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Classifying User Intention and Social Support Types in Online Healthcare Discussions

机译：在线医疗保健讨论中对用户意图和社会支持类型进行分类

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With the development of online healthcare social media, a large volume of user generated content are available in different health discussion forums or groups. Healthcare social media sites could empower patients to play a substantial role in their treatment by acquiring knowledge and support through actively involving in online discussions and interactions. However, users may have difficulty to find relevant topics or peers in these online health forums with a large amount of unstructured information. Most recommendation systems rely on content-based approach to recommend peers or discussions to their participants. However, in healthcare social media sites, content-based approach is not sufficient because health consumers may have different intentions of participation or may be interest in different types of support even if the content matches their interest. Based on previous studies, we utilize Nai?ve Bayes methods and propose two tasks for classifying posts and comments on Quit Stop forum, an online community for smoking cessation intervention, respectively: (1) classification of intentions and (2) classification of social support types. Different text feature sets and user health feature sets are selected to develop classifiers. Taking different evaluation indicators as optimizing goals, we develop genetic algorithms to combine classifiers with different feature sets and optimize the classification results. It is found that for post classification, integrating text and health features could achieve the highest precision, recall and F1 measure. For comment classification, combining different text features could reach the best result. In the future, the classification result could be applied to developing recommender systems for topic recommendation and user prediction of online health forums.
机译：随着在线医疗保健社交媒体的发展，在不同的健康讨论论坛或团体中提供了大量的用户生成的内容。医疗保健社交媒体网站可以通过积极涉及在线讨论和互动来获取知识和支持，使患者在治疗中发挥重要作用。但是，用户可能很难在这些在线健康论坛中找到具有大量非结构化信息的相关主题或同行。大多数推荐系统依赖于基于内容的方法，向他们的参与者推荐同行或讨论。然而，在医疗保健社交媒体网站中，基于内容的方法是不够的，因为即使内容与他们的兴趣匹配，卫生消费者可能对不同类型的支持感兴趣。基于以前的研究，我们利用Nai？ve贝雷斯方法，并提出了两项​​任务，用于分类职位和评论，分别是吸烟干预的在线社区的退出停止论坛：（1）意图分类和（2）社会支持分类类型。选择不同的文本功能集和用户健康功能集以开发分类器。采用不同的评估指标作为优化目标，我们开发遗传算法，将分类器与不同的特征集结合，并优化分类结果。有人发现，对于Post分类，集成文本和健康功能可以实现最高精度，召回和F1测量。对于评论分类，组合不同的文本功能可能达到最佳结果。将来，分类结果可以应用于开发用于主题推荐和在线健康论坛的用户预测的推荐系统。

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