ArticleJMIR cancer2024
Differing Content and Language Based on Poster-Patient Relationships on the Chinese Social Media Platform Weibo: Text Classification, Sentiment Analysis, and Topic Modeling of Posts on Breast Cancer.
Article in JMIR cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- AI-Driven Topic Modeling and Sentiment Analysis of Systemic Lupus Erythematosus Discussions on Social Media: Cross-Platform Study.Journal of medical Internet research · 2026Article
- Emotion Classification in Japanese Cancer Survivor Interview Narratives Using Sentiment Polarity and Plutchik Emotion Frameworks: Model Development and Evaluation Study.JMIR formative research · 2026Article
- LLM-based annotation and token-augmented modeling for emotional tone classification in online cancer peer-support posts.PLOS digital health · 2026Article
- Leveraging Promotional Strategies to Enhance Hospital Influence on Social Media: Cross-Sectional Study.Journal of medical Internet research · 2025Article
- Sentiment analysis and topic modeling of social media data to explore public discourse on irritable bowel syndrome.Scientific reports · 2025Article
- Patient Voices in Dialysis Care: Sentiment Analysis and Topic Modeling Study of Social Media Discourse.Journal of medical Internet research · 2025Article
- Dialogue pathways and narrative analysis in health communication within the social media environment: an empirical study based on user behavior-a case study of China.Frontiers in public health · 2025Article
- Article
- A sentiment analysis and topic modeling of weibo discourse on public perceptions of breast cancer.Digital healthArticle
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7 authors.
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Abstract
backgroundBreast cancer affects the lives of not only those diagnosed but also the people around them. Many of those affected share their experiences on social media. However, these narratives may differ according to who the poster is and what their relationship with the patient is; a patient posting about their experiences may post different content from someone whose friends or family has breast cancer. Weibo is 1 of the most popular social media platforms in China, and breast cancer-related posts are frequently found there.
objectiveWith the goal of understanding the different experiences of those affected by breast cancer in China, we aimed to explore how content and language used in relevant posts differ according to who the poster is and what their relationship with the patient is and whether there are differences in emotional expression and topic content if the patient is the poster themselves or a friend, family member, relative, or acquaintance.
methodsWe used Weibo as a resource to examine how posts differ according to the different poster-patient relationships. We collected a total of 10,322 relevant Weibo posts. Using a 2-step analysis method, we fine-tuned 2 Chinese Robustly Optimized Bidirectional Encoder Representations from Transformers (BERT) Pretraining Approach models on this data set with annotated poster-patient relationships. These models were lined in sequence, first a binary classifier (no_patient or patient) and then a multiclass classifier (post_user, family_members, friends_relatives, acquaintances, heard_relation), to classify poster-patient relationships. Next, we used the Linguistic Inquiry and Word Count lexicon to conduct sentiment analysis from 5 emotion categories (positive and negative emotions, anger, sadness, and anxiety), followed by topic modeling (BERTopic).
resultsOur binary model (F
conclusionsChinese users post different types of content, depending on the poster- poster-patient relationships. If the patient is family, posts are sadder and more anxious but also contain more content on treatments. However, if no patient is detected, posts show higher levels of anger. We think that these may stem from rants from posters, which may help with emotion regulation and gathering social support.
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