Exploring the Health Information Needs of the Elderly Based on the

Online Health Communities

Yuhan Ma and Qiuli Qin*

School of Economics and Management, Beijing Jiaotong University, China

Keywords: Health Information Needs, Online Health Communities, The Elderly.

Abstract: This paper explored the health information needs of the elderly from two perspectives: information demanders

and information providers. It crawled data from questions and health education sections on the online health

communities. LDA model was applied to cluster the unlabeled questions set and obtain five themes. Then the

dataset of article titles with five categories was used to train the classifiers: CNN-BiLSTM and FastText.

After comparing the matching degree between clustering themes and classification results, this paper put for-

ward some suggestions for caregivers and information providers to assist elderly health management.

1 INTRODUCTION

The aging of the population has become an irreversi-

ble worldwide trend in the 21st century. The United

Nations predicted that the proportion of the elderly

aged over 60 would reach 22% of the global popula-

tion by 2050. The arrival of an aging society would

bring out great pressure on society, family, and med-

ical care (Sun, Shen, 2016). However, the supply of

effective services and resources for older people is se-

riously insufficient and limited. It is necessary to

carry out collaborative aging health management.

In the era of big data, the development trend of

health management is integrated, dynamic, and online

(Tian, Du, 2018). It not only focuses on the treatment

of malignant diseases but also the prevention of a per-

son’s overall health status. The elderly face health

problems such as physical decline and cognitive de-

terioration. They also feel lonely and depressed due

to social isolation or lack of companionship. Some

services for the elderly tried to solve the health man-

agement problem through technology. However, they

failed to gain popularity due to a mismatch between

the older users’ demands and the providers’ ideas

(Jo-

vanović, De Angeli, McNeill, Coventry, 2021).

Therefore, it is essential to collect and understand the

needs of the elderly when intervening in their health

management.

2 RELATED WORK

In the face of demographic changes, some health

needs assessment tools were used for the elderly. Brit-

ish scholars proposed a comprehensive geriatric as-

sessment as a diagnostic tool for physiological, psy-

chological, and social adaptation. Moreover, Euro-

pean scholars developed the Care Dependency Scale

to evaluate the unmet care needs of elderly patients

(Yang, Zhou, Ye, Wang, 2021). Health information

needs reflect the lack of individual health knowledge,

which drives health information behavior (Qian,

Zhou, Zhou, Ren, Li, 2019). Questionnaires and in-

terviews were applied to explore the information

needs and sources of the elderly (Edewor, Ijiekhuam-

hen, Patrick., Emeka, 2016). Another method is

through scoping review to classify the care and sup-

port needs of older people with chronic diseases

(Abdi, Spann, Borilovic, Witte, Hawley, 2019). How-

ever, these traditional methods have some limitations:

researchers need to assist when carrying out question-

naires and interviews due to the understanding ability

of the elderly.

As the Internet has become a channel for the dis-

semination of health information, online health com-

munities provide users with an open platform for in-

formation exchange, question-and-answer consulta-

tion, and social support on health-related issues

(Zhao, 2018). Some of them positively impact on

self-management and daily disease control for pa-

tients (Litchman, Edelman, Donaldson, 2018; Zhang,

512

Ma, Y. and Qin, Q.

Exploring the Health Information Needs of the Elderly Based on the Online Health Communities.

DOI: 10.5220/0011751000003607

In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology (ICPDI 2022), pages 512-517

ISBN: 978-989-758-620-0

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Grave, Sklar, Elhadad, 2017). Some researchers used

Latent Dirichlet Allocation (LDA) and binary classi-

fication to divide users into different social support

types (Wu, Hou, Jin, Hu, 2017). With the continuous

development of deep learning technology, long short-

term memory (LSTM) and Convolutional Neural net-

works (CNN) have been applied to recognize entities

in the question and answer texts in the online medical

community

(Liao, Zou, Xi, 2021). However, the plat-

form for elderly users has not received relevant atten-

tion.

Therefore, this paper decided to collect health in-

formation about the elderly from the questions raised

and health education sections of the online health

community. Then it adopted text mining and topic

analysis to explore the elderly health needs from the

perspectives of the information demanders and pro-

viders.

3 EXPERIMENTAL METHODS

The paper applied a python crawler to collect text data

from online health communities. One part came from

information demanders in the questions-raising sec-

tion without labels. LDA model was adopted for topic

identification and clustering. The other part came

from information providers in the health education

section with classification. CNN-BiLSTM and

FastText were used to learn the features and train the

classifiers. By comparing the matching degree of

clustering and classification results, this paper could

comprehensively analyze the health information

needs of the elderly. The whole process of text mining

is shown in Figure 1.

3.1 Data Acquisition and Preprocessing

The existing online health communities have two

kinds: one is the social platform with an open medical

module for the discussion of users; the other kind is

the health advice module in the medical website for

various diseases. Table 1 lists the top five domestic

communities in terms of popularity and their func-

tions. After comparing their functions and conven-

ience of operation interface, we selected the 39-health

net as the experimental data.

From the question-raising section of 39-healthy,

1,660 questions associated with physical changes in

older people were stored as ‘Dataset1’, representing

the health information needs of the elderly. The paper

Figure 1: The Processing Flow of Text Mining (Photo credit: Original).

Table 1: Domestic Online Health Community (Table credit: Original).

Website Introduction

1 39-health networ

Professional health information

ortal

2 Youwenbida net Excellent online health consultation platform

3 Dazhong yangsheng Spread scientific health methods in daily life

4 Xunyiwenyao net

Set up precision medical information inquiry, one-to-one

online consultation, a

ointment re

istration

5 Leha health networ

A sharing platform to spread the concept of healthy life

Exploring the Health Information Needs of the Elderly Based on the Online Health Communities

513

used the inverted index, TF-IDF model, and cosine

similarity to filter questions with the highest similar-

ity. It selected the proportion of the same keywords,

the rate of difference in sentence length, and the order

of keywords as features. From the health education

section of 39-healthy, five categories of article titles

were stored as ‘Dataset2’, 2,400 pieces respectively,

which are the elderly health care, psychology, dis-

ease, fitness, and diet.

3.2 LDA Model Topic Identification

LDA is an unsupervised Bayesian model to identify

the underlying topic information. It adopts the Bag of

Words approach, treating each document as a word

frequency vector, and transforming textual infor-

mation into digital information. For a training set that

is not manually annotated, the number k specified for

the topic is important. The obvious inflection points

and local minimum values in the perplexity curve in

Figure 2 are taken as references. Five themes were fi-

nally determined based on the principle of non-coin-

cidence of clustering circles in Figure 3.

Figure 2: The Perplexity of LDA (Photo credit: Original).

Figure 3: PyLdavis Visualization of Clustering (Photo credit: Original).

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514

3.3 Construction of Text Classifiers

The paper selected FastText, CNN, and LSTM as

classifiers. FastText can directly train word vectors

and classify text without feature engineering.

Word2Vec can be the input of word embedding based

on the neural network. The parameters setting was as

follows: vector size=100, minimum count=5, win-

dow=5, epochs=5. Since the annotation was aimed

for the whole sentence, rather than a single word, each

sentence vector was calculated and stored. Sentences

less than the max length of them were filled with ze-

ros by the function of pad sequences. The dataset was

divided into a training set and a test set in an 8:2 ratio.

The label of y was converted into a one-hot represen-

tation to prepare for the subsequent input of the neural

network. CNN uses convolutional layers and maxi-

mum pooling or max-overtime pooling layers to ex-

tract higher-level and local features. BiLSTM is suit-

able for dealing with time series and remembering the

connections between words. Combing their ad-

vantages, a hybrid model CNN-BiLSTM was con-

structed as shown in Figure 4.

Figure 4: CNN-BiLSTM Model (Photo credit: Original).

4 RESULTS AND DISCUSSION

4.1 Experimental Results

After the analysis of LDA clustering results and key-

words, Table 2 listed five themes reflecting the health

information needs of the elderly. They paid attention

to the causes and hazards of organ failure, resulting in

chronic diseases. They focused on the changing na-

ture of some diseases with age increasing and the re-

lationship with diet. They worried about symptoms

and prevention of acute onset conditions. Moreover,

they wanted to learn the treatment of osteoporosis,

and humeral head necrosis. They paid attention to the

syndrome caused by heart disease and conditioning

methods. These themes reflected the urgent need for

dietary contraindications and conditioning methods

in the process of declining physical function and re-

sistance of the elderly.

Table 2: Five Themes of Health Information Needs (Table

credit: Original).

Themes Keywords

causes and hazards of or-

an failure

cause, chronic, exhaustion,

renal function, eso

eal

nature of the disease and

diet control

allergic, thrombotic, neurotic,

urpura, diet

symptoms and preven-

tion of acute onset

symptom, acute, treatment,

revention, h

ertension

treatment of osteoporo-

sis, diabetes, and hu-

meral necrosis

osteoporosis, humerus, necro-

sis, diabetes, treatment

syndromes and condi-

tioning methods of heart

disease

conditioning way, myocardial

infarction, syndromes, atria

After constructing the classifiers, precision, recall,

and F1 were used to evaluate the classification effect

of each category as shown in Table 3. In addition to

health care, CNN-BiLSTM had a good overall effect.

The comprehensive classification accuracy of the

FastText classifier can reach 98%. Therefore, the re-

sults of these two classifiers would be taken into con-

sideration in the subsequent classification.

Table 3: The Classification Evaluation of CNN-BiLSTM

(Table credit: Original).

CNN+BiLSTM Precision Recall F1-score

health care 0.88 0.88 0.88

psychology 0.97 0.96 0.96

disease 0.93 0.89 0.91

fitness 0.93 0.95 0.94

diet 0.9 0.95 0.92

Through the classification results of 'Dataset1' in

Table 4, it can be obtained that psychology and dis-

ease are the most common questions in this dataset,

and the attention to dietary problems is at an average

level. The frequency of the questions about fitness

and health care is very few.

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Table 4: The Classification Results (Table credit: Original).

Category The amount of data

health care 46

psychology 203

disease 1193

fitness 31

diet 187

However, the results reflected the limitations of

health information demand among older users. Men-

tal health and social support are also influencing fac-

tors, but few of them appeared in the information

need. One reason is that their understanding of health

just stays on the physical health level, neglecting the

issues of mental health and social support. Another

reason is that although the website constantly pub-

lishes educational articles about mental health and di-

etary health care for the elderly, it does not gain the

widespread attention of them.

4.2 Discussion

Nowadays few older people could understand and de-

scribe personal health needs clearly. Moreover, the el-

derly rarely log in to the platforms and expose their

needs because of low trust. As the family members or

caregivers, on the one hand, it is important to make

the elderly aware of their physical condition and help

them adjust their daily habits. On the other hand, it is

necessary to assist them to overcome the digital di-

vide and cultivate information literacy, learning to

achieve self-management and daily maintenance

through the Internet channel.

Combined with the present health demand infor-

mation, information providers could optimize their

health education service, by setting up detailed clas-

sification according to the old users’ needs, making it

convenient for them to find the content of interest.

Medical information service personnel should pub-

lish high-quality, reliable guaranteed health

knowledge, corresponding to potential health infor-

mation needs such as the spirit level and social sup-

port. They should improve the relatively backward

health concept of older users, encouraging them to

seek spiritual comfort and psychological counseling

assistance.

As the traditional pension model in China is

mostly community-based, the elderly put their trust in

the local community. Therefore, information provid-

ers could connect offline communities, contacting the

majority of the elderly for better health education.

Considering the old man's understanding ability, it is

a good choice to visually show the relationship

between disease, diet, exercise, etc. by the knowledge

map.

5 CONCLUSIONS

This paper analyzed the information needs of the el-

derly in the online community, avoiding the under-

standing bias caused by questionnaires and inter-

views. Not only does it accurately target the needs of

older adults, but it also guides caregivers and health

information providers. Meanwhile, this paper sup-

ports elderly users develop self-diagnosis and care

awareness in the interaction of the internet. As the

basic work of health management for the elderly, this

research has the following contributions: one is

through the text clustering and analysis of online in-

formation to dig out the hidden health needs, avoiding

deviation of questionnaire and interview. As in addi-

tion to the traditional research methods, it inspires the

online information provider to consider the compati-

bility of the elderly demand. Previous studies paid lit-

tle attention to the matching of information demand-

ers and information providers.

Moreover, it can be further expanded from data

diversification and coordinated care. In an aging so-

ciety, it is important to improve older people’s ability

to information search and utilization. Optimizing the

suitability and attractiveness of health websites for

the elderly is a direction of effort.

REFERENCES

Abdi, S., Spann, A., Borilovic, J., Witte, D. L., and Hawley,

M. “Understanding the care and support needs of older

people: a scoping review and categorization using the

WHO international classification of functioning, disa-

bility, and health framework (ICF),” BMC Geriatrics,

19(1), BioMed Central, 2019, pp. 1-15.

Edewor, N., Ijiekhuamhen, O. Patrick., and Emeka,U. P.

“Elderly people and their information needs,” Library

Philosophy and Practice (e-journal), 2016.Bonini, C. P.

1963. Simulation of Information and Decision Systems

in the Firm, Englewood Cliffs, NJ: Prentice-Hall.

Jovanović, M., De Angeli, A., McNeill, A., and Coventry,

L. “User requirements for inclusive technology for

older adults,” International Journal of Human-Com-

puter Interaction, 37(20), 2021, pp. 1947-1965.

Liao, K.J., Zou, K.X. and Xi, Y.J. “A text entity recognition

method for online medical community question an-

swering: Based on convolutional neural network and bi-

directional long short-term memory neural network,”

Research on Science and Technology Management,

41(8), 2021, pp. 173-179.

ICPDI 2022 - International Conference on Public Management, Digital Economy and Internet Technology

516

Litchman, M. L., Edelman, L. S., and Donaldson, G. W.

“Effect of diabetes online community engagement on

health indicators: a cross-sectional study,” JMIR diabe-

tes, 3(2), JMIR Publications Inc., Toronto, Canada,

2018, pp. 1-15.

Qian, Y.X., Zhou, H.Y., Zhou, L.Q., Ren, M.L., Li, H. “Re-

search on health Information Demand Mining of elderly

online community users,” Modern Information, 39(6),

2019, pp. 59-69.

Sun, X. Y, and Shen, Y. “Application of health manage-

ment in healthy aging,” Medical information: Medicine

and computer applications, 29(28), 2016.

Tian, M. X, and Du, W. “Research on the current situation,

trend and strategy of health management informatiza-

tion in the era of big data,” Jiangsu Science and Tech-

nology Information, 35(7), 2018, pp. 20-23.

Wu, J., Hou, S.X., Jin, M.M., and Hu, Z. Y. “Research on

text classification and user clustering of online medical

community based on LDA model feature selection,”

Journal of the China Society for Scientific and Tech-

nical Information, 36(11), 2017, pp. 1183-1193.

Yang, L.X., Zhou, S.L., Ye, X.L., and Wang, R. “Research

status and enlightenment of health need assessment

tools for the elderly at home and abroad,” Journal of

Nursing (China), 28(21), 2021, pp. 36-40.

Zhao, D.X. “Review of online health community research

in China,” Library and Information Service, 62(9),

2018, pp. 134-142.

Zhang, S.D., Grave, E., Sklar, E., and Elhadad, N. “Longi-

tudinal Analysis of Discussion Topics in an Online

Breast Cancer Community using Convolutional Neural

Networks,” Journal of Biomedical Informatics, 69,

2017, pp. 1-9.

Exploring the Health Information Needs of the Elderly Based on the Online Health Communities

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