Integration of Social Media Platforms and Specialized Web

Resources for the Effective Use of High-tech Medical Information

Alyona Kozyreva

, Uliana Nazarenko

, Grigory Shovkoplias

, Artem Beresnev

Elizaveta Klevtsova

and Natalia Gusarova

ITMO University, 49 Kronverksky av., St. Petersburg, Russia

Keywords: Chatbot, Healthcare, High-tech Medical Information, Internet Community Structure, Publicly Accessible

Resource, Second Opinion, Spontaneous Crowdsourcing, Telegram Channels, VK Community.

Abstract: The widespread dissemination of information technologies and technological breakthroughs in recent years

have led to the fact that the flow of medical information falls on the patient. In order to interpret their high-

tech medical information, given the lack of doctors and mistrust in them, which is especially typical for

developing countries, people make attempts to use available Internet sources and collective network

intelligence, i.e. appeal to collective opinion. Thus, there is the problem of integration of social media

platforms and specialized web resources for the effective use of high-tech medical information. We consider

this problem in relation to the Russian-speaking segment of the Internet. We have experimentally studied the

structure of public medical Internet communities typical for Russia. We found that they are characterized by

self-organization. We have developed and launched a web resource for the effective use of high-tech medical

information, and to form the motivational component of the resource, we use the identified structure of already

existing network communities of medical focus. We use specialized chat bots as an effective means of

integrating the developed resource and network communities.

1 INTRODUCTION

The prevention and treatment of top 10 diseases

causing the most deaths worldwide, including

coronary artery disease, stroke, chronic obstructive

pulmonary disease, bronchus and lung cancers etc.

(Pietrangelo, 2019) increasingly relies on high

medical technologies. Incorrect or inadequate

(without taking into account the health status in

general) interpretation of such information by the

patient himself can cause him psychological and

social problems, not to mention the treatment of the

disease itself (Giardina, 2018). However, the demand

for such information from patients all over the world

is only increasing.

There are several reasons provoking this situation.

The first is the objectively existing shortage of

https://orcid.org/0000-0001-8117-0869

https://orcid.org/0000-0002-0083-8917

https://orcid.org/0000-0002-5483-716Х

https://orcid.org/0000-0002-4646-6856

https://orcid.org/0000-0001-8394-5605

https://orcid.org/0000-0002-1361-6037

specialists (Allyn, 2020), discrepancies between two

expert interpreters (Sawan, 2017), and distrust of

doctors, caused by the dissatisfaction of patients with

existing medical care. For example, only 15% of

Brazilians and 13% of Russians assess their medicine

positively (Romir, 2020).

In this situation, the importance of the so called

second opinion for patients increases dramatically

(Benbassat, 2019), especially, a second opinion from

a radiologist (re-analysis of diagnostic images,

namely CT, MRI, radiography, mammography, PET-

CT, etc.) or cardiologist (ECG, MRI of the heart, etc.).

In principle, such analysis can be carried out by

an expert doctor remotely, using modern

telecommunications, in combination with automated

interpretation of high-tech medical information by

means of artificial intelligence (Ahuja, 2019).

154

Kozyreva, A., Nazarenko, U., Shovkoplias, G., Beresnev, A., Klevtsova, E. and Gusarova, N.

Integration of Social Media Platforms and Specialized Web Resources for the Effective Use of High-tech Medical Information.

DOI: 10.5220/0010404501540162

In Proceedings of the 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2021), pages 154-162

ISBN: 978-989-758-506-7

Besides, the high-tech medical information contained

in patient requests could serve as a good basis for the

formation of appropriate datasets, but for this it is

necessary to develop appropriate organizational and

motivational procedures.

Thus, there is the problem of integration of social

media platforms and specialized web resources for

the effective use of high-tech medical information,

which is especially relevant for medicine in

developing countries. In the article, this task is

considered in relation to the Russian-speaking

segment of the Internet.

Namely, we have experimentally studied the

structure of public medical Internet communities

typical for Russia. We found that they are

characterized by self-organization. We have

developed and launched a web resource for the

effective use of high-tech medical information, and to

form the motivational component of the resource, we

use the identified structure of already existing

network communities of medical focus. We use

specialized chat bots as an effective means of

integrating the developed resource and network

communities.

2 BACKGROUND AND RELATED

WORKS

Internet services, on which experienced doctors

provide the patient with their reports on his high-tech

medical information, are widely represented in

medically developed countries, for example in USA

– Second Opinions (from 29$), 2nd.MD ($3,000); in

India – SeekMed (video communication with a

specialist is provided). These services are websites or

applications with a strictly defined business model,

which implies the legitimacy and security of the

transmitted information. Most of these services are

expensive and therefore not available to everyone.

Similar services also exist in Russia, for example:

Cardio-online; National Teleradiological Network.

But, all of them are not free and have a very narrow

specialization.

In addition to specialized Internet-services, there

is also the possibility of interpreting high-tech

medical information through social networks, like

Facebook. In Russia and in most developing countries

such as Iran, Malaysia etc., the Telegram messenger

is much more popular. A large share of the Russian-

speaking segment of the Internet is also occupied by

the social network VKontakte. In these social

networks, communities related to medicine,

cardiology, cardiac surgery, radiology, and neurology

are popular. But, although these communities are

usually administered by professional doctors, it is

impossible to guarantee the adequacy of the

interpretation of medical information here.

The issues of obtaining and interpreting medical

information in social media are studied in the

literature mainly in the aspect of crowdsourcing

(Wang, 2020; Kalantarian, 2019; Tucker, 2019).

(McCoy, 2014) defines crowdsourcing to outsource a

task to a group or community of people. (Tucker,

2019) concerns crowdsourcing activity as online

collaboration systems.

Many studies suggest crowdsourcing to perform

only separate, well-structured tasks - for example, for

pre-clinical research (Tucker, 2019), for formatting

incoming information, for improving the quality of the

extracted facts (Kalantarian, 2019). To process

information at a higher level by means of

crowdsourcing, it is proposed to involve specialists.

For example, in (Yoshida, 2016) hundreds of scientists

were recruited first to generate, and then to assess

competing health research ideas using a pre-defined set

of priority-setting criteria. At the same time, there are

examples of using crowdsourcing in artificial

intelligence projects, most often related to annotation

of medical data (Wang, 2020).

Insufficient attention is paid to the composition

and structure of the interaction of crowdsourcing

participants. As noted in the review (Créquit, 2018),

сrowd workers’ characteristics and crowdsourcing

logistics are poorly reported in the reviewed articles.

Crowd workers’ characteristics are frequently

missing: even age and gender are not reported for

about 60% of the studies.

Among the motivating factors for contribution or

collaboration in medical crowdsourcing, various

researchers distinguish recognition, curiosity, intrinsic

satisfaction, or, in some situations, financial incentives

(McCartney, 2013; Go, 2015; Chiauzzi, 2015]. The

(WHO, 2018) recommendations for underdeveloped

countries suggest such an unexpected mechanism for

motivating crowdsourcing, as the organization of

challenge contests for health. However, in general this

aspect of crowdsourcing remains outside the attention of

researchers: according to the review (Créquit, 2018), of

202 studies motivations of crowd workers were

recorded for 5 only.

The analysis performed allows us to draw the

following conclusions.

The use of communities in social networks is

convenient and accessible to all segments of the

population, but at the same time it does not guarantee

the legitimacy and reliability of the interpretation

Integration of Social Media Platforms and Specialized Web Resources for the Effective Use of High-tech Medical Information

155

obtained. In such communities, there is "spontaneous

crowdsourcing" supported by self-organization, but

its use as a motivation mechanism has not been

studied. On the other hand, existing specialized

medical services have a high level of legitimacy and

reliability, but most of them are expensive for users

and do not provide for the use of incoming

information as a source for the formation of datasets

for use in artificial intelligence.

Therefore, the authors of this article set

themselves the following tasks:

1. Explore the structure of organization and self-

organization of participants in communities on social

media platforms aimed at effective use of high-tech

medical information.

2. Conduct a problem-oriented analysis of the

needs and motivations of the participants in these

communities.

3. Based on the analysis, develop a solution that

provides integration of social media platforms and

specialized web resources for the effective use of

high-tech medical information.

3 METHODS AND MATERIALS

The paper analyzes the largest and most popular

medical communities of the VKontakte (VK) social

network on cardiology and the channels of the

Telegram messenger on cardiology and radiology -

More than Holter Monitoring (https://vk.com/holter),

Medic: ECG (https://vk.com/medic_ecg), ECG

electrocardiography (https://t.me/medecg), THE

SYNAPSUS Cardiology (https://t.me/cardiologlove),

Glowing Radiologist (https://t.me/radiologyMMA)

and Radiology Chat (https://t.me/radiologyMMAchat).

The detailed information about the community is

given in the Appendix.

The main audience of the communities was

determined after additional analysis of each

community and detailed review of the participants. A

detailed analysis of the content published in the

communities showed that all communities have an

administrator, who is a doctor, and community

members can be roughly divided into three groups -

patients, students, and professional doctors.

In our community research, we used only publicly

available information. We used the Popsters

analytics service and R programming language. We

used the free developed environment RStudio, as well

as the vkR package, which provides access to the VK

API. To visualize the “friendship network” and build

an interactive graph, we used the tkrplot library.

For each VK community, we calculated 22

variables characterizing community users (Variables

№1-9: Active Users, Population, Clear population,

Members, Share Active Members, Connected Users,

Connected Users Share, Isolates, Isolates Share), the

“friendship networks” (Variables №10-19: Edges,

Connected Components, Vertex Giant Component,

Density, Modularity (from 0 to 1), Clusters, Mean

Geodesics, Diameter, Mean Degree) and the

structural indicators of community users (Variables

№19-22: Female Share, Writer Share, Liker Share,

Passive Share). The detailed description of the

variables is presented in Appendix.

We used the k-means (MacQueen, 1967) and the

LDA (Blei, 2003) algorithms to identify the

community keywords.

To analyze messages in Telegram channels, we

wrote a data grabber in the Python language using the

telethon library. We used the analytics service and the

grabber to see the total number of posts of a certain

text length and certain content.

To view the dynamics of the visibility of the site

Cardio-online, we used the resource Be1.ru.

4 RESULTS

Statistics on the number of subscribers to

Communities №1 and 2 hosted on the VK platform

are shown in Figure 1. It shows that the number of

subscribers is growing in both communities for the

last month.

Figure 1: Statistics on the number of subscribers of

Community №1 and №2.

Figure 2: Community №1 and №2 statistics on the total

number of views for all publications.

ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health

156

Figure 2 shows Community №1 and №2 statistics

for the total number of views for all posts. We can see

a slight increase in each community in the total

number of views for all publications. The growth has

been most noticeable since the beginning of 2020,

especially for Сommunity №2.

Similar data for the communities

№3 and №4

hosted on the Telegram platform are shown in Figure

3 showing active growth in subscribers. Figures 4, 5

display the total number of views in Telegram

channels for all publications on different dates in the

analyzed period. The Figures illustrate a significant

increase of views in the period from 2019 to 2020 for

all Telegram channels. The most pronounced growth

has been observed since the beginning of 2020.

Figure 3: Statistics on the number of subscribers of

Communities №3 and №4.

Figure 4: Statistics on the total number of views for all

publications of Communities №3 and №4.

Figure 5: Statistics on the total number of views for all

publications of Communities №5 and №6.

Figure 6: Dynamics of website «Almazov National Medical

Research Centre of the Ministry of Health of the Russian

Federation» visibility for 12 months.

For comparison, Figure 6 illustrates dynamics of

website «Almazov National Medical Research Centre

of the Ministry of Health of the Russian Federation»

visibility. The orange line shows the number of

requests and the blue line shows the number of

effective impressions. We can see that the number of

requests (the frequency of entering keywords from

site visibility in Yandex search per month, according

to Wordstat) for the last month is 179. The number of

site snippets shown to users in Yandex search results,

according to the site visibility data is 1,816. The

number of requests to the site decreases, the activity

on the site is low. A summary of the obtained user

indicators in Communities №1 and №2 under

consideration is presented in Table 1.

Table 1: The indicators of community users.

№ Variable

Com.

№1

Com.

№2

Units

Active

Users

1,213 1,199 Users

2 Population 19,030 36,869 Users

Clear

Population

18,392 35,936 Users

4 Members 18,228 35,936 Users

Active

Members

7% 3%

% of

group

members

Connected

Users

11,225 14,260 Users

Connected

Users Share

63% 40%

% of

group

members

8 Isolates 7,167 21,676 Users

Isolates

39% 60%

% of

group

members

Based on the processed data, it can be concluded

that users showing any activity on the pages of

Community №1 and Community №2 constitute a

small part of all users of the group, only 7% and 3%.

At the same time, users who are on each other's

friends list make up more than half of all members of

Community №1 (63%) and less than half of all

members of Community №2 (40%). There are also

quite a few isolates in the communities.

A summary of the obtained structural indicators

of the “friendship network” is presented in Table 2.

Integration of Social Media Platforms and Specialized Web Resources for the Effective Use of High-tech Medical Information

157

Table 2: Structural indicators of the “friendship network”.

№ Variable Com. №1 Com. №2

10 Edges 33,993

33,682

Connected

Components

228

187

Vertex Giant

Component

95%

96%

13 Density 0.0002

0.0005

14 Modularity 0.0216

0.0295

15 Clusters 264

234

Mean

Geodesics

6.6

7.1

17 Diameter 18

18 Mean Degree 6.18 4.72

The general structure of the of the network of

active users of Communities №1 and №2 is shown in

Figure 7. The vertices represent users, next to the

black circle, the user ID is indicated with blue

numbers. An arc link is established between those

group members who have each other in the friend list.

Figure 7: General structure of the network of active users of

Communities №1 and №2.

Figure 8: Fragments of the general structure of the network

of active users of Communities №1 and №2, where isolates

are visible.

Figure 9: General structure of the network of active users,

where isolates are visible.

Figure 10: “Friendship Network” Components.

From the constructed graphs, we can see in more

detail the structure of communities. From Figures 8-

10, it is obvious that not all participants in

communities have close ties - many participants are

not connected with each other practically at all. There

are many subgraphs in networks – clusters (Figure 6).

A summary of the community users’ structural

indicators is presented in Table 3 (Community names

have been translated from Russian). For a better

understanding of the specificity of these

communities’ profile and users’ interests, keywords

were highlighted (Table 4). Keywords have been

translated from the native language.

Table 3: Composition of community users.

№ Variable

Com.

№1

Com.

№2

Female

73.1% 70.2%

Writer

0.4% 0.1%

21 Liker Share 6.2% 3.2%

Passive

95.4% 96.9%

Table 4: Keywords of Communities №1 and №2.

words of Com. №1 Ke

words of Com. №2

monitoring

Holter monitoring

Halter cost

analysis

Holter examination

Halter at home

ECG

patient

risk

disease

help

year

heart

female

tests

medic ECG

answer

follow the news

participate in public life

get to know ECG

vascular

cardiologist

artery

stenosis

coronary

literature

aorta

From Table 4, we can see that Community №1

aims to provide high-tech health information

assessment and advice on Holter monitoring, and

ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health

158

Community №2 aims to educate and deepen

knowledge in cardiology.

Figures 11, 12 show the total number of posts for

Communities №3-6.

Left to Right Values: small (less

than 160 symbols), average (from 160 to 1000) and

large (more than 1000) length.

Figures 13, 14 display the

total number

publications with a certain content. Left to Right

Values: text, photos, videos, links.

Figure 11: Statistics of the total number of posts in

Communities №3 and №4 of publications with a certain text

length.

Figure 12: Statistics of the total number of posts in

Communities №5 and №6 of publications with a certain text

length.

Figure 13: Statistics of the of publications with a certain

content in Communities №3 and №4 of publications with a

certain text length.

Figure 14: Statistics of the of publications with a certain

content in Communities №5 and №6 of publications with a

certain text length.

5 DISCUSSION

Since the subject of the medical Telegram channels

and VK communities under consideration is rather

narrow and specific, the number of subscribers is not

large. However, from the Figures 1, 3 we can see that

the number of subscribers is growing significantly,

especially from the second half of 2020. This is

mostly pronounced for the Telegram channels.

Figures 2, 4, 5 illustrate the increase of the total

number of views for all publications. This indicates

an increase in the level of people's interest in these

medical topics.

The obtained indicators of users of Community №

1 (Table 1) indicate a small percentage of active users

(7%) and many isolates (7,167). From the structural

indicators of the “friendship network” (Table 2) and

the general network structure, we can see that not all

users are linked with each other. It shows the diversity

of the audience, which suggests an individual

approach to every person. From the obtained

structural indicators of users of Community № 1

(Table 3), it can be seen that a very small part of the

group creates content and a huge part of the group

does not show any activity. This allows us to say that

most of the community members are not aimed at

active communication among most participants.

Having additionally analyzed the participants and

their actions in the communities, we made sure that

users enter these communities mainly to receive an

assessment of high-tech medical information (Table

4), which is provided by a limited circle of people

(doctors). At the same time, medical students and

doctors join these communities to gain experience

and deepen their knowledge (Figure 10).

Table 4 shows the thematic profile of

communities, their interests and needs. In

combination with the analysis of the group structure

(Figures 7, 9), this once again confirms that people

have little horizontal connection with each other.

Figure 10 shows the most common network

clusters are formed by a group of students from one

medical university and a group of medical workers.

However, there are several leaders with great activity

– they are often contacted, this can be seen from the

variables and pictures.

Figures 11, 12 show that most publications in

Communities № 3 and №4 have medium and large

length. This is due to the patients' description of their

symptoms and the first medical opinion, as well as

detailed answers from doctors and medical students.

A more detailed analysis of the posts shows that

people write their medical data directly. This means

that the need for additional interpretation of their

Integration of Social Media Platforms and Specialized Web Resources for the Effective Use of High-tech Medical Information

159

medical data is so great that it often outweighs the

requirements for privacy and legitimating. This

circumstance should be taken into account when

building a web resource.

Figures 13, 14 show that users send their data in

various graphical formats to get an assessment of

high-tech medical information. At the same time, the

radiology Community №5 is characterized by short

messages. This is because videos and links to useful

materials are more often published on the main

channel. In Community № 6 there are more long

messages.

Summing up the results of the analysis, we can

state that in order to obtain an effective assessment

of high-tech medical information, residents of Russia

address primarily in public Internet spaces. There

emerge and become popular communities working in

the mode of "spontaneous crowdsourcing", and they

have a pronounced self-organization. Users are

divided into groups - patients, students and doctors -

with their interests that do not contradict each other

and are fully satisfied there. This allows such

communities to provide the necessary informational

services to their participiants for free.

However, the medical information discussed in

such communities is transmitted and disseminated in

an open manner, without regard to privacy and

legitimating requirements. In addition, despite the

obvious value of this information, it is practically not

used as a source for the formation of datasets for

subsequent use in electronic medicine through

artificial intelligence.

It points to the relevance of creating a publicly

available solution for evaluating high-tech

information, combining the accessibility and ease of

communication inherent in social networks,

providing a legitimate second opinion for patients

and at the same time allowing the use of the

information provided to form datasets. The authors

see such a solution as a combination of a specialized

network resource and a set of chat bots that integrate

it with network platforms. The results presented

above made it possible to formulate the requirements

for the proposed solution:

1. VK communities and Telegram channels are

not legitimate enough. The chatbot solves this

problem because it integrates with a high-tech

service.

2. People need to be confident that a service can

be trusted. Therefore, when designing a chat bot, it is

necessary to ensure the concealment of personal data.

3. Users need the ability to provide data in various

graphic formats for convenience.

4. The opportunity to exchange information in a

simple dialogue form is necessary. It is necessary to

design different dialog scenarios, different levels of

description for different clusters of users, considering

their peculiarities. Therefore, it is necessary to foresee

several chatbots targeted at specific user groups.

6 PROPOSED SOLUTION

A use-case diagram of the developed specialized web

resource is shown in Figure 15. The resource works

on the principle of teleradiology. Its datasets are

formed thanks to the attending physicians and their

patients, who are ready to provide their anonymized

medical information for forming datasets. The main

actors of this resource are the patient, the expert

doctor, the trainee, and the owner of the resource:

each of them gets a certain benefit from the use. It

also has a high level of privacy and security: this

inspires trust among users. The resource allows using

high-tech tools: it served as the motivation for

creating a bot. The chat-bot is served as an integration

tool. Through its implementation in social networks,

it engages people in using of a high-tech tool and

brings them together, thereby meeting their needs.

The chatbot is easy to use and does not require any

special knowledge.

Patient

TraneeExpertDoctor

Po rtal Ou ner

Medi c i ne User

Cre at in gr eport

Gettingarating

Editinganamnesis

Medicineuser 

registration

Interactingwith

potf olio

Interactingwithreports

sets

Patientregistration

Creatin garequest

Interrac tingwithreport

Viewingreport

<<ex te nd>>

Editin gtheyownreport

<<ex te nd>>

Viewingportfolio

<<ex te nd>>

Clearingportfol io

<<ex te nd>>

Uploaddata

<<ex te nd>>

Filteringreports

Creatingsetofreports

Viewingseto freports

<<ex te nd>>

Downlodingdataset

Discussionoft hereport

<<ex te nd>>

Figure 15: Use Case diagram of the specialized web

resource.

Figure 16 shows a use case diagram describing the

integration of the specialized web resources with

social media platforms. The interaction of social

platforms users with the service is realized through

the mechanism of chat-bots. Our chat-bots

(TelegramBot-MCP and VKBot-MCP) are developed

individually for each platform, since the mechanism

of interaction between a chat-bot and a social

platform depends on the API offered by the platform.

The business logic of interaction between the user

and the bot is implemented on the side of the bot's

ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health

160

software module. Social platforms provide users with

a conversational interface to a chatbot in a familiar

environment. Through the chatbot, the user can send

a request for a second opinion to the Platform and

receive answer as soon as it is ready.

The interaction of chat bots and the main part of

the platform is carried out through a special API,

which allows to scale the system and transparently

connect other social platforms. Currently the

specialized web resource for the effective use of high-

tech medical information «ITMO University's

Medical Crowdsourcing Platform» (ITMO_MCP)

has been launched on the platform of the ITMO

University (St. Petersburg, Russia).

Figure 16: Use Case diagram.

7 CONCLUSION

The article discusses the possibility of creating a

public resource for the effective use of high-tech

medical information. The tasks set by the authors of

the article have been successfully fulfilled. We have

investigated the structure of organization and self-

organization of community members in social

networks aimed at obtaining and effectively using

high-tech medical information. We have also

analyzed public medical Internet spaces (VK medical

communities in cardiology and Telegram medical

channels in cardiology and radiology), with the help

of which the motivations and needs of their

participants were identified.

Based on the results of the analysis of medical

communities, we proposed a solution that provides

the integration of social media platforms and

specialized web resources for the effective use of

high-tech medical information.

ACKNOWLEDGEMENTS

This work was supported Grant of the President of the

Russian Federation for state support of young Russian

scientists - candidates of science, MK-5723.2021.1.6.

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