Information overload and misinformation sharing behaviour of social media users: Testing the moderating role of cognitive ability

Abstract

Sharing of misinformation on social media platforms is a global concern, with research offering little insight into the motives behind such sharing. Drawing from the cognitive load theory and literature on cognitive ability, we developed and tested a research model hypothesising why people share misinformation. We also tested the moderating role of cognitive ability. We obtained data from 385 social media users in Nigeria using a chain referral technique with an online questionnaire as the instrument for data collection. Our findings suggest that information overload and social media fatigue are strong predictors of misinformation sharing. Information stress also contributed to misinformation sharing behaviour. Furthermore, cognitive ability moderated and weakened the effect information strain and information overload have on misinformation sharing in such a way that this effect is more pronounced among those with low cognitive ability. This indicates that those with low cognitive ability have a higher tendency to share misinformation. However, cognitive ability had no effect on the effect social media fatigue has on misinformation sharing behaviour. The study concluded with some theoretical and practical implications.

Keywords

Cognitive ability information strain information overload misinformation Nigeria social media fatigue

1. Introduction and background

Social media have become one of the defining technologies of our time [1]. It is a quick and cost-efficient tool for sharing news and updates with a large audience [2]. Thus, it has drastically transformed how information is exchanged and assimilated among people [3]. The benefits of social media were particularly evident during the COVID-19 crisis and its pandemic restrictions [4], as social media was the predominant method of maintaining personal connections [5], and accessing information [6]. Nearly two-thirds of the world receives its news and information from social media [7]. Consequently, social networking sites have become a valuable tool for disseminating false information, such as phoney reviews, advertisements, political remarks and satire [8]. Social media afford users the great convenience of sharing information with just a click, usually without the scrutiny of the information content, leading to misinformation circulation [9].

According to some studies, misinformation seems to be more prevalent and widely disseminated through social networking sites than through traditional media [10,11]. In this study, misinformation (widely regarded or interchangeably with fake news) refers to any content, such as news, information, articles, messages and so forth, that is made up to deceive people into thinking it is genuine and, as such, prompting people to share it further [12]. The rapidly evolving situation of the abundance of information on social media resulting in information overload has made it increasingly difficult for ordinary people to differentiate between misinformation, fake news and facts [13]. As social media amplifies the spread of news and people read news through links shared on social media [14], understanding the role that social media plays in the sharing of misinformation is essential [15].

While misinformation is gaining scholarly attention [16 –20], the motivations for this detrimental behaviour remain mostly under-researched [9,21]. With regards to studies on misinformation, a recent study examined the impact of misinformation and social noise on the original intended message of BLM using data from the Twitter hashtag ‘BLM’ [16]. The study’s findings showed that there was a significant amount of false information and social noise, which was done to influence, deceive and dilute the BLM’s original intended message. Another study looked at the concerns surrounding three Twitter hashtags (#masks, #maskup and #maskoff) to understand better how social noise can lead to unintended misinformation [17]. The study revealed the prevalence of social noise, leading to the unintended spread of misinformation on Twitter. A related study also found a lot of misinformation pertaining to COVID-19 masks on Twitter, and due to this misinformation, many people did not believe in the effectiveness of masks [19]. Furthermore, it has been hypothesised that accepting conspiracy theories that were circulated in mainstream and social media early in the COVID-19 pandemic in the US would be negatively related to the uptake of preventive behaviours and also of vaccination when a vaccine becomes available [20].

As evident in the aforementioned studies, there are limited theoretical frameworks for understanding misinformation sharing (knowingly or unknowingly) [21]. The few studies that had a theoretical framework mostly adopted the uses and gratifications theory and viewed misinformation sharing as behaviour motivated by fulfilling certain needs, such as socialisation, self-promotion, passing time, entertainment and altruism [9,15,22,23]. Although the uses and gratifications theory has illuminated the motives of people’s information sharing on social media, these motives do not capture the fact that people could share misinformation, mainly because they have limited processing capability due to the excessive amount of rapidly updated information found on social media [24]. By implication, cognitive overload assumes that when human brains are overloaded by information, they have limited processing capability, resulting in limited authentication [6,21], which in turn, leads to misinformation sharing.

Another limitation found in past research on misinformation sharing behaviour is that only a few studies [16,21] have extended to explore solutions or introduced a moderator to reduce the spread of misinformation sharing. Evidence on the intervening mechanisms in users’ misinformation sharing processes is rare [1,25]. Most of the previous prevention literature focused on technical specifications, such as algorithm-based debunking and correcting of misinformation [26,27], rather than the consumer as the misinformation sharer. Besides the technical solutions, studies have now started to examine cognitive factors that may impact people’s susceptibility to fake news [28 –31]. In this view, in this current study, we introduced cognitive ability as a moderator. Cognitive ability refers to the capability to execute higher cognitive processes of reasoning, remembering, understanding and problem solving [25]. There are reasons to believe that the cognitive ability of online users (an indication of analytical reasoning and general intelligence) may affect how users perceive misinformation [32]. Yet, there is a lack of evidence exploring the role of cognitive ability in moderating the relationship between social media fatigue, information overload and information strain with misinformation sharing. This study is one of the first to realise the moderating role of cognitive ability in the relationship between cognitive overload and misinformation sharing behaviour. This contributes to recent research that explored the role of resilience as a moderator between information strain, information overload and fake news sharing on COVID-19 [26]. Thus, we argue that those with higher cognitive ability might share misinformation less compared with those with low cognitive ability, despite being faced with the issue of information abundance on social media.

Finally, studies on misinformation sharing have largely focused on the Western part of the world, with less focus on developing countries that are witnessing an increasing amount of false information circulation [12]. Keeping this in mind, this current study focuses on Nigeria to show an alternative outcome regarding misinformation sharing. Thus, using the cognitive load theory (CLT), we explored the cognitive load variables (social media fatigue, information overload and information strain) related to misinformation sharing to support existing literature. Furthermore, we drew from past studies on cognitive ability to test its moderating role in curtailing misinformation circulation. Thus, the study is guided by the following research questions:

RQ1. Does social media fatigue, information overload and information strain predict misinformation sharing among social media users in Nigeria?

RQ2. Does cognitive ability moderate the relationship between social media fatigue, information overload, information strain and misinformation sharing among social media users in Nigeria?

2. Research model and hypotheses development

It has been shown that the abundance of information on social media contributes to fake news circulation [6,15]. Drawing from this postulation, we adopted the CLT to explain misinformation sharing behaviour. Specifically, we examined the role social media fatigue, information overload, and information strain have on misinformation sharing among social media users in Nigeria. We then extended to test the moderating role of cognitive ability.

2.1. CLT

Cognitive load in this study signifies the amount of information a functional memory can hold in a given period. It has been shown that the human working memory has a limited capacity, which could be overloaded if presented with too much information [33]. By implication, the human brain can only process a small amount of information at a time and as such, a large amount of information makes learning and acquiring knowledge difficult for humans [33]. Evidence has shown that the CLT has been predominantly used in instructional science and learning to understand the human ability to obtain knowledge in a given situation [33,34]. However, recent years have witnessed its usage in an online environment such as social media, where information is abundant, which could be too much for the human cognitive circuit to process, resulting in cognitive load [6,15].

Drawing from the CLT, information overload has been proven to decrease social trust among people [35], and humans overloaded with information are likely to make careless decisions as they are unable to process surrounding information and experience less self-control [35]. Misinformation sharing is one of the careless decisions associated with cognitive overload [6,15]. This indicates that when people are overwhelmed with information from social media, they could share certain information without completely processing it due to the influx of additional information. Furthermore, exposure to information on social media has been shown to lead to information strain, which in turn increases the probability of sharing fake news [26]. In other words, once users feel stressed from a mass of information, their motivation to make sense of new information is reduced, and they recoil from exerting extra effort to verify it [36]. This suggests that users with greater informational strain are more likely to share misinformation.

It has also been shown that when humans are overloaded by information, it leads to fatigue [36], and once humans are fatigued, it reduces their ability to verify the information they encounter [6]. A similar outcome has also been found, which suggests cognitive overload is responsible for social media fatigue [37]. Thus, a link between social media fatigue and misinformation sharing has been established in a prior study [21]. Relating to this study, it could be argued that when individuals are overloaded with information, they are less likely to go into extra trouble verifying information sources, and this could lead to the consumption and further proliferation of misinformation. Recent research revealed a positive relationship between information-induced stress and fake news sharing [24]. Drawing from these streams of ideas, we proposed that:

H1. Social media fatigue positively predicts misinformation sharing behaviour.

H2. Information overload positively predicts misinformation sharing behaviour.

H3. Information strain positively predicts misinformation sharing behaviour.

2.2. Cognitive ability as a moderator of misinformation

In this study, cognitive ability refers to the capability to execute higher cognitive processes of reasoning, remembering, understanding and problem-solving. According to new research, cognitive capacity is important in engaging with online misinformation [38]. When confronted with false information, people with greater cognitive abilities are known to make sound decisions [38]. Pennycook and Rand [32] found that people with stronger cognitive skills are better able to distinguish between facts and lies. In other words, people with stronger cognitive skills are better at distinguishing truth from misinformation and modifying their attitudes when confronted with misinformation [16,25]. Based on these arguments, it is expected that more analytical thinking will support both, a better discernment of manipulated information from reality, and an effective decision-making process resulting in an avoidance of misinformation [29].

Previous evidence suggests that higher cognitive ability allows for more effective processing of available information and controlled decision-making [38,39]. Those who are more motivated to engage in cognitive tasks systematically analyse information, increasing their chances of detecting deception [40]. Lower cognitive capacity, on the other hand, is linked to decreased deception detection abilities [41]. Better social skills are also linked to cognitive capacity. Individuals with higher cognitive abilities are better at regulating social impressions because they have a broader awareness of social settings [42]. In line with this, a recent study finds that cognitive ability is negatively associated with deepfakes sharing in both the United States and Singapore, suggesting that individuals with high cognitive skills are less likely to share deepfakes [43].

We, thus, argue that even though they may be overwhelmed by the abundance of information available on social media, it is expected that those with higher cognitive ability, aided by sufficient information processing capability, more efficient decision-making ability, and a better understanding of social settings, will be less likely to share misinformation. On the other hand, those with low cognitive ability may be easily affected by cognitive overload, leading to greater misinformation sharing. Therefore, we proposed that:

H4. Cognitive ability will moderate the relationship between social media fatigue and misinformation sharing in such a way that the effect will be stronger for those with low cognitive ability.

H5. Cognitive ability will moderate the relationship between information overload and misinformation sharing in such a way that the effect will be stronger for those with low cognitive ability.

H6. Cognitive ability will moderate the relationship between information strain and misinformation sharing in such a way that the effect will be stronger for those with low cognitive ability.

Figure 1 shows the model developed in this current research.

Figure 1.

A model depicting the moderating effect of cognitive ability.

3. Method

3.1. Research design and sample of the study

A survey research design was used to conduct this study. Furthermore, an online questionnaire was used as the instrument for data collection. The respondents in this study consisted of social media users from Nigeria aged 18 and above. Our estimated sample size was 385 respondents, which was calculated based on an estimated number of social media users in Nigeria (29.3 million) [44] using the thresholds of 95% confidence level and 5% confidence interval.

3.2. Research instrument and sampling technique

The instrument for data gathering was an online questionnaire. The questionnaire was broken into two sections: demographics and the main portion that assessed respondents’ sharing behaviour. There was a screening question on the first page of the form with two criteria: being over the age of 18 and being an active social media user. To elicit responses, we posted the link (Survey Monkey) to our survey on various social media platforms, such as Facebook, WhatsApp, Twitter and so on. We adopted the respondent-driven sampling (RDS) chain referral technique to select the respondents for the study. We used this technique to allow the inclusion of only social media users. We executed the RDS technique by sampling earlier participants, referred to as ‘seeds’, who possessed the characteristics of interest. In this study, social media users were the seeds. The initial seeds were instructed to contact and recruit others from their existing network. Progressively, successive sets of participants then recruited people from their social networks for participation. This process continued until we arrived at the desired number (N = 385) of respondents. The data were gathered between October 2021 and December 2021. The characteristics of the sample used in this study are shown in Table 1.

Table 1.

Profile of respondents.

Characteristics	Frequency	Percentage (%)
Gender
Male	210	55
Female	175	44
Age
18–24	100	25.9
25–34	129	33.5
35–44	39	10.1
45–54	87	22.6
55–64	20	5.2
65 and above	10	2.6
Working status
Employed full-time	80	20.8
Employed part-time	43	11.1
Student	149	38.7
Retired	15	3.9
Unemployed	58	15.1
Others	40	10.4
Education
High school	10	2.6
Diploma	110	28.6
Bachelor’s degree	139	36
Master’s degree	72	18.7
PhD	43	11.2
Others	11	2.9
Frequently used SNS
WhatsApp	116	30.1
Facebook	155	40.3
Twitter	39	10.1
Instagram	56	14.5
Others	19	4.9
Time
10–12 h	214	55.6
7–9 h	65	16.9
4–6 h	86	22.3
1–3 h	20	5.2
Ethnicity
Igbo (Eastern Nigeria)	77	20
Yoruba (Western Nigeria)	95	24.7
Hausa (Northern Nigeria)	110	28.6
Others (Other regions)	103	26.7

3.3. Measures

Information overload was adapted from previously validated fake news sharing studies [22,25]. Some of the items under this section are: I am often distracted by the excessive amount of information on social media. I find that I am overwhelmed by the amount of information that I process daily from social media. I receive too much information on social media to form a coherent picture of what’s happening. The responses on a 5-point scale (1 = strongly disagree and 5 = strongly agreed) were combined and averaged (M = 3.83; SD = 1.12, α = 0.76).

Information strain was measured using 4 items from the study by Bermes [26]. The items are: I am forced to change habits to adapt to new developments regarding information on social media. I have to sacrifice my time to keep up with new updates on social media. I feel that my personal life is being invaded by social media communication. The information on social media reduces my life satisfaction. The responses on a 5-point scale (1 = strongly disagree and 5 = strongly agreed) were combined and averaged (M = 4.13; SD = 1.22, α = 0.86).

Social media fatigue was measured using the 4 items adapted from the study of Islam et al. [15]. Examples of the items are: I find it difficult to relax after continually using social media. After a session of using social media, I feel really fatigued. The responses on a 5-point scale (1 = strongly disagree and 5 = strongly agreed) were combined and averaged (M = 3.63; SD = 1.22, α = 0.86).

Cognitive ability was assessed by a 10-question word sum test (M = 4.24, SD = 2.65, range = 10, α = 0.81). The answers to 10 questions were added to create the cognitive ability scale. This test is commonly used as a proxy for general intelligence and cognitive capability [25].

3.4. Validity and reliability

To be sure that our instrument was valid and reliable. We followed certain steps. First, we carried out a content validity test using three communication specialists. Two of the specialists are from Taraba State University, while the third is from the University of Nigeria. We ran a pilot test with (n = 30) volunteers to look for further anomalies or errors. Based on the results of the pilot test and expert observations, we reworded some of our items, resulting in a final clean copy that was used for collecting data. Second, we tested reliability after collecting the data. This reliability was tested using internal consistency reliability, in which we ran Cronbach’s alpha test on our variables and realised that all the variables were above the threshold of 0.78. We also realised that the composite reliability values (CR) were all above the values of 0.70. Third, we tested convergent and discriminant validity. With regard to convergent validity, the average variance extracted (AVE) and factor loadings were all above the recommended threshold of 0.50 and 0.70, respectively, while discriminant validity values (Heterotrait–Monotrait ratio of correlations (HTMT), which were less than 0.85, established no issues of discriminant validity. Thus, our study did not suffer from any issues related to validity and reliability [45].

3.5. Data analysis

For the descriptive aspect of this study, we used SPSS version 25. To test for the validity and reliability as well as the proposed relationships among variables, SmartPLS 3.2.6 was used. Furthermore, before the actual analysis, common method bias (CMB) and variance inflation factor (VIF) values were checked. We discovered that a single variable shared not more than 11.4% of the entire variance, which is less than the 50% criterion, indicating that the CMB was not an issue in this study [46]. In addition, we realised that all the inner VIFs of the structural model were below 3.3.

4. Results

4.1. Direct effect results

As shown in Table 2, all three hypotheses (H1–H3) were supported. Specifically, social media fatigue, information overload and information strain all positively predicted misinformation sharing. Furthermore, we realised the effect sizes (f²) for the relationships in this study were from no effect to medium effect, respectively [47]. Moreover, the Q² was above zero indicating a good predictive relevance [48]. The R² coefficient of determination shows 35% of the variance in people’s misinformation sharing behaviour, which could be explained by the three significant factors: social media fatigue, information strain and information overload. Using the guidelines of Cohen [47], R² of 35% is adequate.

Table 2.

Structural model direct and moderating effect results.

No	Hypothesised relationship	β	t values	Q2	f2	Decision
H1	SMF → MS	0.339	2.854**		0.182	Supported
H2	IOV → MS	0.512	5.866***		0.213	Supported
H3	IS → MS	0.217	2.299*		0.077	Supported
H4	SMF*CA → MS	0.031	1.113		0.001	Not supported
H5	IOV*CA → MS	−0.398	3.484***	0.289	0.159	Supported
H6	IS*CA → MS	−0.213	2.189**		0.057	Supported

SMF: social media fatigue; IOV: information overload; IS: information strain; CA: cognitive ability; MS: misinformation sharing.

Significant at p < 0.05, ** at p < 0.01, and *** at p < 0.001.

4.2. The moderation effects

We used the two-stage approach to analyse moderation because it is versatile and should generally be given preference for creating the interaction term [49]. It has been suggested that when a researcher attempts to realise if a moderator has a significant effect on a relationship, the two-stage approach is more suitable [50]. Hair et al. [49] recommended the two-stage approach because of its versatility in creating interaction terms. As shown in Table 1, cognitive ability did not moderate the relationship between social media fatigue and misinformation sharing (β = 0.031, p > 0.05). Thus, H4 was not supported. However, cognitive ability moderated the relationship between information overload and misinformation sharing (β = –0.398, p < 0.001) as well as between information strain and misinformation sharing (β = –0.213, p < 0.01), thus supporting H5 and H6, respectively. To properly interpret this result, an interaction plot was used. As shown in Figures 2 and 3, the relationship between information overload and misinformation sharing, as well as between information strain and misinformation sharing, was stronger among those with low cognitive ability compared with those with higher cognitive ability.

Figure 2.

Moderating role of CA in the relationship between IOV and MIS.

Figure 3.

Moderating role of CA in the relationship between IOV and MIS.

5. Discussion of findings

This study was set out to examine how cognitive load factors, such as social media fatigue, information strain and information overload influence the misinformation sharing behaviour of social media users in Nigeria. The study further tested the moderating role of cognitive ability. We found information overload to be the most significant factor that predicts misinformation sharing among Nigerian social media users. It could be that when Nigerian social media users are overloaded with information, they are less likely to go to the extra trouble of verifying information sources, and this leads to the consumption and further proliferation of misinformation [51].

According to our findings, social media fatigue predicts misinformation sharing. This factor was the second most prominent factor that influenced the misinformation sharing behaviour of Nigerians. It has been shown that when humans are overloaded with information, it leads to fatigue [36], and once humans are exhausted, it reduces their ability to verify the information they encounter [6]. Thus, we also found information strain to contribute to misinformation sharing behaviour. This was the least influential factor in predicting the misinformation sharing behaviour of Nigerians. This outcome supports the assertion that suggests that exposure to information on social media has been shown to lead to information strain, which in turn increases the probability of sharing fake news [26]. We feel that once users feel stressed by a mass of information, their motivation to make sense of new information is reduced, and they recoil from exerting extra effort to verify it [36].

In this study, we also tested the moderating role of cognitive ability and found that when users are already fatigued due to social media usage and an abundance of information, their cognitive ability even though high might not help to combat misinformation. Thus, cognitive ability did not moderate the positive relationship between social media fatigue and misinformation sharing. Nevertheless, we found that higher cognitive ability moderated the effect information overload and information strain had on misinformation sharing. This implies that Nigerian social media users who have higher cognitive ability, despite being exposed to a lot of information, might still have better resistance to misinformation sharing compared with those who have low cognitive ability. We, thus, argue that even amid the abundance of information exposed on social media as well as the strain attached to information abundance, those with higher cognitive ability will be better off in filtering information, leading to the sharing of accurate information. This study substantiates previous evidence which suggests that higher cognitive ability allows for more effective processing of available information and controlled decision-making [38,39]. It also supports recent study findings which showed that individuals with high cognitive skills are less likely to share deepfakes [43].

6. Conclusion

We conclude that information overload, information strain and social media fatigue influence the misinformation sharing behaviour of social media users in Nigeria. Furthermore, cognitive ability moderated the influence information strain and information overload have on misinformation sharing in such a way that this influence is more pronounced among those with low cognitive ability. This denotes that those with low cognitive ability share more misinformation than those with higher cognitive ability. It is also our conclusion that cognitive ability has no effect on the influence social media fatigue has on misinformation sharing behaviour, suggesting that even if a person has a higher cognitive ability when he or she is fatigued, there is a high tendency of sharing misinformation. This outcome has some theoretical and practical implications.

6.1. Theoretical implications

Our research adds to the current literature on the factors that impact misinformation spreading behaviour among social media users, particularly in Nigeria. We contribute to prior literature by identifying that social media fatigue, information strain and information overload predict misinformation sharing behaviour. With all of these discoveries, we add to the body of knowledge on why people spread misleading news [9,51 –53]. Prior knowledge suggests online information trust is the most significant factor that influences users’ false news distribution [21,51]. We contribute by demonstrating that the most significant predictor of misinformation dissemination is cognitive overload. By implication, the overabundance of information users get exposed to on social media leads to information overload, which in turn leads them to share misinformation since they might not be able to easily make sense of so many messages at a time.

Another interesting theoretical contribution of this study is the non-significant moderating effect of cognitive ability in the relationship between social media fatigue and misinformation sharing. This implies that when users are fatigued, their cognitive ability functions less. Thus, cognitive ability is more effective when a user has not reached a fatigued stage. This novel outcome adds to the literature which solely argues that individuals with high cognitive skills are less likely to share fake news [37,38,42].

Past evidence has shown that the human brain can only process a small amount of information at a time, and as such, a large amount of information makes learning and acquiring knowledge difficult for humans [33]. Nevertheless, this study has established that higher cognitive ability improves discernment and reduces misinformation sharing despite the exposure to the abundance of information on social media.

6.2. Practical implications

We propose intervention methods based on our findings that urge individuals to be sceptical of information they read on social media. First, with regards to social media fatigue, we encourage social media users to limit social media usage to curtail fatigue. When they feel tired or stressed, they should focus on other things rather than the continuous usage of social media. Second, in respect of the positive link between information overload, information strain and misinformation sharing as well as the moderating role of cognitive ability, we call on relevant authorities to improve the cognitive capabilities of people in Nigeria to enable the fight against misinformation sharing. Seminars, workshops and other training could be established to improve people’s cognitive strength. Cognitive ability training should start from primary school to enable the future generation to decipher between real and fake news amid the abundance of information exposed on social media. Cognitive capacity is important in engaging with online misinformation, and when confronted with false information, people with greater cognitive abilities are known to make sound decisions [38]. No doubt, people with stronger cognitive skills are better able to distinguish between facts and lies [32].

6.3. Limitations and future research suggestions

The sample for this study came from only one country (Nigeria). Nonetheless, because misinformation is now a global issue, the findings might be applied to other nations. Furthermore, this research replies to a recent proposal that authors should go beyond the United States and the United Kingdom when investigating lies and misinformation [12]. Academics in the future should seek out social media users from other countries. Furthermore, a cross-national comparison of fake news transmission would yield an intriguing conclusion that might aid policymakers.

Another restriction of our research is that we can only draw conclusions based on the variables we have discovered and investigated. Peer pressure, for example, is another issue to consider. As a result, future research on the spread of falsehoods on social media may need to include more variables. A mediating variable was not included in our model; including one would have resulted in a more sophisticated model, which would have increased our knowledge of false news propagation. Future scholars may be able to fix this problem.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Oberiri Destiny Apuke

Celestine Verlumun Gever

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