"We make choices we think are going to save us": Debate and stance identification for online breast cancer CAM discussions.
"We make choices we think are going to save us": Debate and stance identification for online breast cancer CAM discussions.
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“我们做出我们认为能够拯救我们的选择”：在线乳腺癌 CAM 讨论的辩论和立场识别。
DOI：
10.1145/3041021.3055134
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Elhadad,Noémie
中科院分区：
文献类型：
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作者：
Zhang,Shaodian;Qiu,Lin;Chen,Frank;Zhang,Weinan;Yu,Yong;Elhadad,Noémie
患者在网上健康社区讨论补充和替代医学（CAM）。有时，患者对cam相关问题的矛盾意见会引发社区的争论。本文的目的是确定这样的争论，确定有争议的CAM疗法在一个流行的在线乳腺癌社区，以及患者对他们的立场。为了扩展我们的分析，我们训练了一组分类器。我们首先构建了一个基于长短期记忆神经网络（LSTM）叠加在卷积神经网络（CNN）上的监督分类器，以自动检测一个流行的乳腺癌论坛上与cam相关的辩论。成员在这些辩论中的立场也被基于cnn的分类器识别出来。最后，对分类器自动标记为辩论的帖子进行分析，以探索哪种特定的CAM疗法比其他疗法更容易引发辩论。我们的方法能够检测到F分为77%的CAM辩论，并识别F分为70%的立场。辩论分类器将大约1/6的cam相关帖子识别为辩论。大约60%的与CAM相关的辩论帖子代表了对CAM使用的支持立场。定性分析表明，一些特定的治疗方法，如Gerson疗法和苦杏仁素的使用，经常引发乳腺癌社区成员的争论。本研究表明，神经网络可以有效地定位有争议的CAM疗法的使用和有效性的争论，并有助于理解患者对这些争议问题的看法。至于针对乳腺癌的CAM，患者对其有效性的看法各不相同。许多特定的治疗方法经常引发争论，值得在未来的工作中进行更多的探索。
Patients discuss complementary and alternative medicine (CAM) in online health communities. Sometimes, patients' conflicting opinions toward CAM-related issues trigger debates in the community. The objectives of this paper are to identify such debates, identify controversial CAM therapies in a popular online breast cancer community, as well as patients' stances towards them. To scale our analysis, we trained a set of classifiers. We first constructed a supervised classifier based on a long short-term memory neural network (LSTM) stacked over a convolutional neural network (CNN) to detect automatically CAM-related debates from a popular breast cancer forum. Members' stances in these debates were also identified by a CNN-based classifier. Finally, posts automatically flagged as debates by the classifier were analyzed to explore which specific CAM therapies trigger debates more often than others. Our methods are able to detect CAM debates with F score of 77%, and identify stances with F score of 70%. The debate classifier identified about 1/6 of all CAM-related posts as debate. About 60% of CAM-related debate posts represent the supportive stance toward CAM usage. Qualitative analysis shows that some specific therapies, such as Gerson therapy and usage of laetrile, trigger debates frequently among members of the breast cancer community. This study demonstrates that neural networks can effectively locate debates on usage and effectiveness of controversial CAM therapies, and can help make sense of patients' opinions on such issues under dispute. As to CAM for breast cancer, perceptions of their effectiveness vary among patients. Many of the specific therapies trigger debates frequently and are worth more exploration in future work.