Evidence map›Paper›PMID 42387063›Full record

ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2026

User profiles of young breast cancer survivors on Chinese social media: machine learning-based text mining analysis study.

Lulu Jiang, Xiyi Wang, Yanyan Liu, Futai Zou, Ran Yi, Zhaozhang Sun, Jiehui Xu, Yun Hu

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Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Lulu Jiang *School of Nursing, Shanghai Jiao Tong University, Huangpu District, No.227 Chongqing South Road, Shanghai, 200025, China.
Xiyi Wang *School of Nursing, Shanghai Jiao Tong University, Huangpu District, No.227 Chongqing South Road, Shanghai, 200025, China.
Yanyan LiuSchool of Nursing, Shanghai Jiao Tong University, Huangpu District, No.227 Chongqing South Road, Shanghai, 200025, China.
Futai ZouSchool of Computer Science, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Minhang District, Shanghai, 200240, China.
Ran YiSchool of Nursing, Shanghai Jiao Tong University, Huangpu District, No.227 Chongqing South Road, Shanghai, 200025, China.
Zhaozhang SunDepartment of Applied Health Sciences, School of Health Sciences, College of Medicine and Health, University of Birmingham, Birmingham, B15 2TT, United Kingdom.
Jiehui XuRenji Hospital, Shanghai Jiao Tong University School of Medicine, No. 160 Pujian Road, Pudong New Area, Shanghai, 200127, China. xujiehui@renji.com.
Yun HuSchool of Nursing, Shanghai Jiao Tong University, Huangpu District, No.227 Chongqing South Road, Shanghai, 200025, China. huyunsy@shsmu.edu.cn.

Funding

National Natural Science Foundation of China 71804112National Natural Science Foundation of China 72304183Shanghai Jiao Tong University: Humanities Youth Talent Cultivation Project 2023QN039Shanghai Jiao Tong University School of Medicine: Nursing Development Program SJTUHLXK2024
6 · The paper itself

Abstract

purposeSocial media is vital for improving healthcare access, especially among disadvantaged groups. During the COVID-19 pandemic, young breast cancer survivors (YBCSs) in China increasingly relied on social media for health information, shaping their experiences and needs. However, little is known about how their online behaviors changed during such crises. This study examines the characteristics and health needs of Chinese YBCSs on social media during the pandemic, providing evidence to inform public health management in emergencies.

methodsWe used web crawlers to collect 6,415 breast cancer-related posts from Sina Weibo and Zhihu (November 30, 2017-May 31, 2022). Posts were filtered using operational criteria, combining manual screening and machine learning models. Text mining and natural language processing were applied to construct multidimensional user profiles across three pandemic phases: pre-pandemic, outbreak, and normalization.

resultsIn total, 2,640 posts from YBCSs were included for analysis. YBCSs' online activity increased markedly during the outbreak (from 0.37 to 2.22 posts/hour), with peak engagement during leisure times but shorter active durations. Content shifted from treatment-focused discussions to collective encouragement and pandemic-related topics, then returned to disease management in the normalization phase. Sentiment was generally positive, with fluctuations during the outbreak and stabilization in the normalization phase (sentiment index 0.17-0.38).

conclusionThis study underscores the significant impact of the COVID-19 pandemic on YBCSs, highlighting shifts in temporal routines, content priorities, and emotions. The findings redefine the perspective on managing healthy lives of these vulnerable and fragile groups in the post-crises era, and emphasize the urgent need for timely health information support and equality in healthcare through social media platforms with machine-learning approaches.

Indexed as

Breast NeoplasmsCancer SurvivorsCOVID-19Data MiningMachine LearningSocial MediaAdultChinaFemaleHumansSARS-CoV-2Breast CancerCOVID-19 pandemicNatural language processingSocial mediaText mining

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.