1- Broadcast and Electronic Communication Arts, San Francisco State University, United States
2- Shahid Beheshti University of Medical Sciences, Tehran, Iran
3- University of Tehran,Tehran, Iran ,shahghasemi@ut.ac.ir
2- Shahid Beheshti University of Medical Sciences, Tehran, Iran
3- University of Tehran,Tehran, Iran ,
Abstract: (378 Views)
Background: Social media platforms provide important spaces in which cancer patients narrate their illness experiences and reveal needs that often remain unexpressed in clinical settings. However, Persian-language cancer discourse has received limited scholarly attention. This study sought to identify the emotional orientations and dominant themes in Iranian cancer patients' posts on X and to determine their implications for nursing practice.
Methods: This cross-sectional infodemiology study examined posts published between September 2017 and June 2025. Among 50 screened accounts belonging to self-identified cancer patients, 24 satisfied the inclusion criteria. A Persian-language keyword lexicon yielded 2,489 posts, of which 1,617 remained following manual relevance screening. Sentiment was classified into five categories with a Persian BERT-based model (ParsBERT–DeepSentiPers), and themes were identified by Latent Dirichlet Allocation (LDA) topic modeling in Python 3.10 (Gensim, Transformers); a 12-topic model achieved the highest coherence (0.4729). Sentiment distributions were subsequently examined within each theme.
Results: Happy was the most frequently identified sentiment (31.2%), followed by Neutral (25.5%), Furious (17.3%), Angry (16.7%), and Delighted (9.4%); the overall proportion of positive sentiment (40.6%) exceeded that of negative sentiment (34.0%). Among the twelve identified themes, Treatment Process was the most prevalent (66.8%), followed by Personal Journey & Empowerment (23.7%) and Side Effects & Physical Suffering (23.2%), the only major theme in which negative posts outnumbered positive posts. Negative sentiment was concentrated in accounts of stigma, financial hardship, and experiences with the healthcare system. Metaphorical misuse of cancer language and fabricated illness claims were identified.
Conclusion: Iranian cancer patients' online narratives identify priorities for nursing practice, including symptom-management education, psychosocial screening, financial navigation, stigma reduction, and caregiver inclusion, while positioning nurses to guide patients toward credible online communities amid health misinformation.
Methods: This cross-sectional infodemiology study examined posts published between September 2017 and June 2025. Among 50 screened accounts belonging to self-identified cancer patients, 24 satisfied the inclusion criteria. A Persian-language keyword lexicon yielded 2,489 posts, of which 1,617 remained following manual relevance screening. Sentiment was classified into five categories with a Persian BERT-based model (ParsBERT–DeepSentiPers), and themes were identified by Latent Dirichlet Allocation (LDA) topic modeling in Python 3.10 (Gensim, Transformers); a 12-topic model achieved the highest coherence (0.4729). Sentiment distributions were subsequently examined within each theme.
Results: Happy was the most frequently identified sentiment (31.2%), followed by Neutral (25.5%), Furious (17.3%), Angry (16.7%), and Delighted (9.4%); the overall proportion of positive sentiment (40.6%) exceeded that of negative sentiment (34.0%). Among the twelve identified themes, Treatment Process was the most prevalent (66.8%), followed by Personal Journey & Empowerment (23.7%) and Side Effects & Physical Suffering (23.2%), the only major theme in which negative posts outnumbered positive posts. Negative sentiment was concentrated in accounts of stigma, financial hardship, and experiences with the healthcare system. Metaphorical misuse of cancer language and fabricated illness claims were identified.
Conclusion: Iranian cancer patients' online narratives identify priorities for nursing practice, including symptom-management education, psychosocial screening, financial navigation, stigma reduction, and caregiver inclusion, while positioning nurses to guide patients toward credible online communities amid health misinformation.
Keywords: Digital Health Communication, Cancer (Neoplasms) Narratives (Narration), Sentiment Analysis, Nursing, Persian X
Type of study: Original Article |
Subject:
Nursing
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