Bibliometric Review of Research on Misinformation: Reflective Analysis on the Future of Communication

Abstract

The communication channels driving misinformation often carry the misinformation to elicit responses, which can turn into big threats. Users’ extensive availability and convenience in creating and facilitating user-provided content in online social media enable people with common interests, worldviews and narratives to come together and spread information/misinformation. This research aims to create an intellectual structure through reflective analysis that will help us identify the existing communication pattern that led to misinformation during recent times, also considering the pandemic times. This study analyses and reviews the emerging literature on misinformation using a bibliometric analysis approach. A total of 1,363 papers published from January 2008 to June 2022 from the Scopus database were extracted for analysis in VOS viewer, revealing 10 clusters derived from the keyword, finally coming under four broad themes.

The findings revealed that the earlier studies in this area were more expressive and theoretical, and there is a need to provide simple and scientific solutions to counter the misinformation. Considering all possible adversities, this article draws concrete conclusions by offering directions and propositions to create more comprehensive systems and policies to drive a conclusive future.

Keywords

Misinformation social media media media literacy

Introduction

Misinformation or rumour is a testimony whose true value is unverifiable (Qazvinian et al., 2011). Misinformation proliferates many people at a formidable pace and is generally supported by those who spread the information casually and without intent (Chen et al., 2015). The spread of a vast amount of misinformation causes public distress and brings the public’s physical and psychological health peril and a genuine threat to governance and adherence to social order (Luo et al., 2021). With the advent of Web 2.0, social media has become a critical medium for the diffusion of information. Due to the networks’ collective attribute and the users’ limited onus, the media gets utilised to transmit rumours and misinformation (Kumar & Geethakumari, 2013). Misinformation resembles one more form of misleading information, termed fake news. Fake news is ‘intentionally and verifiably false information’ to manipulate people’s perceptions of real facts, events and statements. The term misinformation overlaps closely with Fake news in terms of the phenomenon that both are false information; however, the former is spread regardless of whether there is an intent to mislead, and the latter is purposefully crafted and fabricated that mimics the form of mainstream news (Olan et al., 2022; Tandoc et al., 2017).

In ancient history, around 2,000 years ago in Rome, Octavian, the adopted son of the great general Julius Caesar, and Mark Anthony, one of Caesar’s most trusted commanders during the civil war, hurled fake news against Mark Anthony as a warfare strategy. The proclamation was that Anthony, who had an affair with Cleopatra, the Egyptian Queen, did not have respect and faithfulness towards traditional Roman values and was unfit to hold an office as he was an alcoholic. To spread this fake news, Octavian used poetry and slogans as a tool of communication (MacDonald, 2017). After the inception of the printing press in the fifteenth century, the dissemination of fake news was faster than ever before. In the eighteenth century, the printing press became a source to spread fake news about the king of Great Britain, George II, the king of Great Britain and Ireland at that time, claiming he was strong and capable of governing Great Britain. To counter the fake news or misinformation spread by the king himself, the rebel group too started utilizing fake news as a tool to defame the king (Steffensen, 2018).

In communication theories, the history goes down to the magic bullet theory or the Hypodermic Needle Theory. A classic example of this theory’s effect is a piece of fake news by Orson Wells and his theatre group about the invasion of aliens in the GroversMilll, New Jersey city. The news terrorised the citizens and led to severe consequences. This piece of fake news also proved via this theory that mass media could effectively manipulate the thought patterns and mindset of the passive recipients of mass media (Michelle, 2018; Nwabueze & Okonkwo, 2018).

The misinformation and its dissemination were also seen at their optimum level, even during the pre-social media era, wherein cases of misinformation were at their peak. The sudden emergence of the Monkey Man in the capital city of India was one of the most significant cases of misinformation, where the panic among the people across the nation was huge. The story ran around and said that a creature that looked like a monkey attacked those people who slept on the roof and created havoc and death, too (Verma & Srivastav, 2003). Later, the in-depth reporting on this news admits that most victims were adult males from low socio-economic backgrounds and educational levels, and the incident mainly occurred during the night and the power failure. The media picked up the story, arousing panic among the masses in a shorter period (Harding, 2001). The media’s role in spreading misinformation and rumour created a mass hysteria where the specialists refer to this as a psychogenic illness, a condition that begins in the mind rather than the body (Goldstein & Hall, 2015). This condition is commonly seen in otherwise healthy, isolated or underdeveloped social groups, physically close and emotionally dependent (Mattoo et al., 2002). Cases of Mass Hysteria are usually linked to the media as media plays a pivotal role in disseminating a message to the masses; hence, the outcome significantly depends on them (Cohut, 2018; Singh et al., 2005). In modern times, misinformation is very different from the traditional form. At present, it spreads faster and has a higher magnitude of influence. Recently, the spread of misinformation has become more dynamic and influential. Social media has played the most significant part in spreading misinformation (Doer et al., 2012). A study conducted at the University of Alberta, Canada, on 9,657 pieces of misinformation in 138 countries between January 2020 and March 2021 revealed that social media is the major producer of misinformation, accounting for almost 85% of it. The misinformation spread during the COVID-19 pandemic was hugely from internet-based sources, making up 90% of misinformation on COVID-19. Facebook alone produced 67% of misinformation among all social media platforms (Banerjee, 2021; McMaster, 2021).

India became the biggest producer of misinformation on the COVID-19 pandemic-related news, followed by the USA and Brazil. The misinformation during the pandemic included the magnetic properties of the COVID-19 vaccine in human bodies, lemon juice taken from the nose would kill coronavirus, keeping cloves, cardamom and camphor in the pocket to cure COVID-19, and many more (Anand, 2022). The extent of misinformation during COVID-19 led WHO to come out with a term called ‘Infodemic’. According to the World Health Organisation, an infodemic is an access to information in the digital and physical environment during a disease outbreak (World Health Organization, 2021). This particular definition was given by the WHO when the world was facing the heat of COVID-19 and social media was used to spread misinformation among the public (Alam & Chu, 2020).

This study reflects all the studies to understand the communication pattern leading to misinformation before and during the social media era. The study will dig in-depth to understand this domain’s work pattern and recommended propositions to aid future research. The purpose is to create an intellectual structure through reflective analysis that will help us identify the existing communication pattern that led to misinformation during recent times, also considering the pandemic times.

The research paper follows the pattern of format—The first section introduces the work and the area. Then the article enlightens on the methodology in detail. Further, the article presents its research findings, elaborately revealing the knowledge structure (identifying themes). Finally, the article elaborates on the significance, future proposition, discussion and conclusion. Finally, the limitations of the study are listed.

Methodology

This study section will focus on the methodology adopted to analyse the literature and the methods to present the finding. A bibliometric approach is considered for the study, where the core focus is on the systematic literature review and deriving themes from the relevant keyword searches. The bibliometric approach is a mathematical and statistical analysis of the existing literature of the domain, which quickly also helps assess and evaluate relevant research’s impact and contribution in a certain area (Linnenluecke et al., 2019). To undergo bibliometrics studies, we can retrieve documents from widely used databases, such as Scopus, Web of Science (WOS) and Google Scholar. The extracted documents from any of these mentioned sources can be used to conduct or perform bibliometric analysis that will help create and generate an intellectual and conceptual structure derived through the connotations such as the co-word, co-citation and co-authorship analysis (Linnenluecke et al., 2019). Different bibliometric indices and techniques were used widely by researchers to create a scientific structure of the research, such as citation counts per paper, author and institution and country (Haustein & Lariviere, 2014; Linnenluecke et al., 2019). However, this study aimed to understand only the knowledge structure in this domain using a co-word analysis of keywords. This study will consider the relevant publications in the fast-moving and emerging area of misinformation and fake news from January 2008 to June 2022 from the Scopus database. Scopus is among the largest curated abstract and citation databases, with a wide global and regional coverage of scientific journals, conference proceedings and books while ensuring only the highest quality data are indexed through rigorous content selection. Besides enriched metadata records of scientific articles, the trustworthiness of Scopus has led to its use as the bibliometric data source for large-scale analyses in research assessments, landscape studies, science policy evaluations and university rankings (Baas et al., 2020). The works of literature were extracted from the Scopus database comprised of the keywords—‘Misinformation’, ‘Rumour’, ‘Rumor’, ‘Fake News’ and ‘Social Media and Rumor’. The extracted documents further understand the evolution and the intellectual structure of the domain studied so far (GökDemir et al., 2020). This study focuses on churning out the pattern of work done to understand the research on misinformation. What insights all the researchers have discussed so far, and what measures were taken to identify the menace of misinformation? Co-word analysis has been done to understand the plethora of studies on misinformation and social media. This study uses Scopus’s major abstract and citation catalogue of peer-reviewed articles. The search was TITLE-ABS KEY, meaning that the title, abstract and keywords are searched for relevant articles.

Scopus could identify and generate 1,407 articles published in the domain. Later, all the articles were also checked manually as a second step to ensure the right article was identified for the study. Finally, 1,363 articles were found relevant, and the bibliometric analysis was done on 1,363 articles in VOS viewer. A co-word analysis was done using a VOS viewer to find the interlink between the keywords. Co-word analysis analyses keywords’ co-occurrence and their relationship (Bhuyan et al., 2021). With the help of VOS viewer, a network and density map have been generated (Figures 2 and 3 in the Appendix), showings the strength of linkages between keywords. Based on the data extraction and analysis, 10 clusters were formed from 147 keywords that occurred at least 5 times in 1,363 papers. The clusters and the keywords had total link strength of 4,731. Further, these 10 clusters were merged into four major themes giving an intellectual structure to the study.

Findings & Discussion

Bibliometric Performance

The analysis done through VOS viewer shows that thus far, 1,363 studies have been done on misinformation, social media and rumour. The co-word analysis divided 147 keywords into 10 clusters (See Table 1 in the Appendix). The value of the minimum occurrence of the keywords was taken as five. Out of 2,525 keywords, 147 met the threshold. VOS viewer generated network map (as shown in Figure 3 in the Appendix) and density map (as shown in Figure 2 in the Appendix).

Figure 1.

Systematic Literature Review.

Intellectual Structure

The study conducted a co-word analysis to comprehend the intellectual structure of the 1,384 articles associated with misinformation, social media, rumour and fake news. Understanding and scrutinizing the existing pieces of literature in-depth provides an outline to chart out future research and possible directions in this study area. The themes were generated based on the cluster of keywords post the co-word analysis, pictorially represented in the network and density maps. The themes and keywords have been highlighted in detail in Table 2 (in the Appendix); however, the themes and the integrated keywords in the themes giving it an intellectual structure, are put below.

Theme 1: Misinformation: Perception, Motivation and the Stimuli behind its propagation (the theme is based on clusters 1, 2 and 7)

Theme 2: Information Dissemination Channels: Tool for Dispersing Misinformation (the theme is based on clusters 4, 7, 8 and 9),

Theme 3: Infodemic: Phenomenon emerged through Misinformation (the theme is based on included clusters 2, 3 and 6)

Theme 4: Effective Ways of Bursting the Misinformation (the theme is based on clusters 4, 5, 7 and 10).

Misinformation: Perception, Motivation and the Stimuli Behind its Propagation

The process of generating or producing misinformation is a complex phenomenon. It requires cognitive and reminiscence boundaries, directional motivations to guard some group or pre-conceived beliefs, and, most importantly, media literacy (Nyhan, 2021). With information overload, people tend to believe in all the information which comes to them (Schultz & Vandenbosch, 2010). Several researchers have pointed out that people generally have reasons and motivations for adopting misinformation (Allcott & Gentzkow, 2017; Apuke & Omar, 2020; Lewandowsky et al., 2012; Schultz & Vandenbosch, 2010; Li & Su, 2020; Yu et al., 2022). There has always been a debate between two basic motivations, that is, directional motivation and accuracy motivation (Taber & Lodge, 2006). Directional Motivation is directly associated with the individuals’ perception or beliefs regardless of the ideology (Apuke & Omar, 2020). However, there are various situations and interventions where directional motivation has been diminished by fostering accuracy motivation. Accuracy motivation is about requiring individuals to justify their beliefs and opinions. This directly reflects the individual’s thinking (Druckman, 2012).

Media Literacy plays an important role in disseminating misinformation. It is one of the necessary dimensions of fake news (Fleming, 2013). The central aim of media literacy is to raise a person’s thoughtfulness. Media literacy education is vital as it helps in arbitration firm information (Bowyer & Kahne, 2016). However, the positioning of media literacy as a solution to simplify the process of misinformation is contrary, as simply educating people to distinguish false information from truth is a tiny part of a larger problem. The problem arises from faith, and the propagation of misinformation has more to do with how many layers a reader must pass to arrive at the source of information (Connor & Weatherall, 2020; Marwick, 2018). Besides media literacy, anger and anxiety can affect the information’s accuracy (Weeks, 2015). Research has also validated the direct impact of the experience of anger and anxiety on sharing misinformation (Freiling et al., 2021; Goswami et al., 2022; Han et al., 2020; Kim et al., 2020; Weeks, 2015). With technological advancements, the spread of misinformation has become faster than ever, leading to complications and challenges in ensuring the reliability of the information (Alzanin & Azmi, 2018). Thus, the researchers have dealt with this area cautiously as a distinct pattern emerged from the considered studies where the cognitive factors are the social factors and also discussed the powerful role of information and communication that create a significant difference between the society and people around us.

Information Dissemination Channels: Tool for Dispersing Misinformation

Information dissemination is vital as it deals with exchanging ideas and attitudes from one person to another through verbal or visual symbols (Jowett & O’Donnell, 2018). Communication through social media and other internet-based platforms has gained huge popularity among people where they can easily share their views, opinions, ideas and attitudes and also helps people learn new things, develop interests and get entertained (Kapoor et al., 2017; Kümpel et al., 2015; Meel & Vishwakarma, 2019). In addition to providing information, social media has often been alleged to disperse misinformation (Faris et al., 2017; Vraga et al., 2020). With the abundance of social media and internet-based platforms and users, there always exists a risk of ‘Information Excess/Overload’ and, thus, misinformation (Apuke & Omar, 2020; de Bruin et al., 2021). Much information under the sky has generated a sense of intense competition for people’s attention, and in this race to gain the users’ attention, the information’s genuineness and quality degrade (Kozyreva et al., 2020; Rose, 2015).

Recently, WhatsApp, a messaging app, has emerged as a convenient platform for spreading misinformation (de Freitas Melo et al., 2019; Herrero-Diz et al., 2020; Machado et al., 2019; Tandoc, 2020). Since WhatsApp is user-friendly, its reach is high across generations; it becomes easier and more convenient to disseminate fake news and misinformation through WhatsApp (Pasquetto et al., 2022). The daily usage statistics reveal that over 70 billion messages are spread on WhatsApp daily (Indumathi & Gitanjali, 2020). Thus, it has also been responsible for various cases of lynching and unrest (Arun, 2019). Besides WhatsApp, Facebook also spreads misinformation at its peak (Clever et al., 2020; McMaster, 2021).

Moreover, the challenges of the spread of misinformation are not restricted to social media. The traditional media, too, plays an effective role. One of the leading instances is the US Presidential elections of 2016, where people believed in the fake news broadcasted by the traditional media (Pennycook & Rand, 2021; Tsfati et al., 2020). The Pizzagate story, which connected the Clinton campaign to a paedophile ring, saw the involvement of almost one-third of American adults believing in the story (Tsfati et al., 2020), and many more are cases of misinformation and fake news dissemination through conventional media channels (Goswami et al., 2022).

Further, a study by Allcott and Gentzkow (2017) reported that 20% of the respondents recalled seeing fake news stories before the election campaign of 2016. However, Cantini et al. (2022) discussed how the existence of social bots appears legitimate to social media users. These social bots were too effective in manipulating information and could work to influence public opinion. Cantini et al. (2022) also proposed Time-Aware Opinion Mining via Bot Removal (TIMBRE) methodology, which filters out the big data created by social bots. So, it is evident from this section discussion that many researchers in the recent past have focused on the channels of misinformation dissemination and how effectively they have been used to reach the masses.

Infodemic: Phenomenon Emerged Through Misinformation

During COVID-19, social media was flooded with numerous pieces of information ranging from news and facts on COVID-19, health care advice, pros and cons of COVID-19 vaccination and many more. The information rate was rising exponentially with the increase in COVID-19 worldwide. This rise led to the birth of the term ‘Infodemic’ along with the ‘pandemic’ (Solomon et al., 2020; World Health Organization, 2021). The rapid spread of information on social networking platforms created a huge rage, and people bowed to social media for support owing to the global lockdown and social distancing (Nabity-Grover et al., 2020). However, the over-reliability and over-dependence of people on social media were worrisome as misinformation spread extensively and rapidly through social media during that period (Doer et al., 2012; Wang et al., 2019). Misinformation spread amid a public health crisis is severely destructive as it misdirects an individual’s response and conduct, which gets considerably affected by the information obtained from social media (Swire-Thompson & Lazer, 2020). The spread of misinformation and conspiracy theories about the disease’s origin, prevention and treatment has led to a challenging situation in front of the authorities (Alam & Chu, 2020). The spread of misinformation regarding COVID-19 led to death and peaked during the pandemic (Harris & Moss, 2020).

The mis-infodemic has not been just limited to the spread of COVID-19. The vaccination campaign started by the government also saw the misinformation’s influence. One factor influencing vaccine hesitancy is the online dissemination of misinformation’s leading to vaccine hesitancy (Garett & Young, 2021). Misinformation on vaccination has greatly affected vaccine hesitancy and reduced vaccination rates globally (Liu et al., 2021). With much misinformation around the corner, people started hesitating about vaccine efficacy in treating the pandemic (Roberts et al., 2021). The World Health Organization recently listed vaccine hesitancy, or the reluctance to receive recommended vaccines when vaccination services are available, as the topmost health threat (Dubé & MacDonald, 2020). Researchers argue that vaccine hesitancy is affected by the three C’s model, that is, confidence, complacency and convenience (Kreps et al., 2020; MacDonald, 2015; Quinn et al., 2019). However, few researchers also discussed the effective role of certain political inclinations leading to vaccine hesitancy among people (Bruine de Bruin et al., 2020; Hornsey et al., 2020; Pennycook et al., 2021).

Here, the research and discussion distinctively highlighted the misinformation that got accelerated with the inception of COVID-19 and how such phenomena created a spillover effect on everything right, from getting the correct information to COVID-19 leading deaths to vaccines. The effect of misinformation generation was so massive that it led to the evolution of the Infodemic amid the pandemic.

Effective Ways of Bursting the Misinformation

Misinformation has always been a threat, and in recent times with the inception of pandemics, it has recently seen an astounding rise. The rise was exponential, and the propagation was huge across all media platforms. With the increase in the spread of misinformation, several researchers have looked into various ways to curb or burst the clouds of misinformation. Various models for detecting rumours and fake news were formed (Shao et al., 2016). In their study, Shao et al. (2016) discussed a platform named Hoaxy that automatically tracks online misinformation. The platform will analyse the information taken from news websites and social media and further track down the source of information, thus detecting and labelling it to be credible/fake. Kim et al. (2018) developed an expandable online algorithm, CURB, to reduce the spread of misinformation with provable guarantees. Mishra and Setty (2019) proposed a model named ‘SADHAN’, which would work in favour of detecting fake news and misinformation. Similar works were done by Qazvinian et al. (2011), who proposed a general outline that engages statistical models to fetch fake tweets that match a more accustomed query. Popat et al. (2018) created a model which uses deep learning algorithms and, without any human intervention, checks the credibility assessments of the textual claims and debunks the misinformation and fake news. This is a thorough end-to-end neural network model that is wholly automated. Researchers have mentioned online webs like Snopes and Politifact to cross-check the news and the facts to combat misinformation and fake news (Hannak et al., 2014; Nguyen & Kyumin, 2018; Shaar et al., 2020).

Furthermore, a few researchers also pointed out the role of media literacy in assessing and gauging misinformation. Media literacy also plays an important role in checking misinformation (Bunce, 2019; Courtney, 2017; Craft et al., 2017). Badrinathan (2020), in his work, also stressed that media literacy could strengthen confidence in fact-checking rumours and misinformation.

Discussion, Conclusion and Propositions

This study adopted a bibliometric method for determining a tactful review of the existing literature exploring the domain of misinformation and social media. VOS viewer was used in conducting a co-word analysis to build a logical base and structure of the domain-related literature. Considering the works of literature in the past, this research listed and reflected on the phenomenon of misinformation and the differential effects of misinformation on individuals, identities and culture. The present work tried to compile and reflect upon the literature, derive an intellectual structure, and present the work done in the past in this area in a well-segregated structure. Research in the past has thoroughly discussed the motivations for misinformation. They deliberated in their studies that, many a time, individuals’ beliefs, anger and anxiety motivate them to generate misinformation. Creating misinformation needs a tool to spread in society. In this case, the media plays an important role in spreading misinformation. Social and traditional media have significant characteristics that channel the spread of misinformation. With the emergence of new media, misinformation has seen an exponential rise in real-time sharing. The new media platforms empowered individuals with services that can easily and directly spread fake news and rumour with just one click. Platforms, such as WhatsApp, Facebook, Twitter and YouTube became rapidly emerging platforms for the spread of misinformation and rumours. Advanced technological innovations such as social bots were also used to manipulate information and spread misinformation. The cases of misinformation worldwide and its presence before the onset of social media cannot be neglected; traditional media has also played a significant role in spreading misinformation. However, there have been fewer studies on the spread of misinformation by the traditional media. The Scopus search revealed only a few articles before the onset of social media.

The degree of misinformation has increased manifold during the pandemic. World Health Organisation has termed the spread of misinformation as Infodemic and has perceived it as far more dangerous than the pandemic. Research related to the Infodemic has emphasised numerous casualties due to spreading misinformation. Considering this rapid spread, researchers have also come across different ways of bursting and detecting misinformation and discussed ways of curbing this menace in society.

These scenarios are pertinent because most of the work happened in this area. However, some gap exists where this research will pave the path for future research in this domain. Below are a few propositions that will help us better comprehend the scenario.

Proposition 1: Misinformation is menacing, and detecting and curbing them immediately is the only way out. However, studies and researchers have discussed and proposed models which technophiles cannot handle due to their complexities. A gap exists in this area where there is a dire need for a basic model to cross-check the information before sharing.

Proposition 2: The media, be it digital or traditional, emerged as a primary source for the spread of misinformation. This is a high time when policy-level initiatives need to be taken where the spread of misinformation should be equated with some forfeiture to limit the menace. There is a high need for studies in this area of ‘how media can limit the broadcast of misinformation’.

Proposition 3: Considering the technological advancements around us in recent times, limited studies are focusing on social bots, the use of artificial intelligence (AI) devices and aids in propagating misinformation or looking into ways of limiting them. A few more studies in this area to understand how they affect the stream of misinformation will also complement the knowledge area.

Proposition 4: Studies on how to train people to identify misinformation are crucial. Research in this area where discussions on various ways of training people and raising the knowledge of identifying misinformation are crucial.

Proposition 5: Researchers, policymakers and people in practice can come together to propose a wholesome approach that can be simple and effective as well as can be used by the masses to curb the propagation of misinformation through technological innovation where the platforms themselves eliminate the misinformation or shows some indications to recognise that the news or the piece of information is not from credible sources.

Limitations

This article is constrained to the bibliometric information mended from the database retrieved from Scopus. The study cannot rebate the option of leaving out misinformation, social media and rumour-related articles from other databases, such as WOS, Google Scholar or other relevant sources. Those interested in the corpus can envisage the domain using the WOS database as quality assurance and can further negate or support the generalised findings of the research.

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Appendix

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