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This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information practices and platforms in business and management studies.

The paper applies a focused, SLR method to analyze articles on fake news in business and management journals from 2010 to 2020.

The paper analyzes the definition, theoretical frameworks, methods and research gaps of fake news in the business and management domains. It also identifies some promising research opportunities for future scholars.

The paper offers practical implications for various stakeholders who are affected by or involved in fake news dissemination, such as brands, consumers and policymakers. It provides recommendations to cope with the challenges and risks of fake news.

The paper discusses the social consequences and future threats of fake news, especially in relation to social networking and social media. It calls for more awareness and responsibility from online communities to prevent and combat fake news.

The paper contributes to the literature on information management by showing the importance and consequences of fake news sharing for societies. It is among the frontier systematic reviews in the field that covers studies from different disciplines and focuses on business and management studies.

This paper aims to explore the determinants causing fake information proliferation on social media platforms and the challenges to control the diffusion of fake news phenomena.

The authors applied the systematic review methodology to conduct a synthetic analysis of 37 articles published in peer-reviewed journals retrieved from 13 scholarly databases.

The findings of the study displayed that dissatisfaction, behavior modifications, trending practices to viral fake stories, natural inclination toward negativity and political purposes were the key determinants that led individuals to believe in fake news shared on digital media. The study also identified challenges being faced by people to control the spread of fake news on social networking websites. Key challenges included individual autonomy, the fast-paced social media ecosystem, fake accounts on social media, cutting-edge technologies, disparities and lack of media literacy.

The study has theoretical contributions through valuable addition to the body of existing literature and practical implications for policymakers to construct such policies that might prove successful antidote to stop the fake news cancer spreading everywhere via digital media. The study has also offered a framework to stop the diffusion of fake news.

The pandemic has enhanced the global phenomenon of disinformation. This paper aims to study the false news concerning COVID-19, spread through social media in Spain, by using the LatamChequea database for a duration from 01/22/2020, when the first false information has been detected, up to 03/09/2021.

A quantitative analysis has been conducted with regard to the correlation between fake news stories and the pandemic state, the motive to share them, their dissemination in other countries and the effectiveness of fact checking. This study is complemented by a qualitative method: a focus group conducted with representatives of different groups within the society.

Fake news has been primarily disseminated through several social networks at the same time, with two peaks taking place in over a half of the said false stories. The first took place from March to April of 2020 during complete lockdown, and we were informed of prevention measures, the country’s situation and the origin of the virus, whereas the second was related to news revolving around the coming vaccines, which occurred between October and November. The audience tends to neither cross-check the information received nor report fake news to competent authorities, and fact-checking methods fail to stop their spread. Further awareness and digital literacy campaigns are thus required in addition to more involvement from governments and technological platforms.

The main limitation of the research is the fact that it was only possible to conduct a focus group of five individuals who do not belong to generation Z due to the restrictions imposed by the pandemic, although a clear contribution to the analysis of the impact of fake news on social networks during the COVID-19 pandemic in Spain can be seen from the privileged experiences in each of the fields of work that were identified. In this sense, the results of the study are not generalizable to a larger population. On the other hand, and with a view to future research, it would be advisable to carry out a more specific study of how fake news affects generation Z.

This research is original in nature, and the findings of this study are valuable for business practitioners and scholars, brand marketers, social media platform owners, opinion leaders and policymakers.

This study aims to investigate the current practices being implemented against the dissemination of fake online news, identify the relationship of new media literacy (NML) with fake news epidemic control and find out the challenges in identifying valid sources of information.

To accomplish constructed objectives of this study, a systematic literature review (SLR) was conducted. The authors carried out the “Preferred Reporting Items for the Systematic Review and Meta-analysis” guidelines as a research methodology. The data were retrieved from ten world’s leading digital databases and online tools. A total of 25 key studies published in impact factor (IF) journals were included for systematic review vis-à-vis standard approaches.

This study revealed trending practices to control fake news consisted of critical information literacy, civic education, new thinking patterns, fact-checkers, automatic fake news detection tools, employment of ethical norms and deep learning via neural networks. Results of the synthesized studies revealed that media literacy, web literacy, digital literation, social media literacy skills and NML assisted acted as frontline soldiers in combating the fake news war. The findings of this research also exhibited different challenges to control fake news perils.

This study provides pertinent theoretical contributions in the body of existing knowledge through the addition of valuable literature by conducting in-depth systematic review of 25 IF articles on a need-based topic.

This scholarly contribution is fruitful and practically productive for the policymakers belonging to different spectrums to effectively control web-based fake news epidemic.

This intellectual piece is a benchmark to address fake news calamities to save the social system and to educate citizens from harms of false online stories on social networking websites.

This study vivifies new vistas via a reinvigorated outlook to address fake news perils embedded in dynamic, rigorous and heuristic strategies for redefining a predetermined set of social values.

This paper aims to examine how content relevancy influences consumers’ preference to receive and share fake news. Further, it investigates how these receivers perceive the social image of the people who share fake news. Finally, this study examines how brand strength and valence and credibility of fake content influence consumer’s word-of-mouth recommendations, purchase intentions and attitude toward the brand.

Three experiments were conducted to test the hypotheses. The data was analyzed using a two-way analysis of variance and PROCESS techniques.

Findings indicate that people prefer to receive and share relevant content, even if it is fake. Sharing fake news conveys the sender’s sociability but also creates a negative perception of narcissism. Individuals are more likely to recommend a brand if the fake news is perceived as credible and positive (vs negative). Finally, brand-strength can help brands to negate the harmful effects of fake news.

Future research can explore the role of group dynamics, tie-strength and media richness (text, image and videos) in the dispersion of fake news and its impact on brands.

Marketers should communicate and educate consumers that sharing fake content can harm their social image, which can reduce information dispersion. Marketers should also improve brand-strength that can protect the brand against the adverse impact of fake news.

This study contributes to the emerging literature on fake news by studying the impact of fake news on consumer intentions and attitudes toward the brand, which are critical for the success of any brand.

Purveyors of fake news perpetuate information that can harm society, including businesses. Social media's reach quickly amplifies distortions of fake news. Research has not yet fully explored the mechanisms of such adversarial behavior or the adversarial techniques of machine learning that might be deployed to detect fake news. Debiasing techniques are also explored to combat against the generation of fake news using adversarial data. The purpose of this paper is to present the challenges and opportunities in fake news detection.

First, this paper provides an overview of adversarial behaviors and current machine learning techniques. Next, it describes the use of long short-term memory (LSTM) to identify fake news in a corpus of articles. Finally, it presents the novel adversarial behavior approach to protect targeted business datasets from attacks.

This research highlights the need for a corpus of fake news that can be used to evaluate classification methods. Adversarial debiasing using IBM's Artificial Intelligence Fairness 360 (AIF360) toolkit can improve the disparate impact of unfavorable characteristics of a dataset. Debiasing also demonstrates significant potential to reduce fake news generation based on the inherent bias in the data. These findings provide avenues for further research on adversarial collaboration and robust information systems.

Adversarial debiasing of datasets demonstrates that by reducing bias related to protected attributes, such as sex, race and age, businesses can reduce the potential of exploitation to generate fake news through adversarial data.

The purpose of this study is to identify the relationship between artificial intelligence (AI) and fake news detection. It also intended to explore the negative effects of fake news on society and to find out trending techniques for fake news detection.

“Preferred Reporting Items for the Systematic Review and Meta-Analysis” were applied as a research methodology for conducting the study. Twenty-five peer-reviewed, most relevant core studies were included to carry out a systematic literature review.

Findings illustrated that AI has a strong positive relationship with the detection of fake news. The study displayed that fake news caused emotional problems, threats to important institutions of the state and a bad impact on culture. Results of the study also revealed that big data analytics, fact-checking websites, automatic detection tools and digital literacy proved fruitful in identifying fake news.

The study offers theoretical implications for the researchers to further explore the area of AI in relation to fake news detection. It also provides managerial implications for educationists, IT experts and policymakers. This study is an important benchmark to control the generation and dissemination of fake news on social media platforms.

The objective of this study was to investigate the impacts of personality traits and the ability to detect fake news on information avoidance behavior. It also examined the effect of personality traits on the ability to detect fake news.

The sample population included Shiraz University students who were studying in the second semester of academic year 2021 in different academic levels. It consisted of 242 students of Shiraz University. The Big Five theory was used as the theoretical background of the study. Moreover, the research instrument was an electronic questionnaire consisting of the three questionnaires of the ability to detect fake news (Esmaeili et al., 2019, inspired by IFLA, 2017), the Big Five personality traits (Goldberg, 1999) and information avoidance (Howell and Shepperd, 2016). The statistical methods used to analyze the data were Pearson correlation and stepwise regression, which were performed through SPSS software (version 26).

The results showed that from among the five main personality factors, only neuroticism had a positive and significant effect on information avoidance. In addition, the ability to detect fake news had a significant negative effect on information avoidance behavior. Further analyses also showed positive and significant effects of openness to experience and extraversion on the ability to detect fake news. In fact, the former had more predictive power.

Following the Big Five theory considering COVID-19 information avoidance and the ability to detect COVID-19 fake news, this study shifted the focus from environmental factors to personality factors and personality traits. Furthermore, this study introduced the ability to detect fake news as an influential factor in health information avoidance behaviors, which can be a prelude for new research studies.

The present study applied the five main personality factors theory in the context of information avoidance behavior and the ability to detect fake news, and supported the effect of personality traits on these variables.

Fake news in cyberspace has greatly interfered with national governance, economic development and cultural communication, which has greatly increased the demand for fake news detection and intervention. At present, the recognition methods based on news content all lose part of the information to varying degrees. This paper proposes a lightweight content-based detection method to achieve early identification of false information with low computation costs.

The authors' research proposes a lightweight fake news detection framework for English text, including a new textual feature extraction method, specifically mapping English text and symbols to 0–255 using American Standard Code for Information Interchange (ASCII) codes, treating the completed sequence of numbers as the values of picture pixel points and using a computer vision model to detect them. The authors also compare the authors' framework with traditional word2vec, Glove, bidirectional encoder representations from transformers (BERT) and other methods.

The authors conduct experiments on the lightweight neural networks Ghostnet and Shufflenet, and the experimental results show that the authors' proposed framework outperforms the baseline in accuracy on both lightweight networks.

The authors' method does not rely on additional information from text data and can efficiently perform the fake news detection task with less computational resource consumption. In addition, the feature extraction method of this framework is relatively new and enlightening for text content-based classification detection, which can detect fake news in time at the early stage of fake news propagation.

This study aims to examine how digital literacy competency can mitigate misinformation in social media among young adults. In recent years, concerns over misinformation have triggered a renewed interest in the aspect of digital literacy. Many young adults in Malaysia are not able to differentiate between real news and fake news. Although there are plenty of studies examining fake news, studies examining the mitigation of misinformation through the lens of digital literacy are still rudimentary.

This research adopted a quantitative approach by conducting a cross-sectional survey among university students in Malaysia to examine how their digital literacy competency influences misinformation. The sample size was estimated GPower software. A total of 134 respondents between the age of 19 and 25 were sampled because young adults in this age group tend to show little difference in their digital literacy level. Structural equation modelling (SEM) was used to examine the proposed model.

The study results reveal that two of the three domains of digital literacy competence, technical literacy and cognitive literacy, have a positive association in reducing misinformation among university students; however, socio-emotional literacy has the opposite effect. Additionally, the survey also explicates that hedonic motivation helps in misinformation mitigation, whereas habit does not.

Theoretically, this study contributes to the literature by revealing how digital literacy can help in identifying misinformation masquerading as valid information through proper verification and analysis, especially in the digital age where everyone is susceptible to misinformation. The results of the study also contribute to the development of a new digital literacy framework that can cultivate a digitally literate generation who can navigate the informational landscape smartly and therefore distinguish between facts and fake news.