Factors Affecting Students' Fake News Identification during COVID-19

in Vietnam: Access from Sociological Study and Application of

PLS-SEM Model

OANH, LU THI MAI

Faculty of Education Sciences, University of Education, Vietnam National University,

144 Xuan Thuy Street, Cau Giay, Ha Noi

VIETNAM

HUNG, LE NGOC

Faculty of Education Management, University of Education, Vietnam National University,

144 Xuan Thuy Street, Cau Giay, Ha Noi,

VIETNAM

TRA, PHAM HUONG

Faculty of Sociology and Development, Academy of Journalism and Communication,

36 Xuan Thuy Street, Cau Giay, Ha Noi,

VIETNAM

BINH, HA ANH

Vietnam Law Newspaper,

42/29 Nguyen Chi Thanh, Ngoc Khanh Ward, Ba Dinh District, Ha Noi,

VIETNAM

THUY, NGUYEN THI THANH

Women’s Research Institute, Vietnam Women's Academy,

68 Nguyen Chi Thanh Street, Dong Da, Ha Noi,

VIETNAM

DANG, NGUYEN DUC

Defense and Security Training Center, Vietnam National University,

VNU Town, Hoa Lac, Thach That, Hanoi,

VIETNAM

OANH, HO THI

Mia Montessori Preschool,

CT3-DN 3, Trung Van New Urban Area, Trung Van, Nam Tu Liem, Hanoi,

VIETNAM

LINH, PHAM DIEU

Faculty of Sociology, University of Social Sciences and Humanities, Vietnam National University,

336 Nguyen Trai Street, Thanh Xuan, Ha Noi,

VIETNAM

THUONG, ONG THI MAI

Faculty of Tourism and Social Work, Vinh University,

182 Lê Duan Street, Vinh, Nghe An,

VIET NAM

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2023.20.126

Oanh Lu Thi Mai,

Hung Le Ngoc, Tra Pham Huong et al.

E-ISSN: 2224-2899

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Volume 20, 2023

HA, PHAN THI THUY

Faculty of Tourism and Social Work, Vinh University,

182 Lê Duan Street, Vinh, Nghe An,

VIETNAM

PHUONG, BUI THI

Faculty of Social and Behavioral Sciences, Hanoi University of Public Health,

1A Duc Thang Street, Tu Liem, Ha Noi,

VIETNAM

Abstract: - This study investigates the ability of Vietnamese students to identify fake news in the context of the

COVID-19 pandemic and the factors that affect their performance in this regard. Data were collected from

1161 students at two universities in Vietnam between January and June 2022 using in-depth face-to-face

interviews and an online questionnaire survey. Results show that while a majority of students are aware of the

importance of verifying information, comparing sources, and identifying news factors, only 32.2% of students

can identify fake news. Factors such as interest in fake news, channels of receiving fake news, awareness,

attitudes, and behaviors towards fake news play a critical role in students' ability to recognize fake news.

Additionally, the study found that the features of fake news strongly and significantly correlate with the

identification of fake news. These findings highlight the need for media literacy education and critical thinking

training programs among Vietnamese students to help them navigate the complex information landscape and

identify fake news in the face of future pandemics or other events.

Key-Words: - Fake news, Covid-19, PLS-SEM model, sociological.

Received: January 28, 2023. Revised: June 7, 2023. Accepted: June 18, 2023. Published: June 30, 2023.

1 Introduction

Fake news is a term used to describe information

that is designed to resemble real news but lacks

objective evidence to verify its accuracy, [1], [2],

and competencies to verify the information, [3]. [4],

define fake news as "intentionally and verifiably

false news articles" that mimic the form of news

media but not its intent or organizational process,

[5]. [6], identified several categories of fake news,

including news satire, news parody, fabrication,

manipulation, advertising, and propaganda. Fake

news can be difficult to detect because it often

resembles legitimate news in terms of presentation,

website design, and use of images, [7]. Additionally,

fake news can be disseminated through a network of

fake websites to create the illusion of

comprehensiveness, [8]. While recent studies have

explored the incentives for spreading fake news, [9]

and the mechanisms for identifying it, [10], [11],

[12], [13], [14], there is still a need for more

accurate and comprehensive models for detecting

the increasingly sophisticated and complex fake

news that is being disseminated today.

To date, numerous approaches to fact-checking,

detecting, and verifying fake news have been

proposed. Studies have highlighted the urgent need

to design and develop effective solutions to combat

misinformation and detect fake news in its early

stages, [15], [16], [17]. According to, [18], there are

two approaches to fighting back against fake news:

(1) using human intervention to determine the

authenticity of the information, i.e. using human

intelligence to analyze information and distinguish

false news from authentic news; and (2) using

algorithms to identify fake news and validate

information sources. [19], emphasized two forms of

news authenticity: manual and automatic.

Automated systems in fact-checking can track the

spread of news using supervised machine learning

models, trained to evaluate combinations of

extracted features from the information content

itself, the information sources, and the types of

information dissemination, [20]. However, the

question remains whether news quality ratings and

fake news flagging are enough to influence

perceptions of news and its credibility. Therefore,

the analysis of the factors affecting the identification

of fake news should be studied comprehensively,

and attention should be paid to improving human

identification capacity in addition to the system of

fact-checking using machine learning techniques.

[21], identified five factors that influence the

credibility of fake news: the source (whether it is of

high or low credibility), the recipient (their level of

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DOI: 10.37394/23207.2023.20.126

Oanh Lu Thi Mai,

Hung Le Ngoc, Tra Pham Huong et al.

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knowledge), the message itself, the means of

dissemination, and the context in which the

information is received. [22], also identified similar

factors but emphasized the importance of the source

and channel of disseminating fake news. Other

researchers have pointed to the article source, URL

source link structure, source reliability, [23], news

message, [24], and diversity in the expression of

text-related characteristics, [25]. In particular, [26],

emphasize the importance of news readers being

aware of the need to critically evaluate the content

they consume. Moreover, the prevalence of

misinformation is often viewed as a manifestation of

widespread false beliefs, [27]. Although studies

have identified various factors for identifying fake

news based on characteristics of the fake news,

reception channels, perceptions, attitudes, and

behaviors about identifying fake news, these

indicators are still incomplete. A more

comprehensive study is needed to evaluate the

diversity in recognizing fake news.

In the specific context of the COVID-19

pandemic, the spread of fake news on social media

has contributed to the spread of the disease by

blurring the official announcements of health

authorities in the background on online platforms,

[28]. Google's trend analysis on the spread of

"misinformation" over the past three years (2018–

2020) showed a significant increase in the spread of

fake news globally in 2020. Despite Vietnam

employing a very effective COVID-19 prevention

strategy in the early stages of the pandemic, the

country has recorded 746,625 infected cases,

516,449 recoveries, 211,832 active cases, and

18,400 deaths as of the latest statistics from

VnExpress News (2021). The rapid increase in

COVID-19 cases not only affects people physically

but also mentally when too much false information

about the pandemic is shared on social media for

personal and group gain. According to the

Vietnamese Department of Cybersecurity and High-

Tech Crime Prevention, since the COVID-19

pandemic appeared, there have been more than

900,000 pieces of information related to the

pandemic in Vietnamese cyberspace, [29]. The

Supreme People's Procuracy of Vietnam reports that

in just over two months, police units throughout the

country have verified and worked with nearly 700

cases of false reporting, resulting in the prosecution

of more than 300 individuals who spread fake news

about the COVID-19 pandemic on cyberspace, [30].

However, the technical processing of fake news in

Vietnam is still very limited. Although a fake news

processing center has been established, it mainly

relies on a group of experts who passively receive,

and process reports from individuals and

organizations. As a result, the center's

announcement website has not been widely

welcomed by the public.

However, existing studies on fake news in

Vietnam have mainly focused on examining the

effects of fake news rather than exploring in-depth

the factors affecting the identification of fake news.

These studies have not specifically examined factors

that influence fake news identification with different

media channels, especially among students who are

frequent consumers of online content. Furthermore,

in the context of the COVID-19 pandemic, the

dissemination of fake news not only creates general

panic but also affects students in particular.

Therefore, this article aims to answer two research

questions: “What is the reality of students' fake news

identification in the context of the COVID-19

pandemic?” and “What are the factors affecting

students' identification of fake news?” The study

examines the factors affecting fake news recognition

through five groups of factors, including fake news

characteristics, fake news channels and perceptions,

attitudes, and behaviors when receiving fake news,

with 31 specific indicators. The research results not

only provide an overview of the situation regarding

fake news identification and the factors affecting

students' ability to identify fake news but also

suggest potential solutions for improving fake news

detection capabilities.

2 Theoretical Framework and

Research Hypothesis

2.1 Theoretical Framework

Fake news is a complex and multidisciplinary

phenomenon that requires an integrated approach

for a full understanding. This study draws on

multiple theoretical perspectives to identify the

factors that influence fake news recognition and

proposes future research to improve individuals'

ability to detect fake news. The different theories

provide complementary insights and together offer a

more comprehensive understanding of the topic. For

instance, [31], defines fake news as intentionally

and verifiably false information, while other

scholars, [32], [33], [34] have described fake news

as news that deliberately presents false information

with the intent to mislead audiences. [35], define

fake news as fabricated information that mimics

news media content in form but not in

organizational process or intent.

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Oanh Lu Thi Mai,

Hung Le Ngoc, Tra Pham Huong et al.

E-ISSN: 2224-2899

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To identify the factors affecting fake news

recognition, we propose a theoretical framework

composed of five groups of factors: (1)

characteristics of fake news, (2) channels and

perception of fake news, (3) attitudes towards fake

news, (4) responses to fake news, and (5) individual

characteristics that may affect fake news

recognition. We selected these factors based on

previous research that has demonstrated their

association with better fake news detection, and we

also considered the feasibility of measuring them in

our study.

By utilizing this theoretical framework, our

study aims to contribute to the development of

effective interventions to combat the spread of fake

news. By identifying the factors that influence

individuals' ability to recognize fake news, we can

design interventions that target those factors and

help people become more critical consumers of

information. The theoretical framework on factors

affecting fake news identification is presented in

Figure 1.

Fig. 1: Theoretical framework on factors affecting

fake news identification

2.2 Research Hypothesis

The primary objective of this study is to investigate

the current state of fake news recognition and

identify factors that influence students' ability to

recognize fake news. We have identified five

independent variables for this purpose: (1)

Characteristics of Information and Identifying Fake

News (COI), (2) Channels of Receiving Information

and Identifying Fake News (CRI), (3) Awareness of

Information and Identifying Fake News (AOI), (4)

Attitude towards Information and Identifying Fake

News (ATOI), and (5) Behavior of Information and

Identifying Fake News (BOI). These variables were

selected based on a review of the literature and are

expected to capture the most salient aspects of fake

news recognition from the perspective of students.

By examining these factors, we aim to provide a

more comprehensive understanding of the

challenges associated with fake news recognition

and identify potential interventions that could

improve students' ability to identify fake news. The

study will employ a mixed-methods design,

involving surveys and interviews, to collect data

from a sample of undergraduate students. We will

use statistical analyses to identify the relationships

between the independent variables and students'

ability to recognize fake news. The findings of this

study will provide insights into the factors that

affect fake news recognition and contribute to the

development of interventions to improve students'

ability to identify fake news. (1) Characteristics of

information and identifying fake news (COI)

Numerous studies have investigated the

characteristics of fake news and its impact on

recognition. [36], identified three common features

of fake news: presenting content as a mainstream

article with readers' feedback, relating to publishers

wanting to promote the content, and serving an

illegal purpose. [37], suggested that fake news often

contains offensive, confusing, or triggering

language. Moreover, [38], emphasized that fake

news is often disguised as engaging content based

on fake stories, posted online to increase profits. To

operationalize the concept of characteristics of

information and identifying fake news (COI) in this

study, we will use nine criteria for evaluating fake

news: (1) characteristics of the news, (2) clear

information, (3) specific purpose, (4) catchy title,

(5) official document format, (6) engaging content,

(7) reliable source, (8) multidimensional

information, and (9) emotional appeal. These

criteria are expected to capture the most relevant

aspects of fake news recognition from the

perspective of undergraduate students, the

population of interest in this study. By applying

these criteria, we aim to provide a rigorous and

systematic approach to identifying and evaluating

fake news in our study. We hypothesize that “the

characteristics of information have a significant

effect on the ability to identify fake news”

(Hypothesis 1), and that the use of the COI criteria

will aid in the identification of fake news. This

study will help to shed light on the factors that

contribute to the spread of fake news and the

strategies that can be used to combat it.

Hypothesis 1: Characteristics of information

affecting the ability to identify fake news

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(2) Channels of receiving information and

identifying fake news (CRI)

According to [39], the extensive usage of the

internet and social media platforms, such as

Facebook, WhatsApp, Twitter, and Instagram, has

had a noteworthy influence on information sharing.

Despite the advantages that these platforms offer,

they have also played a crucial part in the quick

dissemination of fake news and misinformation.

[40], argue that these channels enable the

transmission of substantial amounts of unverified

and unfiltered content, which could potentially

manipulate public opinion by spreading fake news.

Hence, this study aims to investigate the effect of

different channels on individuals' ability to identify

and resist fake news. We focus on Facebook, which

is popular in Vietnam, along with other social

networking channels such as Zalo, Twitter, and

Instagram. Additionally, we consider various

sources that may influence individuals' perception of

fake news, such as traditional media (newspapers,

radio, and television), online sources (unguaranteed

network sources), and interpersonal sources (friends

and relatives). Specifically, we hypothesize that “the

channel through which information is received

significantly affects individuals' ability to identify

and resist fake news” (Hypothesis 2). This study

will explore this hypothesis in the context of

Vietnamese university students, using a combination

of qualitative and quantitative research methods to

provide a robust and scientific investigation.

Hypothesis 2: Information-receiving channels

affect fake news identification.

(3) Awareness of information and identifying

fake news (AOI)

Previous research by [41], has established that an

individual's awareness during the information

processing stage can impact their decision to collect

more information to verify the authenticity of the

information, which is a crucial factor in the

identification of fake news. In this study, we

propose three specific characteristics related to

students' perceptions that may influence their ability

to identify fake news: (1) a diverse knowledge base

about various types of information, (2) recognition

of the elements that contribute to a message's

credibility or lack thereof, and (3) the ability to

engage in critical thinking while consuming

information. By examining the relationship between

these characteristics and the identification of fake

news among Vietnamese university students, we

aim to contribute to a more comprehensive

understanding of how individuals' perceptions can

affect their ability to distinguish between accurate

information and misinformation. Thus, we

hypothesize that “students' perceptions significantly

impact their ability to identify fake news”

(Hypothesis 3).

Hypothesis 3: Students’ perceptions affect fake

news identification.

(4) Attitude of information and identifying fake

news (ATOI)

Previous research by [42], [43], has identified

measures for assessing fake news related to biases

about problem prevalence and content diversity. In

this study, we investigate three specific

characteristics related to students' attitudes that may

affect their ability to identify fake news: (1) a

healthy level of skepticism when receiving

information, (2) a tendency to share articles as a

response to a personal basis, and (3) a tendency to

disregard or ignore the information. By examining

the relationship between these attitudes and the

identification of fake news among Vietnamese

university students, we aim to contribute to a deeper

understanding of how attitudes can impact

individuals' ability to distinguish between accurate

and inaccurate information. Accordingly, we

hypothesize that “students' attitudes significantly

influence their ability to identify fake news”

(Hypothesis 4).

Hypothesis 4: Students’ attitudes affect fake news

identification.

(5) Behavior of information and identifying fake

news (BOI)

Fake news, as defined by [44], refers to intentionally

false articles. To better understand student behavior

in identifying fake news, we examine a range of

habitual practices when receiving information, such

as checking reliability, comparing sources, verifying

information, and looking for unusual formatting. In

addition, we also look at behaviors related to

information response, including providing feedback,

checking the date, and sharing information. We

hypothesize that “the frequency and quality of these

behaviors will significantly impact the ability of

Vietnamese university students to identify fake

news” (Hypothesis 5). To test this hypothesis, we

will specifically investigate the relationship between

these behaviors and students' ability to identify fake

news with a set of established criteria.

Hypothesis 5: Behaviors related to receiving

and responding to information affect the

identification of fake news.

3 Research Methodology

The study design utilized a descriptive quantitative

research method with a survey questionnaire to

investigate factors that affect Vietnamese university

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students' ability to identify fake news. The

questionnaire consisted of five questions and 31

indicators, with a 5-point Likert scale, measuring

the influence of fake news characteristics, fake news

channels, and perceptions, attitudes, and behaviors

on students' recognition of fake news. The survey

instrument was developed based on previous

research and was reviewed by a panel of experts in

the field. Additionally, the questionnaire presented

participants with examples of fake news, enabling

them to consider their channels of reception,

transmission, and response to fake news.

Participants were selected based on their use of

Facebook and smartphone devices, which are the

two primary means used by students to interact and

find information. A total of 1,161 students were

included in the study after meeting the selection

criteria. The survey was distributed using both direct

and indirect methods, including a convenient paper-

based form and a Google Form. The survey data

were analyzed using descriptive statistics and

regression analysis. The reliability and validity of

the survey instrument were assessed through a pilot

study and statistical analysis. The general

characteristics of the study sample are presented in

Table 1.

Table 1. General characteristics of the study sample

Content

Frequency

Percentage %

University

University of

Education

574

49.4

HCMC University of

Technology and

Education

587

50.6

Gender

Male

340

29.3

Female

821

70.7

Year

Freshman

585

50.4

Sophomore

348

Junior

156

13.4

Senior

6.2

Hometown

Rural area

748

64.4

Urban area

413

35.6

The primary data for this study was collected

through both face-to-face and online surveys, with

1,161 students selected using a convenient random

sample. The study utilized several statistical

methods to analyze the data, including Cronbach's

Alpha reliability coefficient to assess the internal

consistency of the survey instrument, exploratory

factor analysis (EFA) to identify underlying factors

in the data, and structural equation modeling (SEM)

to test the study's hypotheses. The significance level

used in the statistical analyses was set at p < 0.05.

PLS-SEM (Partial Least Squares SEM) was used to

analyze the relationships between variables and to

predict key target variables related to the study's

research questions, including factors influencing

students' ability to identify fake news. The statistical

analyses revealed several key findings, including the

most significant factors influencing students' ability

to identify fake news and the specific characteristics

of fake news that are most likely to be overlooked

by students.

The PLS-SEM model was evaluated following

the methodology proposed by [45], which involved

two steps. The first step was evaluating the

measurement model for reliability, convergence,

and discriminant validity. The reliability of the

research instrument was ensured during the data

collection by using Cronbach Alpha, and some

entries were removed to improve reliability.

Specifically, all indicators under five hypothetical

groups ensured Cronbach Alpha values of 0.6 or

higher. The convergence was assessed using the

average variance extracted (AVE) for each

construct, which needed to be above 0.5. The

discriminant validity was evaluated using the

Fornell-Larcker criterion and cross-loadings. The

results showed that all constructs met the

established criteria. The second step involved

evaluating the structural model using the coefficient

of determination R² and the path coefficient. The R²

values indicate the proportion of variance explained

in the dependent variable, and values above 0.1 are

considered acceptable. The path coefficient

measures the strength and direction of the

relationship between the constructs. The analysis

showed that the model had a good fit with an R²

value of 0.682. The reliability of the estimates was

re-evaluated using the bootstrapping test method,

following the methodology proposed by [46]. This

method involves randomly drawing a sample from

the original data set, estimating the model

parameters, and repeating this process a large

number of times. The reliability of the estimates was

evaluated based on the standard error, which should

be small, and the confidence intervals, which should

not contain zero. The results showed that all

estimates were reliable and could be used for further

analysis.

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According to [47], when applying the PLS-

SEM model, the research model is evaluated

through two steps: (1) evaluate the measurement

model and (2) evaluate the structural model.

Initially, the measurement model is evaluated by

assessing the reliability, convergence value, and

discriminant validity of the measurement concepts

in the model. Next, the structural model is evaluated

through the coefficient of determination R², the path

coefficient. The last step is to re-evaluate the

reliability of the estimate. According to [48], the

bootstrapping test method is a suitable method to

evaluate the reliability of the estimates in the

analysis of linear structural models. The reliability

of the research instrument was guaranteed by

Cronbach Alpha during the data run and some

entries will be removed to improve reliability.

Specifically, the indicators mentioned under five

hypothetical groups all guarantee Cronbach Alpha

values of 0.6 or higher:

Table 2. Alpha values of the scales

Hpro

Variables

No. of

Items

Alpha

Coeffici

ent

characteristics of

information (COI)

0.848

Channels of

receiving

information (CRI)

0.676

Awareness of

information (AOI)

0.785

Attitude of

information (ATOI)

0.669

The behavior of

information (BOI)

0.808

In addition, the study also uses descriptive statistical

analysis and statistical tests to clarify students'

evaluations of factors affecting fake news

recognition. Lastly, Table 2 presents the Alpha

values of the scales.

4 Analysis and Results

4.1 The Reality of Students' Fake News

Recognition

To evaluate students' ability to recognize fake news,

we included two pieces of fake news with different

expressions in the survey, along with commonly

reported fake news quotations. However, for the

purposes of this paper, we focus on one specific

piece of fake news that showed a statistically

significant correlation in the PLS-SEM analysis.

After reading the fake news content, participants

were asked to select one of four methods to identify

fake news. The analysis revealed that the majority

of students were unable to correctly identify fake

news, and there was no significant difference in

performance between students from the two schools

with different majors.

Box 4.1 Fake news topic about the announcement

of returning to school in the event of the Covid-

19 outbreak.

On February 15th, 2021, a document

concerning the return of students to school

after the time of preventing and controlling

the covid 19 pandemic was shared on

social networks. We added a small change

to this document by altering the date on it.

The rest was kept the same, only the date

was changed to be more relevant in the

current context. A lot of students shared

and commented on this document as a

result.

Our study aimed to investigate students' ability to

recognize fake news by analyzing their responses to

a commonly shared piece of fake news on social

media that contained a quote from a well-known

public figure. Our analysis of the results showed

that a majority of the participants (61.2%; 710)

perceived the content as official and informational,

while 31.4% (365) considered it to be important and

highly reliable. Only a small proportion of students

(6.0%; 70) correctly identified the content as fake

news due to its ambiguous, unreliable, and

intentionally misleading nature. In addition, a

further 1.4% (16) of the participants did not belong

to any of the aforementioned groups. Our findings

underscore the challenges that students face in

recognizing fake news, up to 94.0% (1076) of

students failed to identify fake news and could not

recognise fake news, which has become a critical

issue in the current digital media environment. To

address this issue, it is essential to develop effective

strategies that enhance students' media literacy and

critical thinking skills. Our study contributes to the

growing body of research on the identification and

evaluation of information in different segments of

the public. Further research is needed to investigate

the underlying factors that contribute to the

difficulties students encounter in identifying and

evaluating fake news. The results of fake news

identification is presented in Table 3.

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Table 3. Results of fake news identification

Fake news

identification

Frequency

Percentag

e %

The information is

in official form

710

61.2

The information is

inaccurate

6.0

The information is

important and

trustworthy

365

31.4

Not belong to the

above groups

1.4

N=1161

4.2 Measurement Model

In this study, we utilized a Partial Least Squares

Structural Equation Modeling (PLS-SEM) approach

to examine six different groups of hypotheses about

the factors that influence students' ability to

recognize fake news. To ensure the reliability and

validity of our analysis, we followed established

guidelines for assessing the factor loadings,

composite reliability, convergent validity, and

discriminant validity of our latent constructs. We

verified that all the latent constructs had factor

loadings above the 0.60 threshold and that the

composite reliability coefficient (CR) was 0.7 or

higher. We also confirmed that the average variance

extracted (AVE) was 0.5 or greater to ensure

convergent validity. Our measurement model results

demonstrated that all of the latent constructs

satisfied these criteria, indicating that they were

reliable and valid measures of the corresponding

constructs. It is crucial to note that these measures

ensure the quality of the measurement model, which

is a crucial component of the PLS-SEM analysis. By

demonstrating the reliability and validity of the

measurement model, we can be confident in the

accuracy of our results and the validity of our

conclusions. In the next section, we will present the

results of our analysis and discuss their implications

for understanding students' ability to recognize fake

news. Lastly, Table 4 presents the factor loadings,

the composite reliability, and the average variance

extract.

Table 4. Factor loadings, Composite Reliability, and

Average Variance Extract

Variables

Items

Loadings

Channels

receiving

informatio

n (CRI)

CRI2

0.853

0.8

0.7

CRI3

0.826

CRI4

0.853

CRI5

0.884

Informatio

n of

interest

(IOI)

COI2

0.871

0.9

0.7

COI3

0.790

COI4

0.794

COI7

0.860

Awareness

informatio

n (AOI)

AOI1

0.724

0.8

AOI2

0.835

Attitude of

informatio

n (ATOI)

ATOI

0.754

0.7

0.6

ATOI

0.811

Behavior

informatio

n BOI

BOI1

0.897

0.9

0.7

BOI2

0.785

BOI3

0.894

BOI4

0.911

BOI5

0.881

BOI6

0.780

BOI10

0.722

Identificati

on of fake

news (IFN)

IFNI

0.791

0.7

0.8

IFNII

0.766

4.3 Discriminant Validity

To assess the discriminant validity of the latent

variables in our PLS-SEM model, we used both the

Fornell-Larcker criteria and the Heterotrait

Monotrait Ratio (HTMT) method, as recommended

by [49], and other scholars in the field, [50], [51].

According to the Fornell-Larcker criteria,

discriminant validity is established when the square

root of the average variance extracted (AVE) for

each latent variable is greater than its correlation

with other constructs, [51]. [52], suggest using the

HTMT ratio to evaluate discriminant validity, which

involves comparing the correlation between

different latent variables to a threshold value of

0.85. Our results, presented in Table 5 and Table 6,

show that all values for the latent variables were less

than 0.85, indicating that discriminant validity was

established among all the constructs in our model.

These findings suggest that our model is reliable and

valid and that each latent variable measures a

distinct construct. In the following sections, we will

present the results of our analysis and discuss their

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implications for understanding students' recognition

of fake news.

Table 5. Discriminant Validity by HTMT

AOI

ATOI

BOI

CRI

IFN

IOI

AOI

0.900

ATOI

0.259

0.783

BOI

0.680

0.133

0.675

CRI

0.234

0.069

0.535

0.706

IFN

0.167

0.109

0.491

0.706

0.446

COI

0.506

0.116

0.618

0.057

0.061

0.844

In addition, examining the Heterotrait - Monotrait

Ratio (HTMT) revealed that the Original Sample (0)

values are all less than 1, so the hypothetical model

is accepted.

4.4 Coefficient of Determinant

To assess the predictive ability of our structural

model of multiple regression relations, we computed

the coefficient of determination, also known as the

R² value. This statistic measures the proportion of

variation in the dependent variable that is explained

by the independent or predictor variables. In our

study, the dependent variable was students'

recognition of fake news, and we included several

independent factors that we operationalized based

on prior research in the field. The R² value ranges

from 0 to 1, with higher values indicating better

predictive performance. However, the interpretation

of the R² value depends on the context of the study

and the field of research. [53], suggests that R²

values up to 0.5 and 0.75 are considered moderate

and significant, respectively. In our study, we

obtained an R² value of 0.60, which suggests that

the selected factors explain 60% of the variation in

students' recognition of fake news. While this value

is above average and indicates that our model has

good predictive performance, it is important to note

that the interpretation of R² values depends on

various factors, such as the sample size, the

measurement instruments used, and the complexity

of the model. Therefore, our findings should be

interpreted in the context of our study design and

the limitations of our methodology. Additional

details on the specific factors included in the model

and their operationalization are provided in Table 7.

Table 6. Coefficient of Determination

Square

R Square

Adjusted

IFN

0.605

0.603

4.5 Structural Model

In order to assess the fit of the model to the data, we

employed the standardized root mean square

residual (SRMR) value, as recommended by [54].

An SRMR value of less than 0.1 is generally

considered an acceptable fit. In this study, the

SRMR value was found to be 0.044, indicating a

good fit of the research model to the sample of

students. To evaluate the strength and direction of

the relationships between independent and

dependent variables, we followed a structured

model evaluation approach suggested by [55], that

involves five steps. These steps include assessing

collinearity issues, determining the significance and

relevance of relationships, evaluating the value of

R², [56], measuring the size of the f² effect, [57],

and assessing the predictive relevance Q², [58]. The

results of our analysis revealed that the model did

not exhibit collinearity issues, as demonstrated by

the Inner VIF Values (AOI=2.009, ATOI=1.081,

BOI=2.309, CRI=1.013, IOI=1.654), which were all

less than 3 (VIF<3). Additionally, the Fornell-

Larcker criterion results confirmed the structural

validity of the model, while the initiation procedure

we used revealed that the model relationships were

both significant and relevant. Collectively, these

findings provide strong evidence for the validity and

robustness of the research model for our sample of

students.

To extend the applicability of the research

findings, we employed the bootstrapping technique

with a repeated sample size of 5000 observations to

assess the reliability of the model, as introduced by

[59]. Specifically, we used the bias-corrected

accelerated method to obtain approximate t-values

for testing the significance of the structural path in

this study. This process provides more accurate

estimates of the standard errors and confidence

intervals of the model coefficients, making the study

more robust and relevant, [60]. To evaluate the

predictive accuracy of the model, we calculated the

coefficient of determination (R²) and effect size (f²),

which measures the relative effect of the predictive

structure on the endogenous structure, [61], [62].

We then used a blindfold procedure to evaluate the

predicted fit (Q²) of the path model, systematically

deleting and predicting every point of data in the

measurement model that reflects the endogenous

structure. This resampling technique provides a

more accurate estimate of the model's predictive

power and helps to validate the results.

The conceptual model was used to test the

hypothetical relationships, which produced both

direct and indirect pathways with mixed results.

Results for H1 (β = 0.772, p < 0.001), H2 (β =

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0.204, p < 0.04), H3 (β = 0.222, p < 0.001), and H5

(β = 0.836, p < 0.022) showed statistically

significant correlations, indicating that these

hypotheses were accepted. However, the H4

hypothesis did not show a significant relationship

between ATOI and IFN (β = -0.020, p < 0.02),

indicating that this hypothesis was not accepted

(Table 7). These findings suggest that while certain

variables have a significant effect on students'

ability to recognize fake news, other variables may

not have a significant effect, highlighting the

complexity of the issue. Further analysis and

investigation are needed to better understand the

relationships between these variables and students'

ability to recognize fake news.

Table 7. Results of hypothesis testing

Hypo Path

Std.

Error

values

VIF

H1: CRI-

>IFN

0.772

0.029

26.543

0.000

1.013

H2: IOI-

>IFN

0.204

0.020

0.041

0.012

1.654

H3: AOI-

>IFN

0.222

0.030

23.265

0.000

2.009

H4:

ATOI-

>IFN

-0.020

0.019

1.098

0.272

1.081

H5: BOI-

>IFN

0.836

0.026

3.122

0.022

2.309

IFN

5 Discussion

To assess the fit of the model to the data, we

employed the standardized root mean square

residual (SRMR) value, as recommended by [63].

An SRMR value of less than 0.1 is generally

considered an acceptable fit. In this study, the

SRMR value was found to be 0.044, indicating a

good fit of the research model to the sample of

students from the University of Education and

HCMC University of Technology and Education.

To evaluate the strength and direction of the

relationships between independent and dependent

variables, we followed a structured model

evaluation approach suggested by [64], that involves

five steps. These steps include assessing collinearity

issues, determining the significance and relevance

of relationships, evaluating the value of R², [65],

measuring the size of the f² effect, [66], and

assessing the predictive relevance Q², [67]. The

results of our analysis revealed that the model did

not exhibit collinearity issues, as demonstrated by

the Inner VIF Values (AOI=2.009, ATOI=1.081,

BOI=2.309, CRI=1.013, IOI=1.654), which were all

less than 3 (VIF<3). Additionally, the Fornell-

Larcker criterion results confirmed the structural

validity of the model, while the initiation procedure

we used revealed that the model relationships were

both significant and relevant. Collectively, these

findings provide strong evidence for the validity and

robustness of the research model for our sample of

students.

Detecting and identifying fake news is a

persistent challenge for media consumers.

According to recent studies, people tend to

overestimate their ability to distinguish between real

and fake news, with only a small percentage able to

recognize disinformation on the internet, [68], [69].

College students are particularly vulnerable to fake

news due to their high reliance on social media as a

news source, [70]. In this study, we examined the

factors that affect college students' ability to

recognize fake news using a PLS-SEM analysis.

Our results showed that four out of the five groups

of factors in our conceptual model (information

literacy, online behavior, critical thinking, and

media literacy) were positively related to fake news

recognition, supporting our hypotheses. However,

we did not find a significant relationship between

attitudes toward online information (ATOI) and fake

news recognition. The findings suggest that

interventions aimed at enhancing students'

information literacy, online behavior, critical

thinking, and media literacy skills could be effective

in improving their ability to recognize fake news.

Future research should explore specific types of

interventions that could be implemented in college

settings to improve students' media literacy skills.

The first factor mentioned by students is the

receiving channel: official channels, Facebook

social networking sites, websites with unreliable

sources, friends, and relatives. [71], found that

adolescents who utilize digital devices to access

information and actively use social media platforms

have the ability to recognize fake news, [72]. In this

study, students mainly receive information from

Facebook, and access to many sources of unreliable

information from social networking channels and

unofficial websites will contribute to the ability to

misidentify fake news and spread fake news more

widely. Therefore, while fact-checking, one should

pay attention to the sources of information and the

channels from which they receive it and make a

comparison between the channels and sources to

identify fake news effectively.

The next factor affecting fake news

identification is information characteristics since

most students are interested in characteristics of

information such as whether it is news; clear

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content; specific purpose; catchy title; official form;

interesting content; reliable source;

multidimensional and objective information... This

is also important because the title of the article often

decides whether students should continue

consuming the information or not.

Similarly, another factor that can influence fake

news recognition is students' perception when

accessing information. Fake news often creates

confusion and a perception of ambiguity.

Individuals are more likely to believe specific

information if they have accessed it before.

Multiple studies have underscored the relationship

between anxiety and the perception of uncertainty.

Additionally, [73], [74], have pointed out that the

perception of uncertainty can lead to irrational and

mystical behaviors, thereby exacerbating anxiety

levels. Therefore, the perception of fake news

should be considered through the fact that students

have diverse knowledge about information, can

react to information, and recognize the factors that

respond to news to improve the ability to identify

fake news. Several studies have also demonstrated

that critical thinking can be a useful psychological

factor against fake news, [75].

In addition, the behavioral element also

contributes to the effectiveness of detecting fake

news. In this study, the group of behaviors to

identify fake news emphasizes indicators such as

checking the reliability of information; comparing

information sources; checking verifiable

information; checking if the source of information is

an authorized or official agency; checking for

unusual formatting (spelling, messy page layout);

finding ways to verify the information and take into

account the day, month, year… Facebook and other

sources have recommended several simple steps to

enhance viewers' media literacy, including: (a)

reviewing previous headlines, (b) verifying

published news sources, (c) confirming the time of

publication, (d) scrutinizing the author's prior work,

(e) verifying sources of evidence to support claims,

(f) using reverse image search tools, such as TinEye,

to trace images, (g) acknowledging confirmation

bias, and (h) searching for similar reports from other

sources, [76]. Checking and comparing information

sources, before readable information is essential to

effectively evaluate fake news.

Besides, the group of factors on attitudes

towards received information can also affect fake

news recognition. However, this study has not

proven it by rejecting the other hypothesis.

Skepticism towards received information will

contribute to improving the effectiveness of fake

news identification and avoiding the act of sharing

incorrect information. Nevertheless, the above

results have also contributed to reflecting the basis

of identifying fake news for students in the context

that Vietnam does not have many technical means

and tools to assess and identify fake news and the

spread of fake news in the context of the Covid-19

pandemic.

It can be seen that developing the capacity of

Vietnamese students to identify fake news is

essential in the absence of supporting tools to check

and control fake news. Therefore, the research has

an important role in studying the current status of

students' fake news identification and understanding

the factors affecting fake news detection. In regards

to factors affecting fake news identification; the

study focuses on the hypothetical factors that are the

characteristics of fake news of interest; information

content; channels from which students receive

information; their perceptions, attitudes, and

behaviors regarding fake news. However, this

article, based on the PLS SEM model, shows that

fake news’ characteristics greatly affect students'

detection. There are several characteristics taken

into consideration such as article title; information

content; information sources; message content;

information transmission path. This is also

important because the title of the article often

decides whether students will continue reading or

not. However, this article, based on the PLS SEM

model, shows that all factors have a great influence

on students' fake news recognition, except for the

factor of student's attitude towards information.

6 Conclusion and Recommendations

In Vietnam, there is a lack of in-depth research as

well as technology application and fake news

verification sites. Individuals often identify fake

news on their own, with only politically charged or

highly publicized cases being subject to examination

and verification. Given the widespread

dissemination of fake news on social media

channels, the Vietnam Fake News Center (VAFC),

authorized by the Department of Radio, Television,

and Electronic Information, has established the

General Electronic Information Website

(https://tingia.gov.vn/) to address this issue.

However, the verification process still relies heavily

on user reports and the VAFC lacks effective tools

to identify fake news in a timely and accurate

manner. Therefore, it is critical to identify the

factors that affect individuals' ability to identify fake

news, particularly among students, to improve their

capacity for identification. This will require the

development and implementation of appropriate

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strategies and technologies to enhance individuals'

ability to detect and combat fake news in the future.

Identifying fake news is a complex and

multidimensional process due to the characteristics

of fake news, the similarity between fake news and

real news, and public opinion. Therefore,

understanding the factors influencing fake news

identification should be approached

multidimensionally from a sociological perspective.

The research results show that students' ability to

recognize fake news is low. Regarding the factors

affecting fake news recognition, it shows that

students' interest in the characteristics of fake news,

the channel of receiving fake news and perception,

and behavior while facing fake news all have

statistical significance, and the correlation is quite

high. In addition, the factor of fake news attitude

showed a very high level of student agreement when

considering other factors such as fake news

characteristics, a channel for receiving fake news,

perception, and behavior related to fake news,

although not showing all the significant correlation

in consideration of statistical models.

The study was conducted in Vietnam in the

context of the COVID-19 pandemic, and the results

indicate that there is a lack of in-depth research as

well as technology application and fake news

verification sites in Vietnam. Identifying fake news

in Vietnam is broadly done individually, and only

fake news that is politically affected or affects

people's lives and attracts public attention will

undergo examination and verification. The Vietnam

Fake News Center (VAFC), which is authorized by

the Department of Radio, Television, and Electronic

Information, has been granted a No. 11/GP-

PTTH&TTĐT license dated January 11, 2021, to

establish the General Electronic Information

Website (https://tingia.gov.vn/). However, the

processing of fake news is still dependent on users’

reports, and the functional team lacks popular tools

to identify it quickly and promptly. Therefore, it is

essential to consider the factors affecting the

identification of fake news by individuals to

improve the identification capacity of people in

general and students in particular.

One of the limitations of this study is that the

demographics of the student sample were not

comprehensively assessed to determine how these

factors affect fake news recognition. Additionally,

unlike many foreign studies that use advanced

techniques to study fake news, Vietnam has only

one agency tasked with identifying and verifying

fake news, which limits its reach and effectiveness.

While technology can aid in identifying fake news,

the complexity and diversity of fake news, as well

as users' behavior, language, and habits, make it

challenging to limit its spread. Therefore, a multi-

dimensional approach to identifying fake news

based on six key aspects—characteristics, receiving

channels, readers' interests, awareness, attitude, and

behavior—is necessary to enhance students' ability

to recognize fake news. The analysis of the factors

affecting fake news identification provides a basis

for future research on improving students' fake news

recognition abilities.

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

-Oanh, Lu Thi Mai: Conceptualization Ideas;

formulation or evolution of overarching research

goals and aims.

Methodology, development or design of

methodology; creation of models.

Data curation, management activities to annotate.

Software Programming, software development;

designing computer programs; implementation of

the computer code and supporting algorithms;

testing of existing code components.

Formal analysis, application of statistical,

mathematical, computational, or other formal

techniques to analyze or synthesize study data.

Visualization, preparation, creation and/or

presentation of the published work, specifically

visualization/data presentation.

Writing - original draft Preparation, creation and/or

presentation of the published work, specifically

writing the initial draft (including substantive

translation).

Writing - review & editing Preparation, creation

and/or presentation of the published work by those

from the original research group, specifically critical

review, commentary or revision – including pre- or

post-publication stages.

-Hung, Le Ngoc: Methodology, development or

design of methodology; creation of models.

Supervision, oversight and leadership responsibility

for the research activity planning and

execution, including mentorship external to the core

team.

-Tra, Pham Huong: Methodology, development or

design of methodology; creation of models.

Supervision, oversight and leadership responsibility

for the research activity planning and

execution, including mentorship external to the core

team.

-Binh, Ha Anh: Investigation Conducting a research

and investigation process, specifically performing

the experiments, or data/evidence collection.

Project administration Management and

coordination responsibility for the research activity

planning and execution.

-Thuy, Nguyen Thi Thanh: Investigation

Conducting a research and investigation process,

specifically performing the experiments, or

data/evidence collection.

Project administration Management and

coordination responsibility for the research activity

planning and execution.

-Dang, Nguyen Duc: Investigation Conducting a

research and investigation process, pecifically

performing the experiments, or data/evidence

collection.

Project administration Management and

coordination responsibility for the research activity

planning and execution.

-Oanh, Ho Thi: Investigation Conducting a research

and investigation process, specifically performing

the experiments, or data/evidence collection.

Project administration Management and

coordination responsibility for the research activity

planning and execution

-Linh, Pham Dieu: Investigation Conducting a

research and investigation process, specifically

performing the experiments, or data/evidence

collection.

Project administration Management and

coordination responsibility for the research activity

planning and execution

-Thuong, Ong Thi Mai: Investigation Conducting a

research and investigation process, specifically

performing the experiments, or data/evidence

collection.

Project administration Management and

coordination responsibility for the research activity

planning and execution.

-Ha, Phan Thi Thuy: Investigation Conducting a

research and investigation process, specifically

performing the experiments, or data/evidence

collection.

Project administration Management and

coordination responsibility for the research activity

planning and execution.

-Phuong, Bui Thi: Investigation Conducting a

research and investigation process, specifically

performing the experiments, or data/evidence

collection.

Project administration Management and

coordination responsibility for the research activity

planning and execution.

Visualization, preparation, creation and/or

presentation of the published work, specifically

visualization/data presentation.

Writing - review & editing Preparation, creation

and/or presentation of the published work by those

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DOI: 10.37394/23207.2023.20.126

Oanh Lu Thi Mai,

Hung Le Ngoc, Tra Pham Huong et al.

E-ISSN: 2224-2899

1437

Volume 20, 2023

from the original research group, specifically critical

review, commentary or revision – including pre- or

post-publication stages.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflicts of Interest

The authors have no conflicts of interest to declare

that are relevant to the content of this article.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

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DOI: 10.37394/23207.2023.20.126

Oanh Lu Thi Mai,

Hung Le Ngoc, Tra Pham Huong et al.

E-ISSN: 2224-2899

1438

Volume 20, 2023