JMIR FORMATIVE RESEARCH

Guelmami et al

Original Paper

Development of the 12-Item Social Media Disinformation Scaleand its Association With Social Media Addiction and Mental Health
Related to COVID-19 in Tunisia: Survey-Based Pilot Case Study
Noomen Guelmami1,2,3, PhD; Maher Ben Khalifa4, MSc; Nasr Chalghaf2,3,5, PhD; Jude Dzevela Kong6, PhD; Tannoubi
Amayra1, MSc; Jianhong Wu6, PhD; Fairouz Azaiez2,3,5, PhD; Nicola Luigi Bragazzi3,6, MD, MPH, PhD
1Higher Institute of Sport and Physical Education of Kef, University of Jendouba, Jendouba, Tunisia

2Group for the Study of Development and Social Environment (GEDES), Faculty of Human and Social Science of Sfax, Sfax, Tunisia

3

Postgraduate School of Public Health, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy

4

Research and Applications Unit in Marketing (URAM), Faculty of Economics and Management of Tunis (FSEGT), University of Tunis El Manar(UTM), Tunis, Tunisia
5

Higher Institute of Sport and Physical Education of Sfax, University of Sfax, Sfax, Tunisia

6

Department of Mathematics and Statistics, York University, Toronto, ON, Canada

Corresponding Author:
Nicola Luigi Bragazzi, MD, MPH, PhD
Department of Mathematics and Statistics
York University
4700 Keele Street
Toronto, ON, M3J 1P3
CanadaPhone: 1 2818392210
Email: robertobragazzi@gmail.com

Abstract
Background: In recent years, online disinformation has increased. Fake news has been spreading about the COVID-19 pandemic.
Since January 2020, the culprits and antidotes to disinformation have been digital media and social media.
Objective: Our study aimed to develop and test the psychometric properties of the 12-item Social Media Disinformation Scale(SMDS-12), which assesses the consumption, confidence, and sharing of information related to COVID-19 by social media users.
Methods: A total of 874 subjects were recruited over two phases: the exploratory phase group had a mean age of 28.39 years(SD 9.32) and the confirmatory phase group had a mean age of 32.84 years (SD 12.72). Participants completed the SMDS-12,
the Internet Addiction Test, the COVID-19 Fear Scale, and the 10-item Perceived Stress Scale. The SMDS-12 was initially tested
by exploratory factor analysis and was subsequently tested by confirmatory factor analysis.
Results: The test supported the three-factor structure. In addition, no items were removed from the measurement scale, withthree factors explaining up to 73.72% of the total variance, and the items had a lambda factor loading ranging from 0.73 to 0.85.
Subsequently, confirmatory factor analysis confirmed the robustness of the measure by referring to a wide range of goodness-of-fit
indices that met the recommended standards. The construct validity of the scale was supported by its convergent and discriminant
validity. The reliability of the instrument examined by means of three internal consistency indices, and the corrected item-total
correlation, demonstrated that the three dimensions of the instrument were reliable: Cronbach α values were .89, .88, and .88 forthe consumption, confidence, and sharing subscales, respectively. The corrected item-total correlation ranged from 0.70 to 0.78.
The correlation of the instrument’s dimensions with internet addiction and mental health factors showed positive associations.
Conclusions: The SMDS-12 can be reliably utilized to measure the credibility of social media disinformation and can be adapted
to measure the credibility of disinformation in other contexts.
(JMIR Form Res 2021;5(6):e27280) doi: 10.2196/27280
KEYWORDS
COVID-19 pandemic; media disinformation; social media addiction; mental health; scale validation

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Introduction
During the COVID-19 pandemic, caused by the emerging
SARS-CoV-2, people around the world have been leaning
toward an excessive use of the internet [1] and social media.
This is the case because, on the one hand, this activity can lower
their feelings of loneliness and, on the other hand, it can provide
them with information on the states of emergency in theircountries and globally [2].
This pandemic is characterized by a high potential for contagion,a low availability of vaccines, an absence of specificallyeffective drugs, and an exponential spread, which has impacted
people’s lifestyles and led to feelings of insecurity [3,4], fear[5], and even community panics in several populations [6-10].
Almost everyone is interested in hearing reliable, updated
information concerning the pandemic, vaccines, and anything
related to COVID-19. This is because during the pandemic, inaddition to seeing their usual activities restricted, people areexposed to a wide range of information, including official
messages, as well as erroneous and misleading news from a
range of unreliable sources [11,12]. The global spread of the
COVID-19 pandemic has been reflected in the disseminationof misinformation on social media and conspiracy theories about
its origins [13].
Indeed, since the beginning of the spread of the disease, several
fake news items related to the outbreak have been shared onsocial networks. Examples include that the virus was caused by
5G cell phones, was deliberately disseminated for political or
financial reasons, was a biological weapon, or was not moredangerous than influenza, with the threats being exaggerated
as a way of limiting freedom [14].
Sharing false news that contains biased, emotionally charged
information tends to capture more attention and interest thandetached, positive, or neutral information [15]. Communication
is of crucial importance in the control of outbreaks, andmisinformation represents a major public health concern in thatthe use of social media as a means of keeping abreast of all the
pandemic-related news is becoming very popular for several
categories of people, due to its capacity of providing informationin real time [16]. Likewise, social media can be utilized as
platforms and venues for disseminating false information intimes of crisis [16].
From another perspective, according to Alheneidi et al [17],besides information and communications technologies,
psychosocial factors seem to play a key role. Personal negative
feelings, such as loneliness, experienced during the
COVID-19–induced lockdown have been shown to promoteinternet addiction behaviors, resulting in a significantly
increased number of hours spent online. The study was
conducted in two Arabic countries—Kuwait and the Kingdomof Saudi Arabia—and showed that people who experienced
greater loneliness were more likely to consume pandemic-related
news from social media.
Governments have been implementing behavioral strategies
and nonpharmaceutical interventions (NPIs), including socialand physical distancing and stay-at-home orders, to control the
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Guelmami et al
spread of COVID-19 and flatten the epidemic curve [18,19].
As a consequence, addiction to social media has increased, as
it is the most accessible and easy-to-use means ofcommunication and social interaction, resulting in excessive
news consumption, which can lead to acute psychologicaldistress and mental health problems, such as anxiety anddepression [20].
The public health measures thus taken and enforced bygovernments, such as the compulsory wearing of masks,
quarantine, mobility restrictions, social and physical distancing,the closures of several public places, bans on gatherings, partial
curfews, and isolation of sick people, risk being compromised
because of erroneous information constantly propagated onsocial media platforms. Indeed, Wang et al [21] have found that
health-related misinformation is a very common phenomenonon social media and tends to be more prevalent than the diffusionof accurate information, in general.
As a matter of fact, significant amounts of disinformation andconspiracy theories have been disseminated through several
social media platforms and consumed by users willing to learn
about the COVID-19 pandemic. In general, the COVID-19
outbreak was accompanied by a large proliferation of fictitious
and inaccurate information on the virus, which was spread, inparticular, by social networks [22].
In a descriptive study by Cinelli et al [23] on the disseminationof COVID-19–related information on five social media
platforms—Twitter, Instagram, YouTube, Reddit, andGab—analyses highlighted a great amount of information about
the COVID-19 outbreak disseminated on social networks, alarge part of which was false information or disinformation.
COVID-19–related misinformation can bring not only highstress rates and serious mental consequences [24], but can also
have a negative impact on the effectiveness of government
strategies, such as the compulsory wearing of masks,
confinement, and social and physical distancing. For instance,
the false belief that the virus threat is being exaggerated may
result in poor compliance and adherence to NPIs and, therefore,
jeopardize the fight against the coronavirus. In the health field,
dissemination of spurious news poses serious challenges because
it can delay or prevent the delivery of effective care provisions
or even threaten people’s lives.
Unfortunately, many fake news items are accepted by thegeneral population. For instance, a recent US study on
COVID-19 conspiracy speculation found that over 80% ofparticipants surveyed believed a particular conspiracy theory
to be “probably” or “certainly” true [25]. In another study
conducted in the United States, Uscinski et al [26] found that
29% of subjects believed that the communication on COVID-19
was biased for political reasons, in order to place then–US
President Donald Trump at a disadvantage against hiscompetitors.
If false news is accepted as true, dissemination of scientifically
proven and evidence-based narratives to amend such fake news
would not have a significant impact on belief in disinformation[27].

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Although governments, public health decision makers and policy
makers, and other stakeholders are suffering from thedissemination and sharing of misinformation on social media,
there exists no scale that enables the quantitative assessment of
the behavior of social media users in the face of misinformation
related to COVID-19.
Therefore, the objective of this study was to develop and validate
an ad hoc measurement tool to measure the behavior of social
media users in terms of consumption, credibility, and sharing
of information related to COVID-19.

Methods
Ethical Declaration
The protocol for this study received approval from the Ethics
Committee of the Higher Institute of Sport and Physical
Education of Kef, University of Jendouba, Jendouba, Tunisia.
The study protocol also received ethical authorization from the

Guelmami et al
UNESCO (United Nations Educational, Scientific and Cultural
Organization) Chair in Health Anthropology Biosphere andHealing Systems, University of Genoa, Genoa, Italy, as well asfrom the Higher Institute of Sport and Physical Education of
Sfax, Sfax, Tunisia. The proposal was also approved by the
Ethics Committee of the University of Jendouba. This study
was undertaken in accordance with the ethical standards of the
Declaration of Helsinki in 1964 and its subsequent amendments.

Participants and Data Collection
A total of 874 subjects, with a mean age of 30.62 years (SD
11.37), who were recruited from social media platforms over
two time periods participated in this study. Participants wereinterviewed by means of an online questionnaire distributed via
two social media platforms: Facebook and Twitter. The
characteristics of the participants (ie, gender, student or
employment status, academic level, and marital status) arepresented in Table 1.

Table 1. Sociodemographic characteristics of the participants selected for this study.
Characteristic

Value (N=874), n (%)

GenderMale

415 (47.5)

Female459 (52.5)

Student or employment status
Student

297 (34.0)

Public function employee

211 (24.1)

Unemployed94 (10.8)

Private function employee

233 (26.7)

Retired39 (4.5)

Academic level
Secondary

252 (28.8)

University

622 (71.2)

Marital status
Single446 (51.0)

Married304 (34.8)

Other

124 (14.2)

Study participants were randomly divided into two groups withthe same number of individuals in each: one group participatedin the exploratory phase and the other participated in theconfirmatory phase.
The exploratory phase group consisted of 437 out of 874(50.0%) participants, of which 248 (56.8%) were female and
189 (43.2%) were male; the mean age of the participants in thisgroup was 28.39 years (SD 9.32). The confirmatory phase groupconsisted of 437 out of 874 (50.0%) participants, of which 211(48.3%) were female and 226 (51.7%) were male; the mean ageof the participants in this group was 32.84 years (SD 12.72).

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Instruments
Sociodemographic Questionnaire
The sociodemographic questionnaire consisted of questions
about age, gender, level of education, the city in which theparticipant was currently living during the COVID-19–induced
restrictions, student or employment status, and marital status.

Development of the 12-Item Social Media Disinformation
Scale
A thorough review of the literature showed that information
consumption includes a series of behaviors and processes, suchas information seeking and information encounter (ie, finding
without seeking). The first is defined as the intentional
acquisition of information, while information encounter
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Guelmami et aldescribes how individuals come across information without
deliberately seeking or retrieving news [28].

mean square error of approximation [RMSEA]=0.059;
standardized root mean residual [SRMR]=0.024).

Understanding social media consumption has proven to be avery important dimension to incorporate into the measurement
instrument, as it can help analyze how people may facedisinformation. The literature has shown that individuals who
consume disinformation make a judgment on the credibility ofthe message, depending on the source of the information, thestory, and the context [29]. Indeed, the work of Rosnow [30]has shown that if disinformation circulates repeatedly, it will
be absorbed, reinforced, and accepted as credible.

The 10-Item Perceived Stress Scale

A further step in the process of information consumption isnews sharing. Previous studies have reported various personal
predictors of sharing misinformation, such as fear of missing
out, social media fatigue, lack of skills in verifying the reliabilityof information, and information overload on social media. Whennews about a rumor is collectively shared by communities, thedissemination of that message is amplified.
Based on these theoretical findings, we operationalized themeasurement of disinformation through the 12-item Social
Media Disinformation Scale (SMDS-12) instrument. The first
dimension of the SMDS-12 assesses the degree to which
COVID-19 information is consumed from social media. The
second dimension reports users’ judgments about their degrees
of belief, confidence, and trust in information related to
COVID-19 shared on social media. The third dimension
describes how one interacts with such news; in this case, sharing
of information related to COVID-19.
Each dimension is made up of four items that are rated on a
5-point Likert scale, ranging from 1 (strongly disagree) to 5
(strongly agree).
Subsequently, a construct evaluation was carried out by a focus
group made up of seven experts: two professionals in social
networks, both administrator and content creators; twoprofessors in human sciences; two experts in linguistics; andan expert in information and communications technology.
Members of the focus group discussed the components of theitems and were invited to collectively modify and validate ausable version of the instrument.

The COVID-19 Fear Scale
The Arabic-language adapted short version of the COVID-19
Fear Scale from Alyami et al was used [31]. This version has
been translated and adapted into Arabic from the initial versionof Ahorsu et al [32]. The scale assesses fear of COVID-19 using
a one-dimensional factor tool divided into seven items, which
are assessed on a 5-point Likert scale, ranging from 1 (strongly
disagree) to 5 (strongly agree). Concomitant and confirmatory
reliability and validity were examined on a set of Saudi
participants.
The internal consistency of the Arabic version examined using
Cronbach α was satisfactory (α=.88), with strong concurrent
validity indicated by significant and positive correlations with
the Hamilton Anxiety and Depression Scale (r=0.6). Likewise,
examination of the factor structure according to Alyami et al[31] was adequate (comparative fit index [CFI]=0.995; root
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An Arabic-language version of the 10-item Perceived Stress
Scale (PSS-10) by Cohen et al [33], adapted by Almadi et al[34], was used to assess stress. The PSS-10 is divided into two
subscales: the first assesses perceived psychological distress,
while the second measures coping strategy. Scores are obtainedon a 5-point Likert scale, ranging from 0 (never) to 4 (very
often). The reliability and validity of the Arabic version of the
PSS-10 presented a two-factor structure adequate for exploratory
factor analysis, and their Cronbach α coefficients were .74 and.77, respectively. In addition, the test-retest reliability had an
intracorrelation coefficient of 0.90.
For the purpose of our study, we considered only the related
negative factor, which is distress; as such, the coping strategy
was not taken into consideration.

The Arabic Internet Addiction Test
To measure internet addiction, we used the Arabic
language–adapted scale from Hawi [35]. The Arabic version of
the Internet Addiction Test (IAT) is an adapted version of the
instrument originally developed by Young [36]. It consists of
20 items, each of which is scored on a 6-point Likert scale,ranging from 1 (strongly disagree) to 6 (strongly agree). The
scale exhibits a unidimensional construct with robust
psychometric properties: the goodness-of-fit indicesdemonstrated by the confirmatory factor analysis were alladequate (normed fit index [NFI]=0.96; CFI=0.98;
Tucker-Lewis index [TLI]=0.98; goodness-of-fit index [GFI]
and adjusted goodness-of-fit index [AGFI] above therecommended thresholds of 0.90). In particular, the internalconsistency examined using the classical Cronbach α statisticalindex was satisfactory (α=.92).

Statistical Tools
Data normality was tested by skewness and kurtosis tests during
the exploratory phase, while multivariate normality was
examined during the confirmatory phase. Asymmetry values
greater than 7 or kurtosis values greater than 3 were judged to
be non-Gaussian [37] and possessing low psychometricsensitivity [38]. In addition, the Mardia coefficient ofmultivariate normality was calculated during the confirmatory
phase.
The exploratory analysis was carried out by unweighted leastsquares with a direct oblimin rotation. To assess whether thedata were suitable for factor analysis, the sampling adequacy
was examined by the Kaiser-Meyer-Olkin (KMO) statistic.
According to the suggestions of Hair et al [39], the KMO value
must be greater than 0.50 to accept the factorial solution.
Furthermore, the chi-square value of the Bartlett sphericity test,
which should be not significant, was calculated [40]. The factors
were retained for eigenvalues greater than 1 and by examining
the scree plot. In addition, an item was deleted if its factor
loading was less than 0.5 [39-41]. The scale relationships havebeen examined through Pearson correlation tests between the
SMDS-12, the COVID-19 Fear Scale, and the PSS-10.
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Guelmami et alFirst-order confirmatory factor analyses were performed to
examine the factor structure of the instrument. The reliabilityof the instrument was examined by evaluating three internalconsistency indices simultaneously: McDonald ω, Cronbach α,
and Gutmann λ6. Convergent validity and discriminant validity
were assessed, respectively, by calculating the average varianceextracted (AVE) and comparing the square roots of the AVE
values to the correlation coefficients. The relationships between
instrument dimensions, internet addiction, and mental healthparameters were assessed by the Pearson correlation matrix.
Descriptive statistical analyses of the factor structure were
performed with SPSS for Windows, version 26 (IBM Corp),
and Amos software for Windows, version 23 (IBM Corp).
Internal consistency indices were calculated using JASP open
source software, version 0.8.5 (JASP Team).

Results
Exploratory Factor Analysis
Table 2 shows the descriptive statistics, with means and standard
deviations; the skewness and kurtosis coefficients of normality;
and the lambda factor loadings. The coefficients of normality
support the normality of the distributions.
The results indicate that the SMDS-12 was appropriate forproceeding with factor analysis (KMO=0.89; Bartlett test of
sphericity=2988.98; df=66; P<.001). Exploratory factor analysis
indicated a three-factor solution (eigenvalues were 5.45, 2.004,
and 1.39 for the first, second, and third factor, respectively),
explaining up to 73.72% of the total variance, with items having
lambda factor loadings ranging from 0.73 to 0.85. The first
factor explained 45.42% of the total variance, the second factor
explained 16.70% of the variance, and the last factor explained
11.60% of the variance. In addition, the examination of the screeplot confirms the three-factor solution; a distinct change in theslope can be seen in the plot in Multimedia Appendix 1.

Table 2. Exploratory factor analysis of the 12-item Social Media Disinformation Scale (SMDS-12) (n=437).
SMDS-12 item No.

Mean (SD)

Skewness

Kurtosis

Lambda factor loading

1

2.94 (1.25)

0.02

2.94

1.25

2

2.95 (1.21)

0.04

2.95

1.21

3

2.89 (1.17)

0.00

2.89

1.17

4

2.83 (1.18)

0.11

2.83

1.18

5

2.76 (1.09)

0.10

2.76

1.09

6

2.80 (1.13)

0.12

2.80

1.13

7

2.65 (1.11)

0.15

2.65

1.11

8

2.64 (1.04)

0.07

2.64

1.04

9

2.45 (1.12)

0.31

2.45

1.12

10

2.45 (1.12)

0.23

2.45

1.12

11

2.42 (1.11)

0.27

2.42

1.11

12

2.41 (1.06)

0.31

2.41

1.06

Confirmatory Factor Analysis
Before proceeding with the confirmatory factor analysis,
univariate and multivariate tests of normality were performed.
The results indicate that the item distribution followed a

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Gaussian distribution (Table 3), while the Mardia coefficientof multivariate normality indicated a value of 7.98 with a criticalratio of 4.55. These results suggest that multivariate normality
was violated; on the other hand, the Mardia coefficient issensitive to the size of the sample.

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Table 3. Confirmatory factor analysis of the 12-item Social Media Disinformation Scale (SMDS-12) (n=437).
SMDS-12 item No.

Mean (SD)

Skewness

Critical ratio

Kurtosis

Critical ratio

1

3.16 (1.16)

–0.1

–0.5

–0.7

–3.1

2

3.20 (1.12)

–0.2

–1.4

–0.7

–2.9

3

3.12 (1.08)

–0.1

–0.7

–0.6

–2.5

4

3.05 (1.11)

0.0

–0.2

–0.7

–2.8

5

2.88 (1.13)

0.0

–0.3

–0.8

–3.4

6

2.91 (1.13)

0.1

0.6

–0.8

–3.2

7

2.80 (1.08)

0.1

0.7

–0.6

–2.7

8

2.78 (1.05)

0.1

0.7

–0.6

–2.7

9

2.43 (1.14)

0.27

2.85

–0.85

–4.57

10

2.43 (1.13)

0.28

3.02

–0.82

–4.39

11

2.35 (1.11)

0.33

3.58

–0.78

–4.21

12

2.38 (1.10)

0.29

3.11

–0.85

–4.58

Figure 1 shows an overview of the model of the confirmatory
factor analysis for the SMDS-12; following guidelines andrecommendations [40,41], which suggest that a factorial weightgreater than 0.71 can be considered to be excellent, we note that

all items adequately contributed to the pre-established theoreticalconstructs. The confirmatory factor analysis results provided
evidence for the three-factor structure of the SMDS-12. The
factor loadings were acceptable and good (range 0.78 to 0.85).

Figure 1. The final confirmatory factor analysis (CFA) of the 12-item Social Media Disinformation Scale. Factor correlation coefficients are 0.24
(between consumption and sharing), 0.28 (between consumption and confidence), and 0.36 (between confidence and sharing). Factor loadings range
from 0.78 to 0.85. e1 to e12 represent the error variance for each item (I). CFA statistics: χ251=62.5, P<.001; χ2/df=1.2; goodness-of-fit index=0.977;
adjusted goodness-of-fit index=0.965; Tucker-Lewis index=0.995; comparative fit index=0.996; root mean square error of approximation=0.023 (90%CI 0-0.04); standardized root mean residual=0.036.

The chi-square value obtained (χ251=62.5; P<.001) may be dueto the size of the sample. However, the chi-square divided bydegrees of freedom value (χ2/df=1.2) respects the usual
recommended threshold. The values of GFI and AGFI are 0.977
and 0.965, respectively. These two values must be greater thanor equal to 0.90. In addition, the two indices TLI and CFI tend
toward 1 and respect the threshold value of 0.95. Finally, theerror indices—RMSEA=0.023 (90% CI 0-0.04) and
SRMR=0.036—show that the measurement errors are tolerable.
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Reliability
The internal consistency and reliability of the three scale factors
were calculated by the three indices: McDonald ω, Cronbach
α, and Gutmann λ6. Examination of the indices for the threecomponents of the scale yielded values greater than or equal to0.80. This provides evidence for the internal consistency of thescale. Likewise, a good internal consistency was supported by
the Cronbach α indices, which had values of .89, .88, and .88
for the consumption, confidence, and sharing subscales,
respectively, as well as by the Gutmann λ6 coefficients, which
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were greater than or equal to 0.84. In addition, the corrected
item-total correlation was calculated for each latent variable.
The results show that the values were adequate, since they werelocated between 0.72 and 0.78 for the first component (ie,consumption), between 0.70 and 0.76 for the second component(ie, confidence), and between 0.73 and 0.76 for the lastcomponent (ie, sharing). These results confirm that the
instrument has good reliability (Table 4). The internalconsistency of the component is considered good if the value
is equal to or greater than 0.70 [41].

Table 4. Internal consistency of the 12-item Social Media Disinformation Scale (SMDS-12).
Latent variable and SMDS-12 item No.

Corrected item-total correlation

Consumption
1

0.75

2

0.72

3

0.76

4

0.78

Confidence
5

0.76

6

0.73

7

0.70

8

0.74

Sharing
9

0.76

10

0.75

11

0.73

12

0.74

Construct Validity
Convergent Validity
The convergent validity was assessed following theFornell-Larcker criterion [42] by the calculation of the AVE.
AVE values above 0.7 are considered very satisfactory, whereasa level of 0.5 is considered acceptable. The AVE values were0.67 for consumption, 0.64 for confidence, and 0.67 for sharing.

Discriminant Validity
Discriminant validity is ensured when the variance shared by
two different latent variables is less than the variance shared by
the latent variable and its indicators (ie, items). This implies
that the square root of the AVE must be greater than all
correlations between latent variables. The comparison of thesquare roots of the AVE values presented on the diagonal ofthe matrix (Multimedia Appendix 2) with the correlation
coefficients shows that the discriminant validity of the scale
was adequate.

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McDonald ω

Cronbach α

Gutmann λ6

0.89

.89

0.86

0.88

.88

0.85

0.88

.88

0.85

The square roots of the AVE values for consumption,
confidence, and sharing were 0.82, 0.80, and 0.81, respectively.
The comparison of each AVE value with correlation coefficientswith the other constructs shows that they were of higher value.

Relationship Between the Credibility of Disinformationand Mental Health During the COVID-19 Pandemic
The correlation matrix (Table 5) provided positive, significant,and moderate associations between the dimension of
consumption and internet addiction (r=0.22), perceived stress(r=0.16), and the fear of COVID-19 (r=0.21). For the confidence
subscale, a moderate correlation was demonstrated with internet
addiction (r=0.34), while the correlations with perceived stressand fear of COVID-19 were 0.14 and 0.23, respectively. The
sharing dimension resulted in a correlation coefficient 0.19 withinternet addiction and lower coefficient values for perceived
stress (r=0.093) and fear of COVID-19 (r=0.16).

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Table 5. Correlation matrix between the 12-item Social Media Disinformation Scale subscales and mental health parameters related to COVID-19.
Variable

Consumption

Confidence

Sharing

Internet addiction

Perceived stress

Fear of COVID-19

r

1

0.35a

0.27a

0.22a

0.16a

0.21a

P value

—b

<.001

<.001

<.001

<.001

<.001

r

0.35a

1

0.33a

0.34a

0.14a

0.23a

P value

<.001

—

<.001

<.001

<.001

<.001

r

0.27a

0.33a

1

0.19a

0.093c

0.16a

P value

<.001

<.001

—

<.001

.014

<.001

r

0.22a

0.34a

0.19a

1

0.14a

0.21a

P value

<.001

<.001

<.001

—

<.001

<.001

r

0.16a

0.14a

0.093c

0.14a

1

0.33a

P value

<.001

<.001

.014

<.001

—

<.001

r

0.21a

0.23a

0.16a

0.21a

0.33a

1

P value

<.001

<.001

<.001

<.001

<.001

—

Consumption

Confidence

Sharing

Internet addiction

Perceived stress

Fear of COVID-19a

The correlation is significant at a significance level of .01 (two-tailed).

b

Not applicable.

c

The correlation is significant at a significance level of .05 (two-tailed).

Discussion
Principal Findings
The objective of this study was to develop and test thepsychometric properties of the SMDS-12 measurement scale
to assess consumption, confidence, and sharing of information
related to COVID-19 by social media users. The 12-item scale
was initially tested through exploratory factor analysis.
The test supported the three-factor structure; in addition, no
items were removed from the measurement scale. Subsequently,
confirmatory factor analysis confirmed the robustness of themeasurement tool. The results also supported the constructvalidity of the scale by its convergent and discriminant validity,
both of which were adequate. The reliability of the instrument
examined by means of three internal consistency indices andthe corrected item-total correlation demonstrated that the three
dimensions of the instrument are reliable.
The correlation between the three dimensions of the instrumentwith the internet addiction scale and mental health factors
showed positive associations, which lay in a range from small,for the relationship of the sharing dimension with stress, tomoderate, for the association of the other two factors withinternet addiction, perceived stress, and fear of COVID-19.

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Regarding the links between the consumption of disinformationand internet addiction, similar results have been reported byPriego-Parra et al [43]. The authors found that internet addiction
and overexposure to rapidly spreading disinformation areassociated with anxiety and depression. In addition, internet
addiction resulting in obtaining information about COVID-19
has increased stress and anxiety levels.
Furthermore, in other studies of COVID-19 related todisinformation spread on social media [44-48], aimed at
identifying the prevalence and factors associated with theconcept, disinformation was shown to be linked to demographic
variables, such as age, gender, and academic level. Moreover,consistent with our findings, misinformation beliefs weresignificantly associated with fear of COVID-19 in addition toother variables, such as lower levels of health education, trustin government, and confidence in science.
During the COVID-19 pandemic, internet addiction and the useof social media in particular have increased significantly [44-48].
Also, time spent on the internet was associated with sharing
misinformation related to the context of the illness [44-49].
Moreover, some studies [50-52] examined the association
between social media and mental health linked to the COVID-19
pandemic. The results showed that social media use was linked
to depression, and excessive social media use led to mental
health issues.
JMIR Form Res 2021 | vol. 5 | iss. 6 | e27280 | p. 8
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JMIR FORMATIVE RESEARCH
Our findings are also in line with a pilot study by Zhong et al[20], which examined the possible association between social
media use and the mental health toll linked to the COVID-19
pandemic in China. This study found that social media use waslinked to both depression and secondary trauma, which also
predicted a change in health behavior.
On the contrary, in a cross-sectional survey by Agley and Xiao
[14], COVID-19–related information sharing behaviors wereclustered, and four belief profiles emerged from the analysis.
A total of 70% of the subjects surveyed believed in scientifically
accepted theories (ie, zoonotic origin of the outbreak) and notin conspiratorial theories. Other profiles disagreed with thezoonotic explanation, and instead believed in misinformation,although to varying degrees. Briefly, trust in science was astrong and significant predictor of news sharing behavior.
Regarding the acquisition of disinformation and the subsequent
sharing of this information, Chua and Banerjee [53] showedthat gullible users had a greater propensity to share healthrumors online. For that reason, Li and Sakamoto [54] suggested
that exposing individuals to collective opinion measures may
reduce the tendency to share false health messages. To explain
the mechanism, the theory of cultural attraction can be utilized.
Indeed, this theory postulates that the spread of rumors resultsfrom psychological pull factors. The reasons for the propagationof this false information are mainly due to the recruitment ofcognitive pull factors that are likely to increase social
interactions [55]. Indeed, on these platforms, content creators
produce their works with a strong psychological appeal toencourage users to react to them and increase their audiences.
This highlights the need for much more research into thecultural, psychological, and social characteristics of users who

Guelmami et al
trust and disseminate this content on social media. In particular,
it is crucial to better understand the roles of thinking and belief
systems. For example, they should also be explored in empiricalstudies, in particular, relying on mathematical models based on
big data and artificial intelligence. This would be of paramountimportance, given the potential impact of COVID-19–related
misinformation on the public health measures implemented tocurb the pandemic [56-61].

Conclusions and Recommendations
The results of this study provided a first demonstration forassessing behaviors related to use, consumption, and sharing
of information related to COVID-19 on social media. The
SMDS-12 exhibited acceptable psychometric properties andcan be utilized in Tunisia and other Arabic countries to explore
user engagement with social media, credibility of information,and interaction with information in terms of sharing.
Furthermore, the instrument could be translated, culturally
validated, and utilized by other scholars from other countries.

Limitations of the Study
The main limitation of this study is the lack of concurrent
validity testing of the instrument with similar instruments. In
addition, the instrument has only been tested on a single
population living in a single country. Also, the study wasobservational and not interventional; it did not investigate waysthat could reduce credibility and counteract the sharing ofrumors and misinformation. Another limitation relates to thestudy population, as the data were collected from a group of
Tunisian social media users. Although we have confirmed thevalidity and reliability of the measurement instrument for theseparticipants, a certain specificity linked to the cultural context
does not allow for the generalization of the results.

Acknowledgments
This research was funded by Canada’s International Development Research Centre (IDRC) (grant 109559-001).

Conflicts of Interest
None declared.

Multimedia Appendix 1
Scree plot for the principal component analysis of the 12-item Social Media Disinformation Scale (SMDS-12).
[PNG File , 16 KB-Multimedia Appendix 1]

Multimedia Appendix 2
Discriminant validity of the 12-item Social Media Disinformation Scale (SMDS-12) subscales.
[DOCX File , 13 KB-Multimedia Appendix 2]

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Abbreviations
AGFI: adjusted goodness-of-fit index
AVE: average variance extracted
CFI: comparative fit index
GFI: goodness-of-fit index
IAT: Internet Addiction Test
IDRC: International Development Research Centre
KMO: Kaiser-Meyer-Olkin
NFI: normed fit index
NPI: nonpharmaceutical intervention
PSS-10: 10-item Perceived Stress Scale
RMSEA: root mean square error of approximation
SMDS-12: 12-item Social Media Disinformation Scale
SRMR: standardized root mean residual
TLI: Tucker-Lewis index
UNESCO: United Nations Educational, Scientific and Cultural Organization

Edited by G Eysenbach; submitted 19.01.21; peer-reviewed by H Dai, R Treskes, SL Lee; comments to author 16.02.21; revised version
received 03.03.21; accepted 03.05.21; published 09.06.21
Please cite as:
Guelmami N, Ben Khalifa M, Chalghaf N, Kong JD, Amayra T, Wu J, Azaiez F, Bragazzi NL
Development of the 12-Item Social Media Disinformation Scale and its Association With Social Media Addiction and Mental Health
Related to COVID-19 in Tunisia: Survey-Based Pilot Case StudyJMIR Form Res 2021;5(6):e27280
URL: https://formative.jmir.org/2021/6/e27280
doi: 10.2196/27280
PMID: 34021742

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Nicola Luigi Bragazzi. Originally published in JMIR Formative Research (https://formative.jmir.org), 09.06.2021. This is an
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