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The purpose of this paper is to provide a general overview of how the major social media companies are addressing the problem of fake news and the spread of digital disinformation. The fight against bad sources and false authorities is one that librarians have been engaged in for a very long time.

While the inaccurate information may not always have been called “fake news,” misinformation, propaganda, conspiracy, exaggeration, manipulated facts and out and out lies have always been combated by librarians through information literacy. It is nearly impossible to go a day in this current news climate without reading or hearing the term “fake news”; whether it is being tweeted by the 45th president of the USA, discussed in the media, detailed in articles about social media or addressed by librarians in literature, conversation, conferences, tweets and blog posts.

The inescapable phrase was named word of the year for 2017 by both the American Dialect Society (“Fake News,” 2018) and Collins Dictionary (Meza, 2017). While the official definitions provided by a number of different sources may vary, the gist of what is meant by fake news is that it is information that is largely inaccurate, misleading, unsubstantiated, manipulated or completely fabricated that is being passed off as truthful, authoritative and accurate.

Though the phrase “fake news” may seem to be a recent term, it has actually been around since the end of the nineteenth century and it is not limited to just discussing political news according to Merriam-Webster.

The insidious proliferation of online misinformation represents a significant societal problem. With a wealth of research dedicated to the topic, it is still unclear what determines fake news sharing. This paper comparatively examines fake and accurate news sharing in a novel experimental setting that manipulates news about terrorism.

The authors follow an extended version of the uses-and-gratification framework for news sharing, complemented by variables commonly employed in fake news rebuttal studies.

Logistic regression and classification trees revealed worry about the topic, media literacy, information-seeking and conservatism as significant predictors of willingness to share news online. No significant association was found for general analytical thinking, journalism skepticism, conspiracy ideation, uses-and-gratification motives or pass-time coping strategies.

The current results broaden and expand the literature examining beliefs in and sharing of misinformation, highlighting the role of media literacy in protecting the public against the spread of fake news.

This is, to the authors’ knowledge, the first study to integrate a breadth of theoretically and empirically driven predictors of fake news sharing within a single experimental framework.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2022-0693

The broader analytical framing of systematically distorted communication (SDC) helps extract value out of the enormous amount of scholarship on fake news.

The massive literature on fake news has been the subject of handbook overviews, systematic literature reviews, summaries, taxonomies, citation studies and so on. Deploying these tools, the approaches that the literature takes can be characterized, Habermas' concept of systematically distorted communication (SDC) will then be presented in its context, reviewed and put to work to frame fake news research to tell us new things that individual pieces of specific analysis and research do not. Conclusions will be offered from this analysis.

Fake news research has become repetitive, revolving around themes such as the fate of journalism, the role of technology, remediating its effects and deep dives into definitional components (disinformation, misinformation, lies and so on). A broader framing of systematically distorted communication allows us to arrive at some conclusions about contemporary fake news: that it is a power strategy with a particular right-wing slant and it creates a sociology – that is, its own interpretive environment – hostile to democratic functioning. It answers the question: what is fake news for?

A perspective on fake news research is much needed and Habermas' concept is a useful framing mechanism for the large corpus of research. Systematically distorted communication asks – and answers – different questions of the research. Meanwhile, SDC itself is modified by its application to fake news research and contemporary conditions.

Fake news on social media about COVID-19 pandemic and its associated issues (e.g. lockdown) caused public panic that lead to supply chain (SC) disruptions, which eventually affect firm performance. The purpose of this study is to understand how social media fake news effects firm performance, and how to mitigate such effects.

Grounded on dynamic capability view (DCV), this study suggests that social media fake news effects firm performance via SC disruption (SCD) and SC resilience (SCR). Moreover, the relation between SCD and SCR is contingent upon SC learning (SCL) – a moderated mediation effect. To validate this complex model, the authors suggest effectiveness of using partial least squares structural equation modeling (PLS-SEM). Using an online survey, the results support the authors’ hypotheses.

The results suggest that social media fake news does not affect firm performance directly. However, the authors’ serial mediation test confirms that SCD and SCR sequentially mediate the relationship between social media fake news and firm performance. In addition, a moderated serial mediation test confirms that a higher level of SCL strengthens the SCD–SCR relationship.

This work offers a new theoretical and managerial perspective to understand the effect of fake news on firm performance, in the context of crises, e.g. COVID-19. In addition, this study offers the advancement of PLS as more robust for real-world applications and more advantageous when models are complex.

Prior studies in the SC and marketing domain suggest different effects of social media fake news on consumer behavior (e.g. panic buying) and SCD, respectively. This current study is a unique effort that investigates the ultimate effect of fake news on firm performance with complex causal relationships via SCD, SCR and SCL.

The purpose of this study is to examine the respondents’ evaluation of the pervasiveness of fake news through various SM platforms in Kuwait. The authors also examined the respondents’ attitudes toward most fake news on SM. A total of 1,539 Kuwaitis were selected.

The questionnaire was the major tool for this study. The respondents, from whom demographic information was obtained, were asked about which SM platforms most frequently spread fake news, their attitudes toward the subjects most frequently involved in spreading fake news, their degree of use of the six SM platforms and interest in various subjects, and the attitudes toward the negative nature of SM news. SPSS was used for the data analysis.

The results showed that WhatsApp was the most likely to be used to disseminate fake news; Twitter and Instagram ranked second. The younger subjects were affected more by text and voice clips than the older ones.

The study, hopefully, produces new knowledge on the subject of fake news in social media, especially in the Arab world, since there are few studies conducted in the region. The study showed that WhatsApp was the SM tool most likely to be responsible for disseminating fake news in Kuwait, which may shed light on the usage of this application to be a news tool, rather than merely an interpersonal communication medium.

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。

Owing to the increased accessibility of internet and related technologies, more and more individuals across the globe now turn to social media for their daily dose of news rather than traditional news outlets. With the global nature of social media and hardly any checks in place on posting of content, exponential increase in spread of fake news is easy. Businesses propagate fake news to improve their economic standing and influencing consumers and demand, and individuals spread fake news for personal gains like popularity and life goals. The content of fake news is diverse in terms of topics, styles and media platforms, and fake news attempts to distort truth with diverse linguistic styles while simultaneously mocking true news. All these factors together make fake news detection an arduous task. This work tried to check the spread of disinformation on Twitter.

This study carries out fake news detection using user characteristics and tweet textual content as features. For categorizing user characteristics, this study uses the XGBoost algorithm. To classify the tweet text, this study uses various natural language processing techniques to pre-process the tweets and then apply a hybrid convolutional neural network–recurrent neural network (CNN-RNN) and state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) transformer.

This study uses a combination of machine learning and deep learning approaches for fake news detection, namely, XGBoost, hybrid CNN-RNN and BERT. The models have also been evaluated and compared with various baseline models to show that this approach effectively tackles this problem.

This study proposes a novel framework that exploits news content and social contexts to learn useful representations for predicting fake news. This model is based on a transformer architecture, which facilitates representation learning from fake news data and helps detect fake news easily. This study also carries out an investigative study on the relative importance of content and social context features for the task of detecting false news and whether absence of one of these categories of features hampers the effectiveness of the resultant system. This investigation can go a long way in aiding further research on the subject and for fake news detection in the presence of extremely noisy or unusable data.

This paper seeks to disambiguate the phenomenon by clarifying terms, highlighting current efforts, including the importance of critical thought and awareness, and a test for genuine serendipity.

The authors review the literature, primarily from a library and information science perspective, and arrive at a theoretical framework and model.

The authors find various initiatives to fight fake news. Building upon Karlova and Fisher's (2013) model as well as research on critical thinking and serendipity, the primary contribution of the paper is a disinformation behavior framework and model. The model includes both the problem of disinformation from a creator and user perspective, as well as the solutions to fight it.

The framework will guide practitioners and researchers in library and information science and beyond, as well as other stakeholders in both understanding the phenomenon, and leading the fight against it.

The spreading of false information has become an alarming phenomenon in the last few years, leading to the popularity of terms such as misinformation, disinformation, infodemic and fake news. While information professionals have been called upon to lead the fight against fake news, in the lack of a comprehensive understanding of the phenomenon, current efforts have been isolated and inadequate. Most models of information behavior deal with information, and not misinformation or disinformation per se.

Fake news represents a real risk for brands, particularly for firms selling essential products, such as food items. Despite this anecdotal acknowledgement, the dynamics of the relationship between fake news and brand reputation remain under-explored. The present study addresses this gap by examining the association of consumer values (universalism and openness to change), brand trust, fake news risk and system trust in the context of natural food products.

The study utilised a cross-sectional survey design and the mall-intercept method to collect data from 498 consumers of natural food residing in India. To test the hypotheses, which were grounded in the stimulus-organism-response (SOR) framework, the collected data were analysed using covariance-based structural equation modelling in SPSS AMOS. The conceptual model proposed universalism and openness to change as stimuli, brand trust as an internal state or organism and fake news risk – captured through the tendency of consumers to believe and act on fake news – as a response.

The findings support a positive association of universalism with brand trust and a negative association with fake news risk. In comparison, openness to change has no association with either brand trust or fake news risk. Brand trust, meanwhile, is negatively related to fake news, and this association is moderated by system trust. Furthermore, brand trust partially mediates the relationship between universalism value and fake news risk.

Notably, the present study is one of the first attempts to understand the fake news risk associated with natural food brands by utilising the SOR framework in an emerging market setting. The study provides interesting insights for policymakers, brands and consumers.

This study aims to explore how consumers process and respond to fake news on halal food in a Muslim-majority country. The study hypothesises that fake news that violates one’s moral code could induce anger resulting in brand hate. Religiosity plays a role as a moderating variable for the relationship.

Data were collected in two studies using quasi-experiment repeated measures factorial design, 2 × 2 between subjects. In Study 1, 219 participated, whereas in Study 2, a total of 173 was recruited for the experiment. The study uses one-way repeated measures design ANOVA and MEMORE to test the effects of moderation for repeated measures.

The findings indicate that fake news that violates one’s moral code, belief and values could induce anger and brand hate. Religiosity moderates the relationship between anger and brand hate

The study’s limitations include the limited dimension measured for religiosity and brand hate.

The study of brand hate as opposed to brand love is relatively scarce. This study has observed how fake news that violates one’s moral code is detrimental to the brand, which in turn induces hate. Marketing managers have to be cautious in marketing their products in more religious countries. Besides, they have to be proactive in combating fake news that might tarnish their brand.