的,报告的方法性能范围为0.58至0.96F1评分或0.51至0.97准确性。年龄预测方法在分类分组方面有所不同,具有更高的报告性能范围,范围从0.31到0.94F1分数或0.43到0.86的准确性。研究的异质性和不同绩效指标的报告使得定量综合结果和得出明确结论具有挑战性。
结论:我们的评论发现,尽管预测Twitter用户年龄和性别的自动化方法已经发展到结合深度神经网络等技术,很大一部分的尝试依赖于传统的机器学习方法,这表明有可能通过使用更高级的方法来提高这些任务的性能。性别预测通常比年龄预测取得更高的报告表现。然而,缺乏标准化的绩效指标报告或标准注释语料库来评估所使用的方法阻碍了对方法的任何有意义的比较。由于收集和标记研究中使用的数据而产生的潜在偏见被认为是一个问题,强调在未来的研究中需要仔细考虑和减轻偏见。这个范围审查提供了对用于预测Twitter用户的年龄和性别的方法的有价值的见解,以及与这些方法相关的挑战和考虑因素。
BACKGROUND: Patient health data collected from a variety of nontraditional resources, commonly referred to as real-world data, can be a key information source for health and social science research. Social media platforms, such as Twitter (Twitter, Inc), offer vast amounts of real-world data. An important aspect of incorporating social media data in scientific research is identifying the demographic characteristics of the users who posted those data. Age and gender are considered key demographics for assessing the representativeness of the sample and enable researchers to study subgroups and disparities effectively. However, deciphering the age and gender of social media users poses challenges.
OBJECTIVE: This scoping review aims to summarize the existing literature on the prediction of the age and gender of Twitter users and provide an overview of the methods used.
METHODS: We searched 15 electronic databases and carried out reference checking to identify relevant studies that met our inclusion criteria: studies that predicted the age or gender of Twitter users using computational methods. The screening process was performed independently by 2 researchers to ensure the accuracy and reliability of the included studies.
RESULTS: Of the initial 684 studies retrieved, 74 (10.8%) studies met our inclusion criteria. Among these 74 studies, 42 (57%) focused on predicting gender, 8 (11%) focused on predicting age, and 24 (32%) predicted a combination of both age and gender. Gender prediction was predominantly approached as a binary classification task, with the reported performance of the methods ranging from 0.58 to 0.96 F1-score or 0.51 to 0.97 accuracy. Age prediction approaches varied in terms of classification groups, with a higher range of reported performance, ranging from 0.31 to 0.94 F1-score or 0.43 to 0.86 accuracy. The heterogeneous nature of the studies and the reporting of dissimilar performance metrics made it challenging to quantitatively synthesize results and draw definitive conclusions.
CONCLUSIONS: Our review found that although automated methods for predicting the age and gender of Twitter users have evolved to incorporate techniques such as deep neural networks, a significant proportion of the attempts rely on traditional machine learning methods, suggesting that there is potential to improve the performance of these tasks by using more advanced methods. Gender prediction has generally achieved a higher reported performance than age prediction. However, the lack of standardized reporting of performance metrics or standard annotated corpora to evaluate the methods used hinders any meaningful comparison of the approaches. Potential biases stemming from the collection and labeling of data used in the studies was identified as a problem, emphasizing the need for careful consideration and mitigation of biases in future studies. This scoping review provides valuable insights into the methods used for predicting the age and gender of Twitter users, along with the challenges and considerations associated with these methods.