=Paper=
{{Paper
|id=Vol-3646/Paper_2
|storemode=property
|title=Towards the Information Technology Usage for E-Government Portal Assessment based on Web Data Extraction Techniques
|pdfUrl=https://ceur-ws.org/Vol-3646/Paper_2.pdf
|volume=Vol-3646
|authors=Andrii Kopp,Oleksandr Chornenkyi
|dblpUrl=https://dblp.org/rec/conf/iti2/KoppC23
}}
==Towards the Information Technology Usage for E-Government Portal Assessment based on Web Data Extraction Techniques==
https://ceur-ws.org/Vol-3646/Paper_2.pdf
Towards the Information Technology Usage for E-Government
Portal Assessment based on Web Data Extraction Techniques
Andrii Kopp 1 and Oleksandr Chornenkyi 2
1
National Technical University “Kharkiv Polytechnic Institute”, Kyrpychova str. 2, Kharkiv, 61002, Ukraine
2
V.N. Karazin Kharkiv National University, Svobody sq. 4, Kharkiv, 61022, Ukraine
Abstract
Today, interdisciplinary studies in computer science and social sciences, including political
science, are inevitable due to the need to work with web-based sources to gain valuable
insights, process large amounts of data, and apply various data analysis techniques. Web data
extraction or web scraping is important for social and political studies when it is necessary to
retrieve data arrays from a website for future analytical processing. Such automatic data
collection and processing is a promising interdisciplinary field for social scientists and
computer scientists. Therefore, this study aims to improve e-government web portal evaluation
processes by proposing a corresponding information technology based on web data extraction
techniques. The software implementation of the proposed technology is based on Python and
Power BI for computation and visualization, respectively. The proposed toolkit was used to
analyze the e-government web portals of two countries selected on the basis of their high e-
government development index, the obtained results of prevailing services on each of citizen
portals were analyzed and discussed, and the corresponding conclusions were made.
Keywords 1
E-Government Web Portal, Citizen Portal, Information Technology, Web Data Extraction.
1. Introduction
1.1. Motivation
Nowadays, the rapid evolution of computer technologies changes scientific approaches to modern
issues and provides ways for creating novel and enhancing existing research methods. It is especially
considerable for applied science wherein computational technique implementation accelerates the
complex applied problem solution requiring large volumes of calculations. Social sciences, which have
historical relations with philosophy, have a peculiar wide range of research methods. However, social
sciences are also in a transformation state and increasingly using computing technology for research
problem solving, which has led to the emergence of computational social sciences. Initially,
computational social sciences were associated with agent-based modeling for the simulation of the
behavior of an individual or social group under certain conditions. Nevertheless, the Internet spreading,
social networks and online platforms popularity increasing within the growth of numbers of Internet
users provoked a new large stream of digital data which has become a valuable source of information
for social sciences researchers and has led to the expansion of the concept of “computational social
science” [1]. Although earlier researchers have argued that digital data analysis-based computational
social science has developed slowly [2], more recent studies show that in recent times increased the
interest of social sciences scholars in using computational techniques for research [1].
1.2. Related Work
The use of information technologies for political science research is not as new as it may seem, and
began in the second half of the 20th century. The first experiments using computers were aimed at
Information Technology and Implementation (IT&I-2023), November 20-21, 2023, Kyiv, Ukraine
EMAIL: kopp93@gmail.com (A. Kopp); chornenkyi.o.o@gmail.com (O. Chornenkyi)
ORCID: 0000-0002-3189-5623 (A. Kopp); 0009-0001-9479-1776 (O. Chornenkyi)
©️ 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
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ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
12
trying to predict election results. Typically, all studies were based on the use of agent-based modeling
in different form, using classical theories of political interactions [3]. The further evolution of the
Internet, increasing the power and availability of computer technology provided new research fields for
political scientists and tools for expanding methodology. Political science methodology expansion led
to the fact that in addition to agent-based modeling, political scientists more often were beginning to
use methods related to big data analysis [4].
Scholars in social and political science address “big data” as a broad concept that includes any digital
elements left by users or organizations on the Internet that can be read by information technologies [5].
In the computer science field, big data is usually associated with so-called “5Vs” used to describe its
characteristics (value, variety, velocity, veracity, and volume) [6].
Today, for social sciences fields and particularly for political science, when working with big data,
it is important to use web scraping tools, which is the automatic extraction of data from websites for
further analytical processing [7]. For political science, this approach can be valuable for defining
features of how political parties or government agencies use their websites [5] and for researching local
politics through the mining and analysis of unstructured data from the websites of local government
institutions [8]. Some researchers conclude and we agree that the use of automated data mining in social
sciences opens a new way for cooperation between social and computer science researchers [6].
It should be stressed that political science must be in the continuous dynamic movement condition
and must permanently react to political changes in the modern world. Today, the policies of many
countries aim at the formation of an inclusive information society, which includes widespread digital
transformation. Governments create and support open e-government web portals, which aim to facilitate
citizens’ access to government information and improve the process of providing government services
to citizens. However, it should be noted that the politics of different states differ from each other and
may have various accents. Under such circumstances, it may be interesting how the policies of different
states influence their e-government web portals.
Thus, we propose an information technology that can help researchers to explore services provided
on government web portals, which can be useful for further analysis of different countries’ policies. It
aims to improve e-government web portal evaluation processes by using web harvesting techniques.
Therefore, this study is expected to answer the following research questions:
What reference model can be used to evaluate the e-government web portal?
What algorithms can be used to process and harvest the desired e-government web portal data?
How can the extracted data be quantitatively evaluated to compare the policies of different
countries and define the prevailing citizen services?
2. Materials and Methods
2.1. E-Government Web Portal Services Model
Let us formally describe the set of services that the e-government web portal is expected to provide:
𝑒𝐺𝑆 = {𝑒𝐺𝑆1 , 𝑒𝐺𝑆2 , … , 𝑒𝐺𝑆𝑛 }. (1)
Here 𝑛 is the number of services 𝑒𝐺𝑆1 , 𝑒𝐺𝑆2 , … , 𝑒𝐺𝑆𝑛 the e-government web portal is expected to
provide, 𝑖 = 1, 𝑛.
Moreover, for each of the e-government web portal services 𝑒𝐺𝑆𝑖 , 𝑖 = 1, 𝑛 we propose to define the
set of keywords 𝑊𝑖 , 𝑖 = 1, 𝑛 which completely describes the mentioned service:
𝛿: 𝑒𝐺𝑆 → 𝑊 = {𝑤 , 𝑤 , … , 𝑤 }. (2)
𝑖 𝑖 𝑖1 𝑖2 𝑖𝑚𝑖
Here 𝑚𝑖 is the number of synonymic keywords 𝑤𝑖1 , 𝑤𝑖2 , … , 𝑤𝑖𝑚𝑖 in 𝑊𝑖 , 𝑖 = 1, 𝑛 defined for 𝑖-th e-
government web portal service 𝑒𝐺𝑆𝑖 , 𝑗 = 1, 𝑚𝑖 .
Hence, the formal definition of E-Government Web Portal Services (EGWPS) model can be
formulated as given below:
𝑒𝐺𝑊𝑃𝑆 = 〈𝑒𝐺𝑆, 𝛿, 𝑊 〉. (3)
Here 𝑊 is the set of keyword sets mapped to each of the e-government web portal services, 𝑊 =
{𝑊1 , 𝑊2 , … , 𝑊𝑛 }. Let us graphically illustrate in Fig. 1 the proposed e-government web portal services
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model. Fig. 1 demonstrates the set of expected services and their keywords used to detect such services
on the e-government web portals under assessment. Using the proposed EGWPS model (Fig. 1), we
propose to find the “distance” between the e-government web portal under assessment and the so-called
“perfect” e-government web portal (in terms of its contents) described by this model.
Figure 1: Proposed e-government web portal services model
Therefore, we propose to use the web data extraction (or web scraping, web harvesting etc.)
technique to automatically explore and assess e-government web portals against the proposed EGWPS
model (Fig. 1).
2.2. Web Data Extraction Algorithm
Fig. 2 below illustrates the e-government web portal data meta-model given using the UML (Unified
Modeling Language) [9] class diagram.
Figure 2: Meta-model of the e-government web portal data
The e-government services data extracted from HTML (Hyper Text Markup Language) pages of a
corresponding web portal is represented as the set of HTML hyperlink tags [10]:
𝐻 = {ℎ1 , ℎ2 , … , ℎ𝑝 }. (4)
Here 𝑝 is the number of hyperlinks ℎ1 , ℎ2 , … , ℎ𝑝 located on e-government web portal HTML pages,
𝑘 = 1, 𝑝.
Each hyperlink tag includes the text and URL (Unified Resource Locator) as it is demonstrated in
the meta-model diagram in Fig. 2.
The set of e-government web portal services 𝑒𝐺𝑆 extracted from the respective HTML pages is
represented as follows:
𝑆 = {𝑆1 , 𝑆2 , … , 𝑆𝑛 }. (5)
Each service 𝑆𝑖 , 𝑖 = 1, 𝑛 is expected to be digitally implemented by one or multiple hyperlinks
located on the e-government web portal pages:
𝑆𝑖 = 𝐻𝑆𝑖 ⊆ 𝐻. (6)
Here 𝐻𝑆𝑖 is the sub-set of hyperlinks extracted from the e-government web portal that implement 𝑖-
th service detected in the e-government web portal 𝑆𝑖 , 𝑖 = 1, 𝑛. Therefore, to detect services provided
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by the e-government web portal using the proposed EGWPS model (Fig. 1) and the meta-model (Fig.
2), the following algorithm should be used:
Given: set of extracted e-government web portal hyperlinks 𝐻
EGWPS model 〈𝑒𝐺𝑆, 𝛿, 𝑊 〉
empty set of detected e-government web portal services 𝑆
for each 𝑒𝐺𝑆𝑖 in 𝑒𝐺𝑆:
for each ℎ𝑘 in 𝐻:
𝑊𝑖 = 𝛿 (𝑒𝐺𝑆𝑖 )
for each 𝑤𝑖𝑗 in 𝑊𝑖 :
if 𝑤𝑖𝑗 is a substring of ℎ𝑘 text:
𝑆𝑖 ← ℎ𝑘
end
end
end
The input set 𝐻 of the e-government web portal hyperlinks can be extracted using web scraping tools
in Python or other programming languages.
The output set 𝑆 basically represents the instances of Service class defined in the proposed meta-
model (Fig. 2). Furthermore, each service 𝑆𝑖 , 𝑖 = 1, 𝑛 has multiple hyperlinks that belong to 𝐻.
Fig. 3 graphically illustrates the proposed algorithm using the UML activity diagram [9].
Figure 3: Proposed algorithm for e-government services data extraction from a web portal
2.3. E-Government Portal Assessment Metrics
Finally, we propose the following metrics to assess the e-government web portal in terms of detected
services. The following metric allows to find the number of services detected in the e-government web
portal under assessment:
𝑆𝐷 = |{𝑆𝑖 ∈ 𝑆, 𝑆𝑖 ≠ ∅}|. (7)
The following metric allows to find the “service richness” calculated as the relative number of
services of the e-government web portal under assessment in comparison to the reference EGWPS
model (Fig. 1) [11]:
15
1
𝑆𝑅 = 𝑆𝐷. (8)
𝑛
The following metric allows to find the “relative cardinality” of the particular service calculated as
the relative number of hyperlinks used to implement the 𝑖-th service detected in the e-government web
portal under assessment in comparison to the maximum possible number of hyperlinks used in the same
web portal for a certain service [12]:
1
|𝑆 |, max |𝑆𝑖 | > 0
max |𝑆𝑖 | 𝑖 𝑖=1,𝑛
𝑆𝐶𝑖 = 𝑖=1,𝑛 (9)
0, max |𝑆𝑖 | = 0
{ 𝑖=1,𝑛
The following metric allows to find the total “service balance” to assess the balance of hyperlinks
related to services detected in the e-government web portal under assessment:
𝑛
1
𝑆𝐵 = ∑ 𝑆𝐶𝑖 . (10)
𝑛
𝑖=1
Using the following algorithm, it is possible to evaluate an e-government web portal against the
EGWPS model. Thus, as a reference model, we can use the experience and best practices of the most
advanced e-government web portals, define the set of services 𝑒𝐺𝑆 a portal is expected to provide, and
the keywords 𝑊 to detect such services in corresponding HTML web pages.
Fig. 4 graphically illustrates the proposed algorithm using the UML activity diagram [9].
Figure 4: Proposed algorithm for e-government services evaluation
2.4. Information Technology for E-Government Portal Assessment
Finally, the information technology for e-government web portal assessment can be formally
described using the following tuple:
𝑒𝐺𝑊𝑃𝐴𝐼𝑇 = 〈𝑒𝐺𝑊𝑃𝑆, 𝑆, 𝐴𝑀〉. (11)
Here 𝐴𝑀 is the algorithmic model, which includes the proposed algorithms (Fig. 3 and Fig. 4) [13]:
𝐴𝑀 = (𝐴 = {𝐴1 , 𝐴2 }, 𝑅 ⊂ 𝐴 × 𝐴). (12)
Here 𝐴 is the set of algorithms, where 𝐴1 is the data extraction algorithm and 𝐴2 is the evaluation
algorithm; 𝑅 describes the interconnections between the proposed algorithms when used to assess an e-
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government web portal. Selected e-government web portals will be analyzed using the proposed
information technology implemented using Python, in-build packages, and third-party libraries:
“urllib” – used the “request” module to open and work with URLs [14];
“re” – for regular expressions operations to parse web pages [15];
“json” – to save results as JSON (JavaScript Object Notation) [16];
“bs4” – used the “Beautiful Soup” library to scrape information from web pages of citizen portals
[17].
Fig. 5 below demonstrates the Data Flow Diagram (DFD) [18] of the data processing workflow
implemented by the proposed information technology.
Figure 5: Data processing workflow for e-government portal assessment
According to Fig. 5, obtained results are displayed using Power BI – a high-performance Business
Intelligence (BI) tool for advanced data visualization and data-driven decision making [19].
Fig. 5 illustrates the hands-on usage of the proposed information technology.
3. Results and Discussion
3.1. E-Government Portal Data Extraction: Citizens Viewpoint
Let us form the reference EGWPS model considering the Integrated Architecture Framework for E-
Government (IAFEG) shown in Fig. 6 [20]. In this study, we focus on the “Social Sub-system” layer
of this framework, in particular – on its “Citizens” perspective [20]. The citizens’ viewpoint according
to IAFEG [20] includes the following services (or topics) expected from an e-government web-portal:
taxation;
education;
health;
immigration;
employment.
The IAFEG-based set of services the e-government web portals are expected to provide 𝑒𝐺𝑆 (from
the citizens’ viewpoint of the IAFEG “Social Sub-system” layer) and the keywords 𝑊 used to describe
each of the services on HTML web pages are given in Table 1.
According to the “UN E-Government Knowledgebase” and its UN (United Nations) E-Government
Survey 2022, top five countries with the highest E-Government Development Index are Denmark
(0.9717), Finland (0.9533), Republic of Korea (0.9529), New Zealand (0.9432), and Iceland (0.9410).
Denmark citizen portal “Life in Denmark.dk” is shown in Fig. 7 [21]. The “Life in Denmark.dk”
portal offers topics related to immigration, housing, working, family and children, money and taxation,
education, healthcare, travel and transportation, pension, rights, leisure and networking, as well as
stand-alone digital services (Fig. 7) [21]. Table 2 shows hyperlinks detected on the “Life in
Denmark.dk” citizen portal according to IAFEG taxation, education, health, immigration, and
employment services [20]. The “Suomi.fi” portal offers similar topics to “Life in Denmark.dk”. These
topics are connected to family, social security, healthcare, education, working, housing, rights and
17
obligations, finances and taxation, moving and travelling (Fig. 8) [22]. Table 3 shows hyperlinks
detected on the “Suomi.fi” citizen portal according to IAFEG [20].
Figure 6: Integrated Architecture Framework for E-Government [20]
Table 1
Proposed EGWPS model contents based on IAFEG [20]
Services Keywords
Taxation tax, finance, income, money, debt, credit
Education education, school, study, child, training, student
Health health, insurance, care, sick, medical, funeral
Immigration immigration, citizen, travel, visa, residence, international
Employment employment, work, job, business, license, certification
Figure 7: Denmark e-Government web portal “Life in Denmark.dk” [21]
Finland citizen portal “Suomi.fi” is shown in Fig. 8 [22]. In this study we focus on the top two
countries (Denmark and Finland) and their citizen portals. First of all, their impact according to the E-
Government Development Index is greater than 0.95. Another country, which aspirations were highly
estimated is Republic of Korea, however, we failed to access its English web portal version.
Therefore, we obtained the following e-government web portals evaluation results (Table 4). Here
𝑆𝐶𝑖 , 𝑖 = 1,5 describe taxation, education, health, immigration, and employment services.
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Table 2
Extracted data from the Denmark e-Government web portal [21]
Service Link Fitness
Taxation More about Money and tax True
Tax matters and what taxes are spent on True
Taxation on purchase and sale of real property True
Education More about Family and children True
More about School and education True
Child allowance False
Framework for the primary school True
Admission to higher education in Denmark True
Danish language training True
Health More about Healthcare True
Health insurance card True
How the Danish healthcare system works? True
Immigration More about Travel and transport True
ICS: International Citizen Service True
Conditions for foreign citizens True
Residence in Denmark for EU/EEA citizens True
Employment More about Working True
More about Leisure and networking True
Framework for the primary school False
How the Danish healthcare system works False
Figure 8: Denmark e-Government web portal “Suomi.fi” [22]
3.2. Citizen Web Portal Data Analysis
Fig. 9 demonstrates the structure of JSON documents produced for Power BI visualization, which
contain calculated metrics for assessed citizen web portals.
19
Figure 9: Structure of produced JSON documents
Table 3
Extracted data from the Finland e-Government web portal [22]
Service Link Fitness
Taxation Income support True
Managing your personal finances True
Taxation and public finances True
Education Having children False
Pre-primary education and schooling True
Studying True
Livelihood and social assistance of students True
Health Informal career for a loved one False
Staying healthy True
Immigration For citizens True
Travel True
Employment Unemployment True
Starting a business True
Rules of working life True
Work in Finland True
Contact business advice True
Table 4
E-government web portals evaluation results
Web portal 𝑆𝐷 𝑆𝑅 𝑆𝐶1 𝑆𝐶2 𝑆𝐶3 𝑆𝐶4 𝑆𝐶5 𝑆𝐵
Life in Denmark.dk 5 1.00 0.50 1.00 0.50 0.67 0.67 0.67
Suomi.fi 5 1.00 0.60 0.80 0.40 0.40 1.00 0.64
Here in Fig. 9 we have the following JSON properties:
“ServicesDetected” represents 𝑆𝐷;
“ServiceRichness” represents 𝑆𝑅;
“ServiceCardinality” represents 𝑆𝐶𝑖 , 𝑖 = 1,5 accroding to IAFEG citizen services of taxation,
education, health, immigration, and employment;
“ServiceBalance” represents 𝑆𝐵.
Fig. 10 illustrates the Power BI dashboard developed to visualize JSON-based data and display
citizen portal web services assessment results. Analyzing obtained results (Table 4 and Fig. 9 – 10), we
can assume that:
both evaluated “Life in Denmark.dk” and “Suomi.fi” citizen service web portals demonstrate the
highest service richness values (1.00), which signalize their general correspondence to 5 citizen services
defined by IAFEG [20];
evaluated citizen web portals focus differently on provided services: “Life in Denmark.dk” is
mostly focused on education (1.00), immigration (0.67), and employment (0.67), while “Suomi.fi” on
employment (1.00), education (0.80), and taxation (0.60);
both evaluated citizen web portals have moderate “service balance” scores of 0.67 for “Life in
Denmark.dk” and 0.64 for “Suomi.fi”, which confirms the previous observations.
20
Figure 10: Power BI dashboard
Moreover, let us estimate the correlation value between 𝑆𝐵 and E-Government Development Index
(EGDI) values [23]. The obtained Pearson’s correlation coefficient [24] value is 1.00, which signalize
absolute positive relation between EGDI estimated by UN and “service balance” scores. Finally, let us
estimate the accuracy of the proposed information technology using the following formula:
(∑ 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 = 𝑇𝑟𝑢𝑒)
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = . (13)
(∑ 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 = 𝑇𝑟𝑢𝑒) + (∑ 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 = 𝐹𝑎𝑙𝑠𝑒)
Here ∑ 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 = 𝑇𝑟𝑢𝑒 is the number hyperlinks estimated as correctly categorized against IAFEG
services [20], and ∑ 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 = 𝐹𝑎𝑙𝑠𝑒 is vice versa (see Table 2 – 3). Hence, the accuracy of proposed
information technology for e-government web portal assessment is 0.80 for Denmark and 0.88 for
Finland. However, the total accuracy for both estimated citizen web portals “Life in Denmark.dk” and
“Suomi.fi” is 0.83.
Therefore, the proposed information technology allows to obtain accurate (of 83%) e-government
web portal assessment results and can be suggested scholars in social political science fields.
4. Conclusion and Future Work
In this paper we proposed the information technology for e-government web portal assessment based
on web data extraction techniques. The study aims to improve the processes of e-government web portal
assessment by using web harvesting and data analysis approaches. Therefore, we developed algorithms
to extract and assess e-government web portals using the proposed E-Government Web Portal Services
reference model and evaluation metrics. The software implementation of the proposed technology is
based on Python programming language and Power BI data visualization tool. Such a tool allows non-
technical users, i.e. social or political science scholars, to configure the desired references models and
automatically assess e-government web-portals as part of their studies with the accuracy of 83%.
The following conclusions can be made after the obtained results analysis:
this approach has a room to identify the differences between e-government web portals and the
services they provide;
consequently, for researchers who study and compare the state of information society formation
in different countries, in terms of digital services provision, conducting such experiments can be a useful
complement to other data-driven methods;
the need for interdisciplinary cooperation between social and computer science is increasing and
such interdisciplinary studies can benefit both domains with new methods and solutions.
21
In the future we plan to elaborate metrics proposed to evaluate e-government web portals, as well as
conduct a large-scale study, with more government portals and more careful sampling, to identify and
study their differences. From the information technology viewpoint, such experiments require advanced
techniques to be applied, such as data warehousing, data mining, and data visualization.
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