Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.
Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Womens Health (Lond). 2020 Jan-Dec;16:1745506520949392. doi: 10.1177/1745506520949392.
We sought to evaluate whether there was variability in language used on social media across different time points of pregnancy (before, during, and after pregnancy, as well as by trimester and parity). Consenting patients shared access to their individual Facebook posts and electronic medical records. Random forest models trained on Facebook posts could differentiate first trimester of pregnancy from 3 months before pregnancy (F1 score = .63) and from a random 3-month time period (F1 score = .64). Posts during pregnancy were more likely to include themes about family (β = .22), food craving (β = .14), and date/times (β = .13), while posts 3 months prior to pregnancy included themes about social life (β = .30), sleep (β = .31), and curse words (β = .27), and 3 months post-pregnancy included themes of gratitude (β = .17), health appointments (β = .21), and religiosity (β = .18). Users who were pregnant for the first time were more likely to post about lack of sleep (β = .15), activities of daily living (β = .09), and communication (β = .08) compared with those who were pregnant after having a child who posted about others' birthdays (β = .16) and life events (.12). A better understanding about social media timelines can provide insight into lifestyle choices that are specific to pregnancy.
我们试图评估社交媒体上的语言使用是否存在不同的变化,这些变化包括妊娠的不同时间点(妊娠前、妊娠期间和妊娠后,以及妊娠早、中、晚期)和生育次数。同意参与的患者分享了他们个人的 Facebook 帖子和电子病历。基于 Facebook 帖子训练的随机森林模型可以区分妊娠早期和妊娠前 3 个月(F1 分数=0.63)以及随机的 3 个月时间(F1 分数=0.64)。妊娠期间的帖子更可能包含关于家庭(β=0.22)、食物渴望(β=0.14)和日期/时间(β=0.13)的主题,而妊娠前 3 个月的帖子则包含更多关于社交生活(β=0.30)、睡眠(β=0.31)和脏话(β=0.27)的主题,而产后 3 个月的帖子则包含感恩(β=0.17)、健康预约(β=0.21)和宗教信仰(β=0.18)的主题。第一次怀孕的用户更有可能发布关于睡眠不足(β=0.15)、日常生活活动(β=0.09)和沟通(β=0.08)的内容,而那些已经有孩子后再次怀孕的用户则更有可能发布关于他人生日(β=0.16)和生活事件(β=0.12)的内容。更好地了解社交媒体时间线可以深入了解特定于妊娠的生活方式选择。