A text analytics approach for mining public discussions in online cancer forum: Analysis of multi-intent lung cancer treatment dataset
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shah, Adnan Muhammad | - |
dc.contributor.author | Lee, Kang Yoon | - |
dc.contributor.author | Hidayat, Abdullah | - |
dc.contributor.author | Falchook, Aaron | - |
dc.contributor.author | Muhammad, Wazir | - |
dc.date.accessioned | 2024-04-26T13:00:21Z | - |
dc.date.available | 2024-04-26T13:00:21Z | - |
dc.date.issued | 2024-04 | - |
dc.identifier.issn | 1386-5056 | - |
dc.identifier.issn | 1872-8243 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/91054 | - |
dc.description.abstract | Background: Online cancer forums (OCF) are increasingly popular platforms for patients and caregivers to discuss, seek information on, and share opinions about diseases and treatments. This interaction generates a substantial amount of unstructured text data, necessitating deeper exploration. Using time series data, our study exploits topic modeling in the novel domain of online cancer forums (OCFs) to identify meaningful topics and changing dynamics of online discussion across different lung cancer treatment intent groups. Methods: For this purpose, a dataset comprising 27,998 forum posts about lung cancer was collected from three OCFs: lungcancer.net, lungevity.org, and reddit.com, spanning the years 2016 to 2018. Results: The analysis reflects the public discussion on multi-intent lung cancer treatment over time, taking into account seasonal variations. Discussions on cancer symptoms and prevention garnered the most attention, dominating both curative and palliative care discussions. There were distinct seasonal peaks: curative care topics surged from winter to late spring, while palliative care topics peaked from late summer to mid-autumn. Keyword analysis highlighted that lung cancer diagnosis and treatment were primary topics, whereas cancer prevention and treatment outcomes were predominant across multi-care contexts. For the study period, curative care discussions predominantly revolved around informational support and disease syndromes. In contrast, social support and cancer prevention prevailed in the palliative care context. Notably, topics such as cancer screening and cancer treatment exhibit pronounced seasonal variations in curative care, peaking in frequency during the summers (May to August) of the study period. Meanwhile, the topic of tumor control within palliative care showed significant seasonal influence during the winters and summers of 2017 and 2018. Conclusion: Our text analysis approach using OCF data shows potential for computational methods in this novel domain to gain insights into trends in public cancer communication and seasonal variations for a better understanding of improving personalized care, decision support, treatment outcomes, and quality of life. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | ELSEVIER IRELAND LTD | - |
dc.title | A text analytics approach for mining public discussions in online cancer forum: Analysis of multi-intent lung cancer treatment dataset | - |
dc.type | Article | - |
dc.identifier.wosid | 001188219400001 | - |
dc.identifier.doi | 10.1016/j.ijmedinf.2024.105375 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, v.184 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85185402170 | - |
dc.citation.title | INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS | - |
dc.citation.volume | 184 | - |
dc.type.docType | Article | - |
dc.publisher.location | 아일랜드 | - |
dc.subject.keywordAuthor | Lung cancer | - |
dc.subject.keywordAuthor | Text analysis | - |
dc.subject.keywordAuthor | Topic modeling | - |
dc.subject.keywordAuthor | Cancer forums | - |
dc.subject.keywordAuthor | Seasonal variation | - |
dc.subject.keywordAuthor | Trend analysis | - |
dc.subject.keywordPlus | COMMUNITIES | - |
dc.subject.keywordPlus | FRAMEWORK | - |
dc.subject.keywordPlus | SURVIVORS | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Health Care Sciences & Services | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Health Care Sciences & Services | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.