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Given the rapid growth in efforts on misinformation correction, the study aims to test how evidence type and veracity interact with news agreement on the effectiveness of fact-checking on how well a corrective message discount a false news information.

Experimental participants (N = 511) in Hong Kong were exposed to the same news article and then to a piece of corrective information debunking the news article with variation in the types of evidence (numerical vs narrative) and veracity (no verdict vs half false vs entirely false) in 2019.

Among the participants who disagreed with the news article, numerical fact-checking was more effective than narrative fact-checking in discounting the news article. Some evidence of the backfire effect was found among participants for whom the article was attitude incongruent.

When debunking false information with people exposed to attitude-incongruent news, a milder verdict presented in the form of a half-false scale can prompt a more positive perception of the issue at stake than an entirely false scale, implying that a less certain verdict can help in mitigating the backfire effect compared to a certain verdict.

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.

In an April 2018 webinar, the Freedom to Read Foundation asked the question: Do information consumers have the right to be misinformed? Fake news is nuanced, prolific, sometimes malicious, often automated, and has the added complications of emotion, privacy, and ethics. And unfortunately, fake news and its foundational components of misinformation and disinformation (mis/dis), aren’t quickly fixed by learning a few information literacy strategies or media literacy concepts. People are inclined to believe what they want to believe despite training, awareness of critical thinking, and acknowledgement of widely held “objective facts.” Are they less intelligent or information poor because they choose to exist in their own information worlds and privilege their own confirmation biases?

Individuals have the right to seek, avoid, and use information for themselves as they see fit, regardless of whether or not others deem their information deficient, insufficient, or even false. However, this is a very black and white perspective on a much more complex and nuanced moral issue. Even if it is to their detriment, people ultimately do have the right to be misinformed, choosing the information they will and won’t accept. But information professionals should still be compelled to instruct patrons on the importance of seeking, finding, and using quality information and sources.

The purpose of this paper is to develop a multi-agent-based simulation model for the online opinion dissemination during hazardous chemical leakage emergencies into rivers in China, to explore an appropriate crisis information release policy of China’s government for controlling public panic.

In the proposed model, two fundamental attributes of crisis information, i.e., truthfulness (for true or false news) and attitude (for positive, neutral or negative opinion), are considered. Four major agents in the online community system, i.e., citizens, the government, media and opinion leaders, are included. Using four typical accidents of hazardous chemical leakage into rivers in China as case studies, insightful policy implications can be obtained for crisis management and panic control.

The news about the terrible potential damages from such a type of accidents will instantly arise wide-ranging public panic; therefore, the corresponding crisis information release policy should be carefully designed. It is strongly advised against publishing false news to temporarily conceal the accidents, which will seriously hurt the government’s reputation and agitate much larger-scale public panic in terms of degree and duration. To mitigate public panic, the true news especially about treatment measurements should be published immediately. If the government does nothing and releases no crisis information, the public panic will go out of control.

This paper only focuses on the crisis information release policies from the perspectives of the government. Furthermore, this study especially focuses on the cases in China, and extending the proposed model study for general contexts is an important direction to improve this study. Finally, the proposed model should be extended to other types of emergencies to further justify its generalization and universality, especially various natural catastrophes like storms, floods, tsunamis, etc.

This paper develops a multi-agent-based model for online public opinion dissemination in emergency to explore an appropriate crisis information release policy for controlling public panic stemming from hazardous chemicals leakage accidents into rivers. The proposed model makes major contributions to the literature from two perspectives. First, the crisis information about emergency accidents are divided into true and false news based on the truthfulness attribute, and into neutral, positive and negative emotions based on the attitude attribute. Second, the proposed model covers the main agents in the online virtual community.

This article examines the misinformation on the COVID-19 pandemic in social media and electronic media, as well as whether the existing legal administration and laws in Bangladesh, Singapore, and Vietnam are adequate to combat the infodemic. People who believe misinformation and fake news about Coronavirus, prevention, and treatment may put their lives in danger. False information about Coronavirus has spread throughout the world, not just in South and Southeast Asian countries, causing widespread concern in the global healthcare community. We employed a qualitative approach as well as the case study analysis method. Case studies were conducted using news reports and news channels. We examined the legal provisions of the People's Republic of Bangladesh's Constitution, as well as factual analyses of Singapore and Vietnam. We discovered the impact of misinformation dissemination through social and electronic media, which is prevalent not only among rural Bangladeshis but also in almost all classes in Singapore and Vietnam, and how such influence can be detrimental to the interests of Bangladesh, Vietnam, and Singapore.

Berthon and Pitt (2018) recently highlighted the symbiotic relationship between fake news and brands. This paper aims to draw on semiotics to refine the fake/real news dichotomy to a fourfold typology.

First, the authors turn to semiotics and review Greimas’ (1966) semiotic square. Second, they use this framework to refine the fake/real news dichotomy into a four-fold typology. Third, they illustrate each type with a news report on the topic of climate change. Fourth, they apply this framework to reveal four types of brand: real, fake, empty and ironic.

Given that brand communications are heterogeneous, the authors suggest that the typology can be reconceptualized as dimensions and brands communications decoded accordingly. They conclude by exploring further opportunities offered by the semiotic square for interpretive investigation.

The value of the paper lies in the novel use of the semiotic square to shed light on both news and brand communications.

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.

This chapter deals with the question: What is a conspiracy theory? It provides a concept of conspiracy theories and situates conspiracy theories in the context of COVID-19.

In order to understand how COVID-19 conspiracy theories work, one requires a theoretical concept of conspiracy theories. The developed understanding is especially grounded in Frankfurt School critical theory. Section 2 of this chapter works out a critical theory concept of conspiracy theories. Section 3 is an introduction to the communication of COVID-19 conspiracy theories.

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 current natural language processing algorithms are still lacking in judgment criteria, and these approaches often require deep knowledge of political or social contexts. Seeing the damage done by the spreading of fake news in various sectors have attracted the attention of several low-level regional communities. However, such methods are widely developed for English language and low-resource languages remain unfocused. This study aims to provide analysis of Hindi fake news and develop a referral system with advanced techniques to identify fake news in Hindi.

The technique deployed in this model uses bidirectional long short-term memory (B-LSTM) as compared with other models like naïve bayes, logistic regression, random forest, support vector machine, decision tree classifier, kth nearest neighbor, gated recurrent unit and long short-term models.

The deep learning model such as B-LSTM yields an accuracy of 95.01%.

This study anticipates that this model will be a beneficial resource for building technologies to prevent the spreading of fake news and contribute to research with low resource languages.