Harvard Medical School, Boston, MA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, CA.
Department of Biomedical Informatics, Harvard Medical School, Boston, MA.
Am J Obstet Gynecol. 2021 Jun;224(6):597.e1-597.e14. doi: 10.1016/j.ajog.2020.11.042. Epub 2020 Dec 9.
Contraceptive method choice is often strongly influenced by the experiences and opinions of one's social network. Although social media, including Twitter, increasingly influences reproductive-age individuals, discussion of contraception in this setting has yet to be characterized. Natural language processing, a type of machine learning in which computers analyze natural language data, enables this analysis.
This study aimed to illuminate temporal trends in attitudes toward long- and short-acting reversible contraceptive methods in tweets between 2006 and 2019 and establish social media platforms as alternate data sources for large-scale sentiment analysis on contraception.
We studied English-language tweets mentioning reversible prescription contraceptive methods between March 2006 (founding of Twitter) and December 2019. Tweets mentioning contraception were extracted using search terms, including generic or brand names, colloquial names, and abbreviations. We characterized and performed sentiment analysis on tweets. We used Mann-Kendall nonparametric tests to assess temporal trends in the overall number and the number of positive, negative, and neutral tweets referring to each method. The code to reproduce this analysis is available at https://github.com/hms-dbmi/contraceptionOnTwitter.
We extracted 838,739 tweets mentioning at least 1 contraceptive method. The annual number of contraception-related tweets increased considerably over the study period. The intrauterine device was the most commonly referenced method (45.9%). Long-acting methods were mentioned more often than short-acting ones (58% vs 42%), and the annual proportion of long-acting reversible contraception-related tweets increased over time. In sentiment analysis of tweets mentioning a single contraceptive method (n=665,064), the greatest proportion of all tweets was negative (65,339 of 160,713 tweets with at least 95% confident sentiment, or 40.66%). Tweets mentioning long-acting methods were nearly twice as likely to be positive compared with tweets mentioning short-acting methods (19.65% vs 10.21%; P<.002).
Recognizing the influence of social networks on contraceptive decision making, social media platforms may be useful in the collection and dissemination of information about contraception.
避孕方法的选择通常受到社交网络中个人经验和意见的强烈影响。尽管社交媒体（包括 Twitter）越来越多地影响着育龄人群，但在这种环境下讨论避孕问题尚未得到充分的研究。自然语言处理是一种机器学习，它使计算机能够分析自然语言数据，从而能够进行这种分析。
本研究旨在阐明 2006 年至 2019 年间在 Twitter 上关于长效和短效可逆避孕方法的态度的时间趋势，并将社交媒体平台确立为避孕问题的大规模情感分析的替代数据来源。
我们研究了 2006 年 3 月（Twitter 的成立）至 2019 年 12 月期间提到可逆转处方避孕方法的英语推文。使用搜索词（包括通用名或品牌名、口语名称和缩写）提取提到避孕的推文。我们对推文进行了描述和情感分析。我们使用曼-肯德尔非参数检验评估了提到每种方法的推文总数以及正面、负面和中性推文数量的时间趋势。重现此分析的代码可在 https://github.com/hms-dbmi/contraceptionOnTwitter 获得。
我们提取了 838739 条提到至少 1 种避孕方法的推文。在研究期间，与避孕相关的推文数量大幅增加。宫内节育器是最常被提及的方法（45.9%）。长效方法比短效方法更常被提及（58%比 42%），并且随着时间的推移，长效可逆避孕相关推文的年比例有所增加。在对提到单个避孕方法的推文（n=665064）进行情感分析中，所有推文中比例最大的是负面推文（在有至少 95%置信度的情感的 160713 条推文中，有 65339 条为负面推文，占 40.66%）。与提到短效方法的推文相比，提到长效方法的推文更有可能是正面的（19.65%比 10.21%；P<.002）。
认识到社交网络对避孕决策的影响，社交媒体平台可能在收集和传播有关避孕的信息方面具有作用。
