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虚拟现实视频(《漫步痴呆症》)对YouTube用户的影响:主题建模分析

Impact of a Virtual Reality Video ("A Walk-Through Dementia") on YouTube Users: Topic Modeling Analysis.

作者信息

Li Xiaoli, Liu Xiaoyu, Yin Cheng, Collins Sandra, Alanazi Eman

机构信息

School of Health Sciences, Southern Illinois University Carbondale, 1365 Douglas Drive, ASA Building - Mail Code 6615, Carbondale, IL, 62901, United States, 1 9407583557.

College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO, United States.

出版信息

JMIR Form Res. 2025 Apr 16;9:e67755. doi: 10.2196/67755.

DOI:10.2196/67755
PMID:40237652
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12016674/
Abstract

BACKGROUND

Emerging research has highlighted the potential of virtual reality (VR) as a tool for training health care students and professionals in care skills for individuals with Alzheimer disease and related dementias (ADRD). However, there is limited research on the use of VR to engage the general public in raising awareness about ADRD.

OBJECTIVE

This research aimed to examine the impact of the VR video "A Walk-Through Dementia" on YouTube users by analyzing their posts.

METHODS

We collected 12,754 comments from the VR video series "A Walk-Through Dementia," which simulates the everyday challenges faced by individuals with ADRD, providing viewers with an immersive experience of the condition. Topic modeling was conducted to gauge viewer opinions and reactions to the videos. A pretrained Bidirectional Encoder Representations from Transformers (BERT) model was used to transform the YouTube comments into high-dimensional vector embeddings, allowing for systematic identification and detailed analysis of the principal topics and their thematic structures within the dataset.

RESULTS

We identified the top 300 most frequent words in the dataset and categorized them into nouns, verbs, and adjectives or adverbs using a part-of-speech tagging model, fine-tuned for accurate tagging tasks. The topic modeling process identified eight8 initial topics based on the most frequent words. After manually reviewing the 8 topics and the content of the comments, we synthesized them into 5 themes. The predominant theme, represented in 2917 comments, centered on users' personal experiences with the impact of ADRD on patients and caregivers. The remaining themes were categorized into 4 main areas: positive reactions to the VR videos, challenges faced by individuals with ADRD, the role of caregivers, and learning from the VR videos.

CONCLUSIONS

Using topic modeling, this study demonstrated that VR applications serve as engaging and experiential learning tools, offering the public a deeper understanding of life with ADRD. Future research should explore additional VR applications on social media, as they hold the potential to reach wider audiences and effectively disseminate knowledge about ADRD.

摘要

背景

新兴研究突出了虚拟现实(VR)作为一种工具,在培训医护专业学生和从业人员掌握针对阿尔茨海默病及相关痴呆症(ADRD)患者的护理技能方面的潜力。然而,关于使用VR让公众参与提高对ADRD的认识的研究有限。

目的

本研究旨在通过分析YouTube用户的帖子,考察VR视频《漫步痴呆症》对他们的影响。

方法

我们从VR视频系列《漫步痴呆症》中收集了12754条评论,该系列模拟了ADRD患者所面临的日常挑战,为观众提供了对这种病症的沉浸式体验。进行主题建模以评估观众对视频的看法和反应。使用预训练的来自变换器的双向编码器表征(BERT)模型将YouTube评论转换为高维向量嵌入,以便系统地识别和详细分析数据集中的主要主题及其主题结构。

结果

我们确定了数据集中最常出现的300个单词,并使用针对精确标记任务进行微调的词性标记模型将它们分类为名词、动词、形容词或副词。主题建模过程基于最常出现的单词确定了8个初始主题。在人工审查这8个主题和评论内容后,我们将它们综合为5个主题。占主导地位的主题出现在2917条评论中,集中在用户关于ADRD对患者和护理人员影响的个人经历上。其余主题分为4个主要领域:对VR视频的积极反应、ADRD患者面临的挑战、护理人员的角色以及从VR视频中学习。

结论

本研究通过主题建模表明,VR应用是引人入胜的体验式学习工具,能让公众更深入地了解ADRD患者的生活。未来研究应探索社交媒体上更多的VR应用,因为它们有潜力触及更广泛的受众并有效传播有关ADRD的知识。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/233b/12016674/d3f1ebbf717b/formative-v9-e67755-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/233b/12016674/f629eb9fdaf6/formative-v9-e67755-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/233b/12016674/0cbd5bd62507/formative-v9-e67755-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/233b/12016674/8575d1b27ccf/formative-v9-e67755-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/233b/12016674/d3f1ebbf717b/formative-v9-e67755-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/233b/12016674/f629eb9fdaf6/formative-v9-e67755-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/233b/12016674/0cbd5bd62507/formative-v9-e67755-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/233b/12016674/8575d1b27ccf/formative-v9-e67755-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/233b/12016674/d3f1ebbf717b/formative-v9-e67755-g004.jpg

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本文引用的文献

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JMIR Aging. 2024 Jul 9;7:e51520. doi: 10.2196/51520.
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Geriatr Nurs. 2022 Nov-Dec;48:269-273. doi: 10.1016/j.gerinurse.2022.10.013. Epub 2022 Nov 1.
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中文社交媒体中公众对痴呆症的话语和情绪:微博帖子的机器学习分析。
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Gerontol Geriatr Educ. 2023 Apr-Jun;44(2):211-222. doi: 10.1080/02701960.2022.2067850. Epub 2022 Apr 22.
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