Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study
Abstract BackgroundEarly-stage breast cancer has the complex challenge of carrying a favorable prognosis with multiple treatment options, including breast-conserving surgery (BCS) or mastectomy. Social media is increasingly used as a source of information and as a decision too...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2025-01-01
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| Series: | JMIR Cancer |
| Online Access: | https://cancer.jmir.org/2025/1/e52886 |
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| author | Daphna Y Spiegel Isabel D Friesner William Zhang Travis Zack Gianna Yan Julia Willcox Nicolas Prionas Lisa Singer Catherine Park Julian C Hong |
| author_facet | Daphna Y Spiegel Isabel D Friesner William Zhang Travis Zack Gianna Yan Julia Willcox Nicolas Prionas Lisa Singer Catherine Park Julian C Hong |
| author_sort | Daphna Y Spiegel |
| collection | DOAJ |
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Abstract
BackgroundEarly-stage breast cancer has the complex challenge of carrying a favorable prognosis with multiple treatment options, including breast-conserving surgery (BCS) or mastectomy. Social media is increasingly used as a source of information and as a decision tool for patients, and awareness of these conversations is important for patient counseling.
ObjectiveThe goal of this study was to compare sentiments and associated emotions in social media discussions surrounding BCS and mastectomy using natural language processing (NLP).
MethodsReddit posts and comments from the Reddit subreddit r/breastcancer and associated metadata were collected using pushshift.io. Overall, 105,231 paragraphs across 59,416 posts and comments from 2011 to 2021 were collected and analyzed. Paragraphs were processed through the Apache Clinical Text Analysis Knowledge Extraction System and identified as discussing BCS or mastectomy based on physician-defined Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concepts. Paragraphs were analyzed with a VADER (Valence Aware Dictionary for Sentiment Reasoning) compound sentiment score (ranging from −1 to 1, corresponding to negativity or positivity) and GoEmotions scores (0‐1) corresponding to the intensity of 27 different emotions and neutrality.
ResultsOf the 105,231 paragraphs, there were 7306 (6.94% of those analyzed) paragraphs mentioning BCS and mastectomy (2729 and 5476, respectively). Discussion of both increased over time, with BCS outpacing mastectomy. The median sentiment score for all discussions analyzed in aggregate became more positive over time. In specific analyses by topic, positive sentiments for discussions with mastectomy mentions increased over time; however, discussions with BCS-specific mentions did not show a similar trend and remained overall neutral. Compared to BCS, conversations about mastectomy tended to have more positive sentiments. The most commonly identified emotions included neutrality, gratitude, caring, approval, and optimism. Anger, annoyance, disappointment, disgust, and joy increased for BCS over time.
ConclusionsPatients are increasingly participating in breast cancer therapy discussions with a web-based community. While discussions surrounding mastectomy became increasingly positive, BCS discussions did not show the same trend. This mirrors national clinical trends in the United States, with the increasing use of mastectomy over BCS in early-stage breast cancer. Recognizing sentiments and emotions surrounding the decision-making process can facilitate patient-centric and emotionally sensitive treatment recommendations. |
| format | Article |
| id | doaj-art-55b9badc8b694cddaba020705fa61351 |
| institution | Kabale University |
| issn | 2369-1999 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Cancer |
| spelling | doaj-art-55b9badc8b694cddaba020705fa613512025-02-04T20:16:16ZengJMIR PublicationsJMIR Cancer2369-19992025-01-0111e52886e5288610.2196/52886Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing StudyDaphna Y Spiegelhttp://orcid.org/0000-0002-9348-9380Isabel D Friesnerhttp://orcid.org/0009-0000-0216-4031William Zhanghttp://orcid.org/0009-0001-1640-9949Travis Zackhttp://orcid.org/0000-0002-1620-6455Gianna Yanhttp://orcid.org/0009-0003-4465-7522Julia Willcoxhttp://orcid.org/0009-0006-6359-2674Nicolas Prionashttp://orcid.org/0000-0001-5067-7077Lisa Singerhttp://orcid.org/0000-0001-9244-3199Catherine Parkhttp://orcid.org/0000-0002-1533-0972Julian C Honghttp://orcid.org/0000-0001-5172-6889 Abstract BackgroundEarly-stage breast cancer has the complex challenge of carrying a favorable prognosis with multiple treatment options, including breast-conserving surgery (BCS) or mastectomy. Social media is increasingly used as a source of information and as a decision tool for patients, and awareness of these conversations is important for patient counseling. ObjectiveThe goal of this study was to compare sentiments and associated emotions in social media discussions surrounding BCS and mastectomy using natural language processing (NLP). MethodsReddit posts and comments from the Reddit subreddit r/breastcancer and associated metadata were collected using pushshift.io. Overall, 105,231 paragraphs across 59,416 posts and comments from 2011 to 2021 were collected and analyzed. Paragraphs were processed through the Apache Clinical Text Analysis Knowledge Extraction System and identified as discussing BCS or mastectomy based on physician-defined Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concepts. Paragraphs were analyzed with a VADER (Valence Aware Dictionary for Sentiment Reasoning) compound sentiment score (ranging from −1 to 1, corresponding to negativity or positivity) and GoEmotions scores (0‐1) corresponding to the intensity of 27 different emotions and neutrality. ResultsOf the 105,231 paragraphs, there were 7306 (6.94% of those analyzed) paragraphs mentioning BCS and mastectomy (2729 and 5476, respectively). Discussion of both increased over time, with BCS outpacing mastectomy. The median sentiment score for all discussions analyzed in aggregate became more positive over time. In specific analyses by topic, positive sentiments for discussions with mastectomy mentions increased over time; however, discussions with BCS-specific mentions did not show a similar trend and remained overall neutral. Compared to BCS, conversations about mastectomy tended to have more positive sentiments. The most commonly identified emotions included neutrality, gratitude, caring, approval, and optimism. Anger, annoyance, disappointment, disgust, and joy increased for BCS over time. ConclusionsPatients are increasingly participating in breast cancer therapy discussions with a web-based community. While discussions surrounding mastectomy became increasingly positive, BCS discussions did not show the same trend. This mirrors national clinical trends in the United States, with the increasing use of mastectomy over BCS in early-stage breast cancer. Recognizing sentiments and emotions surrounding the decision-making process can facilitate patient-centric and emotionally sensitive treatment recommendations.https://cancer.jmir.org/2025/1/e52886 |
| spellingShingle | Daphna Y Spiegel Isabel D Friesner William Zhang Travis Zack Gianna Yan Julia Willcox Nicolas Prionas Lisa Singer Catherine Park Julian C Hong Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study JMIR Cancer |
| title | Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study |
| title_full | Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study |
| title_fullStr | Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study |
| title_full_unstemmed | Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study |
| title_short | Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study |
| title_sort | exploring the social media discussion of breast cancer treatment choices quantitative natural language processing study |
| url | https://cancer.jmir.org/2025/1/e52886 |
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