This study explores the relationship between informational support seeking
questions, responses, and helpfulness ratings in online health communities. We
created a labeled data set of question-response pairs and developed multimodal
machine learning and deep learning models to reliably predict informational
support questions and responses. We employed explainable AI to reveal the
emotions embedded in informational support exchanges, demonstrating the
importance of emotion in providing informational support. This complex
interplay between emotional and informational support has not been previously
researched. The study refines social support theory and lays the groundwork for
the development of user decision aids. Further implications are discussed.

该研究探讨了在线健康社区中信息支持寻求问题、回答和有益评级之间的关系。我们创建了一个标记的问题 - 回答数据集，并开发了可靠地预测信息支持问题和回答的多模式机器学习和深度学习模型。我们采用可解释的人工智能揭示了信息支持交流中蕴含的情感，并展示了情感在提供信息支持中的重要性。情感与信息支持之间的这种复杂相互作用以前未曾研究。这项研究完善了社会支持理论，并为用户决策辅助工具的开发奠定了基础。进一步的影响也得到了讨论。

情绪在在线健康社区信息支持问答对中的作用：一种多模态深度学习方法

The Role of Emotions in Informational Support Question-Response Pairs in  Online Health Communities: A Multimodal Deep Learning Approach

In this paper, we develop an LLM-powered framework for the curation and
evaluation of emerging opinion mining in online health communities. We
formulate emerging opinion mining as a pairwise stance detection problem
between (title, comment) pairs sourced from Reddit, where post titles contain
emerging health-related claims on a topic that is not predefined. The claims
are either explicitly or implicitly expressed by the user. We detail (i) a
method of claim identification -- the task of identifying if a post title
contains a claim and (ii) an opinion mining-driven evaluation framework for
stance detection using LLMs.
We facilitate our exploration by releasing a novel test dataset, Long
COVID-Stance, or LC-stance, which can be used to evaluate LLMs on the tasks of
claim identification and stance detection in online health communities. Long
Covid is an emerging post-COVID disorder with uncertain and complex treatment
guidelines, thus making it a suitable use case for our task. LC-Stance contains
long COVID treatment related discourse sourced from a Reddit community. Our
evaluation shows that GPT-4 significantly outperforms prior works on zero-shot
stance detection. We then perform thorough LLM model diagnostics, identifying
the role of claim type (i.e. implicit vs explicit claims) and comment length as
sources of model error.

我们开发了一个基于 LLM 的框架，用于在线健康社区中新兴意见挖掘的策划和评估，以匹配 Reddit 中源自帖子标题和评论的（标题，评论）对之间的态度检测问题。我们还释放了一个新的测试数据集，名为 LC-Stance，用于评估在线健康社区中的声明识别和态度检测任务。我们的评估表明，GPT-4 在零 - shot 态度检测方面明显优于之前的工作，我们还通过 LLM 模型诊断发现声明类型和评论长度是模型错误的原因。

基于大型语言模型的在线健康话语中新兴观点挖掘范围

Scope of Large Language Models for Mining Emerging Opinions in Online  Health Discourse

Online health communities are a valuable source of information for patients
and physicians. However, such user-generated resources are often plagued by
inaccuracies and misinformation. In this work we propose a method for
automatically establishing the credibility of user-generated medical statements
and the trustworthiness of their authors by exploiting linguistic cues and
distant supervision from expert sources. To this end we introduce a
probabilistic graphical model that jointly learns user trustworthiness,
statement credibility, and language objectivity. We apply this methodology to
the task of extracting rare or unknown side-effects of medical drugs --- this
being one of the problems where large scale non-expert data has the potential
to complement expert medical knowledge. We show that our method can reliably
extract side-effects and filter out false statements, while identifying
trustworthy users that are likely to contribute valuable medical information.

本研究提供了一种基于语言线索和专家远程监督的方法，用于自动建立用户生成的医疗声明的可信度和作者的信任度。我们将这种方法应用于提取药物的罕见或未知副作用的任务，并证明其可以可靠地提取副作用和过滤错误的声明，同时确定有价值的医疗信息的可信用户。