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Symptom clusters in women with breast cancer: an analysis of data from social media and a research study

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Abstract

Purpose

User-generated content on social media sites, such as health-related online forums, offers researchers a tantalizing amount of information, but concerns regarding scientific application of such data remain. This paper compares and contrasts symptom cluster patterns derived from messages on a breast cancer forum with those from a symptom checklist completed by breast cancer survivors participating in a research study.

Methods

Over 50,000 messages generated by 12,991 users of the breast cancer forum on MedHelp.org were transformed into a standard form and examined for the co-occurrence of 25 symptoms. The k-medoid clustering method was used to determine appropriate placement of symptoms within clusters. Findings were compared with a similar analysis of a symptom checklist administered to 653 breast cancer survivors participating in a research study.

Results

The following clusters were identified using forum data: menopausal/psychological, pain/fatigue, gastrointestinal, and miscellaneous. Study data generated the clusters: menopausal, pain, fatigue/sleep/gastrointestinal, psychological, and increased weight/appetite. Although the clusters are somewhat different, many symptoms that clustered together in the social media analysis remained together in the analysis of the study participants. Density of connections between symptoms, as reflected by rates of co-occurrence and similarity, was higher in the study data.

Conclusions

The copious amount of data generated by social media outlets can augment findings from traditional data sources. When different sources of information are combined, areas of overlap and discrepancy can be detected, perhaps giving researchers a more accurate picture of reality. However, data derived from social media must be used carefully and with understanding of its limitations.

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Abbreviations

CHV:

Consumer health vocabulary

GI:

Gastrointestinal

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Funding

This study was funded by the following Grants—National Science Foundation SES-1424875, National Institutes of Health R21AG042761, and Department of Defense #DAMD 17-01-1-0446.

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Authors and Affiliations

  1. Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA

    Sarah A. Marshall & Edward H. Ip

  2. College of Computing and Informatics, Drexel University, Philadelphia, PA, 19104, USA

    Christopher C. Yang, Qing Ping & Mengnan Zhao

  3. Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA

    Nancy E. Avis & Edward H. Ip

Authors
  1. Sarah A. Marshall
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  2. Christopher C. Yang
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  3. Qing Ping
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  4. Mengnan Zhao
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  5. Nancy E. Avis
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Correspondence to Edward H. Ip.

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Conflict of interest

All authors declare they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the research study of breast cancer survivors. Permission to extract data from the forum MedHelp.com was granted by site managers, and all data from this source were collected anonymously.

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Marshall, S.A., Yang, C.C., Ping, Q. et al. Symptom clusters in women with breast cancer: an analysis of data from social media and a research study. Qual Life Res 25, 547–557 (2016). https://doi.org/10.1007/s11136-015-1156-7

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