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利用结构主题模型对韩国胰腺癌的在线信息进行分析。

Online information analysis on pancreatic cancer in Korea using structural topic model.

机构信息

Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.

Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea.

出版信息

Sci Rep. 2022 Jun 23;12(1):10622. doi: 10.1038/s41598-022-14506-1.

Abstract

Inappropriate information on a deadly and rare disease can make people vulnerable to problematic decisions, leading to irreversible bad outcomes. This study explored online information exchanges on pancreatic cancer. We collected 35,596 questions and 83,888 answers related to pancreatic cancer from January 1, 2003 to May 31, 2020, from Naver, the most popular Korean web portal. We also collected 8495 news articles related to pancreatic cancer during the same period. The study methods employed were structural topic modeling, keyword frequency analysis, and qualitative coding of medical professionals. The number of questions and news articles increased over time. In Naver's questions, topics on symptoms and diagnostic tests regarding pancreatic cancer increased in proportion. The news topics on new technologies related to pancreatic cancer from various companies increased as well. The use of words related to back pain-which is not an important early symptom in pancreatic cancer-and biomarker tests using blood increased over time in Naver's questions. Based on 100 question samples related to symptoms and diagnostic tests and an analysis of the threaded answers' appropriateness, there was considerable misinformation and commercialized information in both categories.

摘要

关于致命且罕见疾病的不当信息可能会使人们容易做出有问题的决策,从而导致不可逆转的不良后果。本研究探讨了胰腺癌的在线信息交流。我们从 2003 年 1 月 1 日至 2020 年 5 月 31 日,从韩国最受欢迎的门户网站 Naver 上收集了 35596 个与胰腺癌相关的问题和 83888 个答案,同时还收集了同期与胰腺癌相关的 8495 篇新闻文章。我们采用的研究方法是结构主题建模、关键词频率分析以及医疗专业人员的定性编码。问题和新闻文章的数量随时间推移而增加。在 Naver 的问题中,与胰腺癌症状和诊断测试相关的主题比例增加。与来自不同公司的胰腺癌新技术相关的新闻主题也有所增加。在 Naver 的问题中,与背痛相关的词汇(这不是胰腺癌的重要早期症状)以及使用血液的生物标志物测试的使用频率随着时间的推移而增加。基于 100 个与症状和诊断测试相关的问题样本以及对线程答案适当性的分析,这两个类别都存在相当多的错误信息和商业化信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c73c/9226061/19e5534f5dbc/41598_2022_14506_Fig1_HTML.jpg

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