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This study aimed to investigate whether information from reliable news sources such as medical experts and government officials, along with governmental and individual risk responses, influences consumers’ perceptions of news and intention to seek more information. Additionally, it aimed to explore the relationships between these perceptions and consumers’ intentions to seek information in a food safety risk event.

A survey design methodology was employed. A quasi-experimental approach divided 470 Taiwanese participants into three groups, each exposed to varying online news content about food safety news, designed according to the Internalization-Distribution-Explanation-Action (IDEA) model. This involved different combinations of reliable sources and risk response advice to examine the impact on news comprehension and behaviour intentions.

The results indicated that consumers perceived the news as highly credible when they read it with reliable news sources or risk response advice. Governmental and individual risk response advice significantly impacted consumers’ understanding of news. In addition, perceptions of news credibility and understanding of news can increase individuals’ information-seeking intentions to protect themselves from food safety risks.

This study introduced novel insights into the application of the source credibility theory (SCT) model within Taiwanese food safety incidents, identifying key factors that motivate consumer information-seeking behaviour. It marks an initial attempt to incorporate the IDEA model-based risk communication content into research design, aligning with existing literature while highlighting the critical role of reliable sources in enhancing news credibility and consumer response.

The burgeoning literature on climate-related finance suggests that climate change influences financial markets. Building on this foundation, the present study aims to investigate the time-varying predictive power of news related to physical and transition climate risks for financial instability across the financial systems of the US, EU, and the ASEAN+3 countries (comprising the Association of Southeast Asian Nations plus China, Japan, and South Korea), from January 2003 to August 2022, on a monthly basis.

In this study, we use the VAR-based Granger-causality test in the presence of instabilities introduced by Rossi and Wang (2019), and combine it with the innovative rolling and recursive bootstrap time-varying Granger-causality approach of Shi et al. (2020). These methods were chosen for their capacity to effectively capture the dynamic influence of climate risk-related news on financial instability over time, offering an advantage over traditional constant parameter regressions and standard Granger causality methods. Additionally, we make use of the Media Climate Change Concerns indices recently developed by Ardia et al. (2022), coupled with regional financial stress indices.

Our findings indicate that the predictive power of climate change news for financial instability is substantial but varies over time. This influence becomes especially pronounced during periods that align with specific local and global events. In the US and EU, the predictive power is influenced by a combination of global and local macroeconomic, political, health, and climate-related factors. In contrast, ASEAN+3 financial systems show a stronger response to regional and local events, with comparatively less sensitivity to global events.

The results of this study are noteworthy for investors, highlighting increased market instability during periods with prevalent climate change news. Investors can adjust their strategies to mitigate risks and respond to macro-events that trigger climate news-related market instability, while considering regional sensitivities. Similarly, these findings are significant for policymakers, emphasizing the need to consider the influence of climate news on financial markets when designing regulatory frameworks. This could involve enacting measures to stabilize the financial system during periods of significant climate news. Policymakers might consider developing macroprudential regulations to bolster financial institutions’ resilience against climate change news effects.

This study pioneers the exploration of how climate change news affects financial system stability at the macro level. It extends beyond traditional research, typically focusing on direct effects of climate change in banking and asset markets, by examining broader implications of climate risk-related news for financial system instability. Furthermore, this study enhances our understanding of the predictors of global financial stability by examining the financial systems of the US, the EU, and ASEAN+3. It specifically investigates the impact of climate change news, a topic not extensively explored in previous research focusing mainly on macro-factors such as financial liberalization and business cycles.

The objective is to develop a more effective model that simplifies and accelerates the news classification process using advanced text mining and deep learning (DL) techniques. A distributed framework utilizing Bidirectional Encoder Representations from Transformers (BERT) was developed to classify news headlines. This approach leverages various text mining and DL techniques on a distributed infrastructure, aiming to offer an alternative to traditional news classification methods.

This study focuses on the classification of distinct types of news by analyzing tweets from various news channels. It addresses the limitations of using benchmark datasets for news classification, which often result in models that are impractical for real-world applications.

The framework’s effectiveness was evaluated on a newly proposed dataset and two additional benchmark datasets from the Kaggle repository, assessing the performance of each text mining and classification method across these datasets. The results of this study demonstrate that the proposed strategy significantly outperforms other approaches in terms of accuracy and execution time. This indicates that the distributed framework, coupled with the use of BERT for text analysis, provides a robust solution for analyzing large volumes of data efficiently. The findings also highlight the value of the newly released corpus for further research in news classification and emotion classification, suggesting its potential to facilitate advancements in these areas.

This research introduces an innovative distributed framework for news classification that addresses the shortcomings of models trained on benchmark datasets. By utilizing cutting-edge techniques and a novel dataset, the study offers significant improvements in accuracy and processing speed. The release of the corpus represents a valuable contribution to the field, enabling further exploration into news and emotion classification. This work sets a new standard for the analysis of news data, offering practical implications for the development of more effective and efficient news classification systems.

The modern high-choice news environment has changed the way people consume news. We examined the structure of news repertories, the role of news trust (and cynicism) in shaping news repertoires, and the effects of different news repertoires on political participation in the South Korean news environment, where news portals function as major news sources.

We conducted a two-wave national survey of adults in January and February 2022 with a three-week time interval. Latent profile analyses and latent profile regression were employed.

Four major news repertoires were identified: news portal-concentrated, portal and TV combination, all except print and SNS, and platform omnivores. Trust in mainstream news media was found to be high in the following order: platform omnivores, all except print and SNS, portal and TV combination, and portal-concentrated repertoires. Cynicism about mainstream media was found to be higher among the users of portal-concentrated repertoire than those using the other three repertoires. Those who rely mainly on news portals for news (i.e. portal-concentrated) were less likely to participate in politics.

This study provides comprehensive insights into how audience perceptions of trust and cynicism toward mainstream news media shape news consumption patterns in today’s high-choice media environment. In particular, the findings of this study illuminate the function of news portals as a substitute platform for those who exhibit lower trust and higher cynicism toward mainstream news media.

In the past few years, research focusing on misinformation, referred to broadly as fake news, has experienced revived attention. Past studies have focused on explaining the ways in which people correct it online and on social media. However, fewer studies have dealt with the ways in which people are able to identify fake news (i.e. fake news literacy). This study contributes to the latter by theoretically connect people’s general social media use, political knowledge and political epistemic efficacy with individuals’ fake news literacy levels.

A diverse and representative two-wave panel survey in the United States was conducted (June 2019 for Wave 1, October 2019 for Wave 2). We performed cross-sectional, lagged and autoregressive regression analyses to examined how social media us, people’s political knowledge and political epistemic efficacy are related to their fake news literacy.

Results suggest that the more people used social media, were politically knowledgeable and considered they were able to find the truth in politics (i.e. epistemic political efficacy), the more likely they were to discern whether the news is fake. Implications of helping media outlets and policy makers be better positioned to provide the public with corrective action mechanisms in the struggle against fake news are discussed.

The measurement instrument employed in the study relies on subjects’ self-assessment, as opposed to unobtrusive trace (big) digital data, which may not completely capture the nuances of people’s social media news behaviors.

This study sheds light on how the way people understand politics and gain confidence in finding political truth may be key elements when confronting and discerning fake news. With the help of these results, journalists, media outlets and policymakers may be better positioned to provide citizens with efficient, preemptive and corrective action mechanisms in the struggle against misinformation.

Recent literature highlights the importance of literacy education to contest fake news, but little is known about what specific mechanisms would contribute to foster and reinvigorate people’s fake news literacy. This study helps address this gap.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2024-0140

This paper aims to explain how consumer persuasion knowledge and perceived deception in advertisements can influence consumers’ future evaluation of fake news about a brand.

This research develops a conceptual model using widely used persuasion knowledge theory and confirmation bias theory. A questionnaire-based online survey (n = 410) was conducted by displaying an advertisement stimulus followed by a fake news stimulus to test the model. Covariance-based structural equation modeling was used to analyze the hypothesized research model.

The results demonstrate that consumers with high persuasion knowledge are more likely to trust and adopt fake news about an advertised brand through the mediation of perceived deception in the advertisement. Additionally, perceived deception indirectly affects information adoption through the mediation of news credibility.

Theoretically, this study contributes to the existing body of literature on advertising deception and fake news. This research also extends theory of persuasion knowledge in understanding adoption of fake news. Practically, this study has significant implications for various stakeholders, including brands, social media corporations and consumers.

This research adds novel insights in the relationship of consumers’ persuasion knowledge and credibility and adoption of fake news. Furthermore, the investigation of the relationship between the perceived deception in advertising and the adoption of fake news has not been explored, which is also novel.

We examine the relationship between macroeconomic news and fund price jumps, using high-frequency 5-min intraday data for Exchange Traded Funds (ETFs) and Listed Open-end Funds (LOFs) from 2019 to 2020.

We utilize the non-parametric jump test known as the LM method to detect fund price jumps. In addition, we perform Logistic regression to analyze the relationship between macroeconomic news and fund price jumps. Moreover, we use multiple linear regression to explore the relationship between fund price jumps and subsequent returns.

The probability of price jumps increases by 22.56% when macroeconomic news is released. Moreover, the returns associated with news-driven price jumps display a reversal pattern, and there is an asymmetric relationship in subsequent returns following macroeconomic shocks. Specifically, funds tend to exhibit lower returns after news-driven price jumps compared to those that are not influenced by news events.

In today's digital age, investors have unprecedented access to a wealth of information through the Internet and various communication platforms. News and market data can be instantly accessed and disseminated, allowing for swift dissemination of information to investors worldwide. However, despite this enhanced accessibility, investors continue to exhibit overreactions or underreactions to new information.

Macroeconomic news release provide crucial insights into the overall health and performance of the economy. By monitoring and analyzing these indicators, investors can gain valuable information that can guide their investment decisions. Furthermore, by fostering a transparent and reliable information disclosure systems, governments can play a critical role in ensuring the stability and transparency of the funds market.

The paper utilizes 5-min high-frequency data from funds and incorporates a comprehensive macroeconomic news information database. These methodological choices enhance the precision and reliability of the analysis, allowing for a more nuanced understanding of the relationship between macroeconomic news releases and fund price jumps.

Most new media research focuses on behavior as a measure of engagement, while the psychological state of being occupied with its content has received little attention. This study examined news engagement beyond pure action observation by exploring young people’s psychological experiences with the news.

The study carried out a digital native’s survey on 212 people (18–28 years). The focus of the survey was on understanding individuals’ engagement with online news using affective and cognitive components. The authors compared the influence of each type of engagement on youth consumption of and attitudes toward online news.

The results of the hierarchical regression analysis showed that affective engagement can be a stronger predictor of online news consumption than cognitive engagement. While affective engagement significantly predicts positive attitudes toward online news, cognitive engagement had no significant effect.

These findings suggest that “engaging the heart” is more influential than “engaging the mind” in drawing young people to the news in today’s information environment. The study thus contributes to the understanding of the cognitive and emotional focus on news content and their importance in shaping young people’s expectations of online news. The findings from this study could have broader implications for future trends in online news consumption.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

Fake news stories have become a central element of crises that corporate public relations practitioners have confronted. Whether such stories are rumors, outright lies or deliberate attempts to discredit corporations, they have the same impact and require specific strategies for public relations practitioners to effectively respond. The purpose of this study is to uncover strategies to manage crises that arise from fake news and if and how these strategies differ for other corporate crises.

In this multi-method study of 21 in-depth interviews and a 8-person focus group with senior-level corporate public relations practitioners, authors explored decision-making strategies for responding to fake news crises. Transcripts of interviews and the focus group were thematically analyzed.

Results reveal insights regarding how public relations practitioners determine if and when to respond to fake news crises in corporations; what response strategies public relations practitioners have the autonomy to employ for fake news crises in corporations, and how public relations practitioners control media narratives during fake news crises in corporations.

The findings guide public relations practitioners to craft an autonomous decision-making process and effective online listening strategies—establishing a watchful waiting approach—and determine if the fake news issue is a passing moment or movement swirling into a crisis.

Few studies have examined the perspectives of crisis communication experts about minimizing and managing fake news crises. The study identifies opportunities for future research focused on crises originating from fake news and disinformation.