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This study aims to examine how characteristics of an online review and a consumer reading the review influence the probability that the consumer will assess the review as authentic (real) or inauthentic (fake). This study further examines the specific factors that increase or decrease a consumer’s ability to detect a review’s authenticity and reasons a consumer makes these authenticity assessments.

Hypothesized relationships were tested using an online experiment of over 400 respondents who collectively provided 3,224 authenticity assessments along with 3,181 written self-report reasons for assessing a review as authentic or inauthentic.

The findings indicate that specific combinations of factors including review valence, length, readability, type of content and consumer personality traits and demographics lead to systematic bias in assessing review authenticity. Using qualitative analysis, this paper provided further insight into why consumers are deceived.

This research showed there are important differences in the way the authenticity assessment process works for positive versus negative reviews and identified factors that can make a fake review hard to spot or a real review hard to believe.

This research has implications for both consumers and businesses by emphasizing areas of vulnerability for fake information and providing guidance for how to design review systems for improved veracity.

This research is one of the few works that explicates how people assess information authenticity and their consequent assessment accuracy in the context of online reviews.

This paper aims to examine the dynamics of emotional cues and cognitive cues in review fakeness. Additionally, the boundary condition (i.e. review valence) for the dynamics between emotional cues and cognitive cues is investigated.

This research conducted two studies, which analyzed restaurant and hotel reviews collected from Yelp.com. The authors adopted linguistic inquiry and word count 2015 to code review contents and tested the hypotheses using logistic regression.

Fake reviews contain more emotional cues compared with authentic reviews. Moreover, the dynamics of emotional cues and cognitive cues are salient among negative reviews.

This research provides implications to identify fake online reviews based on linguistic cues.

This research contributes to the literature by revealing the competition of mental resources between emotional and cognitive systems when deception is for harming others. Grounded in interpersonal deception theory, this paper investigates the interactive effect and complements the literature, which mainly used emotional cues and cognitive cues individually to detect fake reviews.

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 systematic review aims to synthesize the literature reporting the motives, sociodemographic, attitude/behavior and impacts of fake news during the COVID-19 pandemic, targeting the general population worldwide.

A systematic review approach was adopted based on PRISMA, targeting articles published in five databases from January 2020 to November 2021. The screening resulted in 46 eligible papers.

Results indicate low level of awareness, knowledge, media/health literacy, low trust in science/scientists and entertainment/socialization to be the main motivating drivers for fake news dissemination, whereas the phenomenon is more prominent among those with low socio-economic status, and males. Negative impacts were reported due to fake news dissemination, especially violation to precautionary measures, negative affections, and low trust in government/news, with many believing that others are more susceptible to fake news than themselves.

Considering the pandemic is still on-going and the deleterious consequences of fake news, there is a need for cohort-based interventions from the concerned authorities.

The systematic review covers a wide timeline of 23 months (i.e. up to end of 2022) targeting five well-known databases, hence articles examined are deemed extensive and comprehensive. The review specifically focused on the general population with results revealing interesting motives, sociodemographic profiles, attitude and impact of this phenomenon during the COVID-19 pandemic.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2022-0082.

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 extensive distribution of fake news on the internet (FNI) has significantly affected many lives. Although numerous studies have recently been conducted on this topic, few have helped us to systematically understand the antecedents and consequences of FNI. This study contributes to the understanding of FNI and guides future research.

Drawing on the input–process–output framework, this study reviews 202 relevant articles to examine the extent to which the antecedents and consequences of FNI have been investigated. It proposes a conceptual framework and poses future research questions.

First, it examines the “what”, “why”, “who”, “when”, “where” and “how” of creating FNI. Second, it analyses the spread features of FNI and the factors that affect the spread of FNI. Third, it investigates the consequences of FNI in the political, social, scientific, health, business, media and journalism fields.

The extant reviews on FNI mainly focus on the interventions or detection of FNI, and a few analyse the antecedents and consequences of FNI in specific fields. This study helps readers to synthetically understand the antecedents and consequences of FNI in all fields. This study is among the first to summarise the conceptual framework for FNI research, including the basic relevant theoretical foundations, research methodologies and public datasets.

This paper aims to unify fragmented definitions of fake news and also present a comprehensive classification of the concept. Additionally, it provides an agenda for future marketing research based on the findings.

A review of 36 articles investigating fake news from 1990 to 2020 was done. In total, 615 papers were found, and the article pool was refined manually in two steps; first, articles were skimmed and scanned for nonrelated articles; second, the pool was refined based on the scope of the research.

The review resulted in a new definition and a collective classification of fake news. Also, the feature of each type of fake news, such as facticity, intention, harm and humor, is examined as well, and a definition for each type is presented.

This extensive study, to the best of the author’s knowledge, for the first time, reviews major definitions and classification on fake news.

Este artículo pretende unificar las definiciones fragmentadas de las noticias falsas y también presentar una clasificación exhaustiva del concepto. Además, ofrece una agenda para futuras investigaciones de marketing basada en los resultados.

Se realizó una revisión de 36 artículos que investigaban las noticias falsas desde 1990 hasta 2020. Se encontraron 615 artículos, y el grupo de artículos se refinó manualmente en dos pasos, primero, se descremaron los artículos y se escanearon los artículos no relacionados, segundo, el grupo se refinó basado en el alcance de la investigación.

La revisión dio como resultado una nueva definición y una clasificación colectiva de las noticias falsas. Además, se examinan las características de cada tipo de noticias falsas, como la facticidad, la intención, el daño y el humor, y se presenta una definición para cada tipo.

este amplio estudio revisa por primera vez las principales definiciones y la clasificación de las noticias falsas.

本文旨在统一假新闻的零散定义, 并对假新闻的概念进行全面的分类。此外, 它还根据本文的研究结果为未来的营销研究提供了一个议程。

对1990年至2020年期间调查假新闻的36篇文章进行了回顾。一共发现了615篇论文, 并分为两步对此文章库进行了人工提炼：首先, 对文章进行略读和扫描以找出非相关文章, 其次, 根据研究范围对文章库进行了提炼。

此次审查导致了对假新闻的新定义和集体分类。此外, 还分析了假新闻的真实性、意图、危害性、幽默性等各种类型的特征, 并给出了各种类型的定义。

此项涉及广泛假新闻内容的研究首次回顾了关于假新闻的主要定义和分类。

The rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels, freelance reporters and websites. Amid the coronavirus disease 2019 (COVID-19) pandemic, individuals are inflicted with these false and potentially harmful claims and stories, which may harm the vaccination process. Psychological studies reveal that the human ability to detect deception is only slightly better than chance; therefore, there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate. This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre- and post-pandemic, which has never been done before to the best of the authors’ knowledge.

The detailed literature review on fake news detection is divided into three major parts. The authors searched papers no later than 2017 on fake news detection approaches on deep learning and machine learning. The papers were initially searched through the Google scholar platform, and they have been scrutinized for quality. The authors kept “Scopus” and “Web of Science” as quality indexing parameters. All research gaps and available databases, data pre-processing, feature extraction techniques and evaluation methods for current fake news detection technologies have been explored, illustrating them using tables, charts and trees.

The paper is dissected into two approaches, namely machine learning and deep learning, to present a better understanding and a clear objective. Next, the authors present a viewpoint on which approach is better and future research trends, issues and challenges for researchers, given the relevance and urgency of a detailed and thorough analysis of existing models. This paper also delves into fake new detection during COVID-19, and it can be inferred that research and modeling are shifting toward the use of ensemble approaches.

The study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful, although currently reported accuracy has not yet reached consistent levels in the real world.

The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of emotion-driven content, sentimental resonance, topic modeling and linguistic features of news articles to predict the probability of fake news.

A data set of over 12,000 articles was chosen to develop a model for fake news detection. Machine learning algorithms and natural language processing techniques were used to handle big data with efficiency. Lexicon-based emotion analysis provided eight kinds of emotions used in the article text. The cluster of topics was extracted using topic modeling (five topics), while sentiment analysis provided the resonance between the title and the text. Linguistic features were added to the coding outcomes to develop a logistic regression predictive model for testing the significant variables. Other machine learning algorithms were also executed and compared.

The results revealed that positive emotions in a text lower the probability of news being fake. It was also found that sensational content like illegal activities and crime-related content were associated with fake news. The news title and the text exhibiting similar sentiments were found to be having lower chances of being fake. News titles with more words and content with fewer words were found to impact fake news detection significantly.

Several systems and social media platforms today are trying to implement fake news detection methods to filter the content. This research provides exciting parameters from a viral theory perspective that could help develop automated fake news detectors.

While several studies have explored fake news detection, this study uses a new perspective on viral theory. It also introduces new parameters like sentimental resonance that could help predict fake news. This study deals with an extensive data set and uses advanced natural language processing to automate the coding techniques in developing the prediction model.