论文标题
使用内容和活动功能在Twitter中基于机器学习的方法检测抑郁症的方法
Machine Learning-based Approach for Depression Detection in Twitter Using Content and Activity Features
论文作者
论文摘要
Facebook，Twitter和Instagram等社交媒体渠道永远改变了我们的世界。现在，人们比以往任何时候都变得越来越紧密，并揭示了一种数字角色。尽管社交媒体当然具有几个非凡的功能，但这些功能也是不可否认的。最近的研究表明，社交媒体站点的大量使用与抑郁症的增加之间存在相关性。本研究旨在利用机器学习技术，以根据他/她的网络行为和推文来检测可能的沮丧Twitter用户。为此，我们培训和测试了分类器，以区分用户是否使用从其网络和推文中提取的功能和推文中提取的功能。结果表明，使用越多的功能，检测到抑郁症用户的准确性和F量得分就越高。此方法是一种数据驱动的，预测的方法，用于早期发现抑郁症或其他精神疾病。这项研究的主要贡献是特征的探索部分及其对检测抑郁水平的影响。
Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/ her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.
