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Purpose: Mentioning a criminal’s country of origin in crime news is a divisive and much-discussed issue among both journalists and members of society. Scholars assume that mentioning a criminal’s foreign origin could develop and maintain prejudices against individuals with a migrant background among news recipients. However, until now, no attention has been paid to what increases the likelihood that a journalist does or does not mention a criminal’s country of origin when reporting on crimes. Methodology/approach: One possible explanation is that the frequency and intensity of specific news factors could lead to mentioning a criminal’s origin, since increased importance of a news story is usually assigned when many high-intensity news factors occur. Even though numerous studies have determined the frequency of specific news factors in (crime) news, the explanation hypothesized in this chapter has not yet been examined. To investigate this supposition empirically, a quantitative content analysis of four German prime newscasts (n = 290), including public and private broadcasts, was conducted in the current study. Findings: The findings indicate that mentioning criminals’ origins is still common practice in journalism; furthermore, criminals with foreign origins are explicitly represented as foreign almost ten times more often than German-origin criminals are explicitly mentioned as German. News factors such as personalization, location, and influence show some effects of positively predicting journalistic mentioning of a criminal’s country of origin.

The purpose of the study is threefold: first, to identify what factors influence mobile users' willingness of news learning and sharing, second, to find out whether users' learning in the news platforms will affect their sharing behavior and third, to access the impact of sharing intention on actual sharing behavior on the mobile platform.

This study proposes an influence mechanism model for examining the relationship among the factors, news learning and news sharing. The proposed mechanism includes factors at three levels: personal, interpersonal and social level. To achieve this, researchers collected data from 474 mobile news users in China to test the hypotheses. The tools SPSS 26.0 and AMOS 23.0 were used to analysis the reliability, validity, model fits and structural equation modeling (SEM), respectively.

The findings indicate that news learning on the mobile platforms is affected by self-efficacy and self-enhancement. And news sharing intention is influenced by self-efficacy, interpersonal trust, interpersonal reciprocity, online community identity and social norms positively. News sharing intention has a significant effect on news sharing behavior, but news learning has an insignificant relationship with new sharing.

This study provides practical guidelines for mobile platform operators and news media managers by explicating the various factors of users' engagement on the news platforms. This paper also enriches the literature of news learning and news sharing on mobile by the integration of two theories: the social ecology theory and the interpersonal behavior theory.

Extant literature suggests that the difficulty associated with the interpretation of macroeconomic news announcements by the market in general in different economic environments, might be the reason why most studies do not find any significant relationship between real-sector macroeconomic variables and financial asset returns. This paper aims to use a different approach to measure macroeconomic news. The objective is to examine if a different measure of a macroeconomic news variable, constructed from media coverage of the same, significantly affects hedge fund returns.

The authors use a news index for unemployment, which is a real-sector variable, constructed from newspaper coverage of unemployment announcements and examine its impact on hedge fund returns.

Contrary to the other studies that examine the impact of macroeconomic news on hedge fund returns, the authors find that media coverage of unemployment news announcements significantly affects hedge fund returns.

Overall, this paper demonstrates that the manner in which the market interprets macroeconomic news announcements in different economic environments is probably a more relevant factor for hedge funds and is more likely to impact hedge fund returns. In conjunction with variables – constructed from media coverage of unemployment news announcements – that factor in the manner of interpretation, it is found that surprises also matter for hedge fund returns. This is an important consideration for hedge fund managers as well.

To the best of the authors’ knowledge, this is the first study that examines the impact of media coverage of macroeconomic news announcements on hedge fund returns and finds significantly different results with real-sector macro variables.

Social media allow for observing different aspects of human behaviour, in particular, those that can be evaluated from explicit user expressions. Based on a data set of posts with user opinions collected from social media, this paper aims to show an insight into how the readers of different news portals react to online content. The focus is on users’ emotions about the content, so the findings of the analysis provide a further understanding of how marketers should structure and deliver communication content such that it promotes positive engagement behaviour.

More than 5.5 million user comments to posted messages from 15 worldwide popular news portals were collected and analysed, where each post was evaluated based on a set of variables that represent either structural (e.g. embedded in intra- or inter-message structure) or behavioural (e.g. exhibiting a certain behavioural pattern that appeared in response to a posted message) component of expressions. The conclusions are based on a set of regression models and exploratory factor analysis.

The findings show and theorise the influence of social media content on emotional user engagement. This provides a more comprehensive understanding of the engagement attributed to social media content and, consequently, could be a better predictor of future behaviour.

This paper provides original data analysis of user comments and emotional reactions that appeared on social media news websites in 2018.

Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms.

A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles’ semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users’ online recommendation lists based on their current news browsing.

This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation.

The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms’ commercial value.

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.

The collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.

This study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.

As the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.

Accounting literature is replete with quantitative models that use financial ratios to identify the probability of a going concern qualification. These studies, however, ignore qualitative cues that auditors use to identify going concern problems and mitigating factors (sound financial plans etc.) that auditors take into account in their choice of report. Tests whether, in the presence of financial distress, non‐financial cues play an important role in auditors’ choice. Results indicate that non‐financial variables can be used to discriminate between the auditor’s decision to issue the going concern qualified versus the clean report. Helps company management understand how auditors evaluate their clients and the importance of the qualitative criteria used in their evaluation. Can be used to predict the most probable outcome prior to the external audit. Second, facilitates understanding of the non‐financial red flags that could trigger the going concern report. Third, can be used to analyze potential acquisition targets, and, if the acquisition target is still otherwise desirable, be used in pricing negotiations. Fourth, can be applied to aspects of the firm’s own division’s operations in order to enable the internal audit department to better allocate its own investigational and problem‐solving resources. Finally, the fact that qualitative factors have power in predicting the going concern modified report suggests that company decision makers can evaluate others even if the auditor for political or other reasons has chosen not to render a modified report.

The spread of fake news on social networking sites (SNS) poses a threat to the marketing landscape, yet little is known about how fake news affect consumers’ perceptions, attitudes and behaviors. This study aims to explore when consumers believe fake news, whom they blame for it (e.g. negative attitudes toward brands or SNS) and when they choose to share it.

Data obtained from 80 open-ended, semistructured interviews, conducted with SNS consumers and experts, is analyzed following the principles of grounded theory and the Gioia methodology.

Factors affecting consumers’ perceptions of fake news include skepticism, awareness, previous experience, appeal and message cues. Consumers’ brand- and SNS-related attitudes are affected by consumers’ blame, which is determined by consumers’ perceptions of the vetting efforts, role and ethical obligation of SNS. Consumers’ motives for sharing fake news include duty, retaliation, authentication and status-seeking. Theoretical and practical implications derived from the study’s novel conceptual framework are discussed.

This study identifies communication strategies that marketing professionals can use to mitigate and counter the negative effects of fake news.

By simultaneously considering consumers’ perceptions of the source, information and medium (i.e. SNS), this study presents a novel conceptual framework providing a marketing-centered, dynamic view on consumers’ fake news experience and connecting consumers’ perceptions, attitudes and behaviors in the context of fake news.

This study aims to understand how fake news can cause an impact on consumer behavioral intentions in today’s era when fake news is prevalent and common. Brands have not only faced reputational losses but also got a dip in their share prices and sales, which affected their financial standing. Hence, it is significant for brands to understand the impact of fake news on behavioral intentions and to strategize to manage the impact.

This study uses several branding and marketing concepts such as brand experience, brand trust, brand credibility, consumer behavioral intentions along with variables suggested by Elaboration Likelihood Model and Heuristic Systematic Model such as personal relevance/involvement. For fake news, news truthfulness, news credibility and source credibility are used.

The results of this study shows that positive brand experience, brand trust, brand credibility help in creating positive behavioral intentions for brands. This study shows that brands focusing on providing positive brand experience have a stronger brand trust and credibility and are affected less by fake news than those brands which do not emphasize on these factors.

This paper can assist brand managers in understanding the impact fake news can have on behavioral intentions of consumers. The managers can strategize such that the fake news affects their brands the least.

The authors in this paper attempt to fill in the gap in literature, which is to study how the fake news impacts the brands considering the credibility, trust and experience they establish with their customers. The existing literature discusses the generation and dissemination of fake news on social media and its impact on political scenarios and personalities. Also, studies explain the impact of fake news on the financial position of brands, but marketing facets are not tested empirically.

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.