Milne David N, McCabe Kathryn L, Calvo Rafael A
School of Information, Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology, Sydney, Sydney, Australia.
School of Electrical and Information Engineering, University of Sydney, Sydney, Australia.
J Med Internet Res. 2019 Apr 26;21(4):e11410. doi: 10.2196/11410.
Online peer support forums require oversight to ensure they remain safe and therapeutic. As online communities grow, they place a greater burden on their human moderators, which increases the likelihood that people at risk may be overlooked. This study evaluated the potential for machine learning to assist online peer support by directing moderators' attention where it is most needed.
This study aimed to evaluate the accuracy of an automated triage system and the extent to which it influences moderator behavior.
A machine learning classifier was trained to prioritize forum messages as green, amber, red, or crisis depending on how urgently they require attention from a moderator. This was then launched as a set of widgets injected into a popular online peer support forum hosted by ReachOut.com, an Australian Web-based youth mental health service that aims to intervene early in the onset of mental health problems in young people. The accuracy of the system was evaluated using a holdout test set of manually prioritized messages. The impact on moderator behavior was measured as response ratio and response latency, that is, the proportion of messages that receive at least one reply from a moderator and how long it took for these replies to be made. These measures were compared across 3 periods: before launch, after an informal launch, and after a formal launch accompanied by training.
The algorithm achieved 84% f-measure in identifying content that required a moderator response. Between prelaunch and post-training periods, response ratios increased by 0.9, 4.4, and 10.5 percentage points for messages labelled as crisis, red, and green, respectively, but decreased by 5.0 percentage points for amber messages. Logistic regression indicated that the triage system was a significant contributor to response ratios for green, amber, and red messages, but not for crisis messages. Response latency was significantly reduced (P<.001), between the same periods, by factors of 80%, 80%, 77%, and 12% for crisis, red, amber, and green messages, respectively. Regression analysis indicated that the triage system made a significant and unique contribution to reducing the time taken to respond to green, amber, and red messages, but not to crisis messages, after accounting for moderator and community activity.
The triage system was generally accurate, and moderators were largely in agreement with how messages were prioritized. It had a modest effect on response ratios, primarily because moderators were already more likely to respond to high priority content before the introduction of triage. However, it significantly and substantially reduced the time taken for moderators to respond to prioritized content. Further evaluations are needed to assess the impact of mistakes made by the triage algorithm and how changes to moderator responsiveness impact the well-being of forum members.
在线同伴支持论坛需要监督以确保其安全性和治疗效果。随着在线社区的发展，它们给人工版主带来了更大的负担，这增加了有风险的人可能被忽视的可能性。本研究评估了机器学习通过将版主的注意力引导到最需要的地方来协助在线同伴支持的潜力。
本研究旨在评估自动分诊系统的准确性及其对版主行为的影响程度。
训练一个机器学习分类器，根据论坛消息需要版主关注的紧急程度将其优先分类为绿色、琥珀色、红色或危机级别。然后将其作为一组小部件注入到由ReachOut.com主办的一个受欢迎的在线同伴支持论坛中，ReachOut.com是一家澳大利亚的基于网络的青年心理健康服务机构，旨在早期干预年轻人心理健康问题的发作。使用一组手动优先排序的消息的保留测试集来评估系统的准确性。对版主行为的影响通过回复率和回复延迟来衡量，即收到版主至少一次回复的消息比例以及做出这些回复所需的时间。在三个时期对这些指标进行了比较：发布前、非正式发布后以及正式发布并伴有培训后。
该算法在识别需要版主回复的内容方面达到了84%的F值。在发布前和培训后期间，分别标记为危机、红色和绿色的消息的回复率分别提高了0.9、4.4和10.5个百分点，但琥珀色消息的回复率下降了5.0个百分点。逻辑回归表明，分诊系统对绿色、琥珀色和红色消息的回复率有显著贡献，但对危机消息没有贡献。在相同期间，危机、红色、琥珀色和绿色消息的回复延迟分别显著降低（P<.001），降低幅度分别为80%、80%、77%和12%。回归分析表明，在考虑版主和社区活动后，分诊系统对减少回复绿色、琥珀色和红色消息的时间有显著且独特的贡献，但对危机消息没有贡献。
分诊系统总体上是准确的，版主在很大程度上同意消息如何被优先排序。它对回复率有适度影响，主要是因为在引入分诊之前版主已经更有可能回复高优先级内容。然而，它显著且大幅减少了版主回复优先内容所需的时间。需要进一步评估来评估分诊算法所犯错误的影响以及版主反应性的变化如何影响论坛成员的幸福感。
