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

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.

Keywords: Breast cancer; MedHelp; Online forum; Social media; Symptom cluster; Text mining.

Fig. 1

Clustering results for social media group. The size of the circle reflects the frequency of the symptom. The thickness of the lines connecting individual symptoms reflects the degree of similarity between the two. The medoid of each cluster is highlighted using a thicker edge. Red linkages represent the highest 10 % similarity between nodes

Fig. 2

Clustering results for research study group T1 (moderate and severe symptoms). The size of the circle reflects the frequency of the symptom. The thickness of the lines connecting individual symptoms reflects the degree of similarity between the two. The medoid of each cluster is highlighted using a thicker edge. Red linkages represent the highest 10 % similarity between nodes

Fig. 3

Clustering results for research study group T2 (severe symptoms only). The size of the circle reflects the frequency of the symptom. The thickness of the lines connecting individual symptoms reflects the degree of similarity between the two. The medoid of each cluster is highlighted using a thicker edge. Red linkages represent the highest 10 % similarity between nodes