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What is the best method of family planning for me?: a text mining analysis of messages between users and agents of a digital health service in Kenya

机译：对我来说最好的计划生育方法是什么？：肯尼亚数字医疗服务用户与代理商之间的消息的文本挖掘分析

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>Background: Text message-based interventions have been shown to have consistently positive effects on health improvement and behavior change. Some studies suggest that personalization, tailoring, and interactivity can increase efficacy. With the rise in artificial intelligence and its incorporation into interventions, there is an opportunity to rethink how these characteristics are designed for greater effect. A key step in this process is to better understand how users engage with interventions. In this paper, we apply a text mining approach to characterize the ways that Kenyan men and women communicated with the first iterations of askNivi, a free sexual and reproductive health information service.  >Methods: We tokenized and processed more than 179,000 anonymized messages that users exchanged with live agents, enabling us to count word frequency overall, by sex, and by age/sex cohorts. We also conducted two manual coding exercises: (1) We manually classified the intent of 3,834 user messages in a training dataset; and (2) We manually coded all conversations between a random subset of 100 users who engaged in extended chats.  >Results: Between September 2017 and January 2019, 28,021 users (mean age 22.5 years, 63% female) sent 87,180 messages to askNivi, and 18 agents sent 92,429 replies. Users wrote most often about family planning methods, contraception, side effects, pregnancy, menstruation, and sex, but we observed different patterns by sex and age. User intents largely reflected the marketing focus on reproductive health, but other topics emerged. Most users sought factual information, but requests for advice and symptom reports were common.  >Conclusions: Young people in Kenya have a great desire for accurate and reliable information on health and wellbeing, which is easy to access and trustworthy. Text mining is one way to better understand how users engage with interventions like askNivi and maximize what artificial intelligence has to offer.
机译：>背景：基于文本消息的干预措施已被证明对健康改善和行为改变具有持续的积极影响。一些研究表明，个性化，定制和交互性可以提高功效。随着人工智能的发展以及将其纳入干预措施中，有机会重新考虑如何设计这些特性以产生更大的效果。此过程中的关键步骤是更好地了解用户如何进行干预。在本文中，我们采用文本挖掘方法来描述肯尼亚男性和女性与免费的性健康和生殖健康信息服务AskNivi的第一个迭代版本进行交流的方式。 >方法：我们标记并处理了超过17.9万条匿名消息，这些消息是用户与实时代理人交换的，从而使我们能够按性别，年龄和性别对整个词频进行计数。我们还进行了两次手动编码练习：（1）在训练数据集中手动分类了3834条用户消息的意图； （2）我们对参与扩展聊天的100个用户的随机子集之间的所有对话进行了手动编码。 >结果：在2017年9月至2019年1月之间，有28,021位用户（平均年龄22.5岁，女性占63％）发送了87,180条消息给askNivi，18位代理商发送了92,429条回复。用户最常写有关计划生育方法，避孕，副作用，怀孕，月经和性别的信息，但我们观察到的性别和年龄不同。用户意图在很大程度上反映了市场对生殖健康的关注，但是其他话题也出现了。大多数用户都寻求事实信息，但要求提供建议和症状报告的情况却很普遍。 >结论：肯尼亚的年轻人非常希望获得关于健康和福祉的准确，可靠的信息，这些信息易于获取且值得信赖。文本挖掘是一种更好地了解用户如何参与诸如AskNivi之类的干预措施并最大程度地提供人工智能的方法。

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