Annotating and detecting topics in social media forum and modelling the annotation to derive directions-a case study

B Athira,Sumam Mary Idicula,Anand Kulanthaivel,Josette Jones,Enming Zhang

doi:10.1186/s40537-021-00429-7

Abstract

The widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

Highlights

Social media is a rich resource for gathering information, even on personalized topics such as health, wellness, and decision-making
The F1-scores of the KNN, neural network, and SVM methods for each type of feature vector were less than 65% compared to the ensemble neural network (ENN), which had an F1 score of 72.1%
More in-depth research is required to explore the “support” the community members provide, “support offers and support requests” and “expert survivors’ recommendations,” to satisfy the personal needs of patients. This is a pioneering study on identifying the private concerns of patients through posts in breast cancer patient and survivor communities

Summary

Introduction

Social media is a rich resource for gathering information, even on personalized topics such as health, wellness, and decision-making. The role of the patient has transformed from that of a passive receiver of health information to a provider of knowledge and information. This transformation has spawned an emerging field of social network explorations, the patient-centric social network [1]. The users of such a social network are patients (or caregivers) with certain health conditions or symptoms seeking possible solutions.

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