Search

Although the COVID-19 pandemic is no longer considered a public health emergency of international concern, research is ongoing to determine whether and how financial markets have been affected. Using an event study methodology, we examine Chinese stock reactions to breaking news about COVID-19 during 2020-2023, covering nine important events from the first declaration of the novel coronavirus to classifying COVID-19 from top category A to B in China. The empirical results show that the Chinese stock market reacts negatively to bad news, such as the breakout of COVID-19 and the dynamic zero-COVID strategy, and positively to good news, such as COVID-19 vaccine approvals, ending the zero-COVID strategy, and reopening boards. The overall intensity of reactions to bad news is stronger than that to good news. Small-sized stocks react more positively to good news and more negatively to bad news. Different industries have different reactions to different events; however, the sector that responds strongly and negatively to bad news does not have to respond strongly and positively to good news, which, to some extent, reflects their profitability performance during the pandemic. In China, consumer discretionary, utilities and real estate are the most negatively affected by the pandemic, while materials, health care, and energy are the most positively affected. This study has important implications for reminding market participants to pay attention to breaking news announcements about COVID-19., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Motivated by the growing convergence between news media and social media as dominant sources of information dissemination, this study examines the connection between textual sentiment and stock returns. Previous studies have examined the effect of sentiment extracted from these two sources on stock returns independently, without modelling how one source can confound the relationship between stock returns and the other source. We investigate this using data from four markets (USA, UK, South Africa and Brazil) and a sample period stretching from January 2016 to April 2023. Employing a suite of methods that encompass both simple parametric techniques and complex models designed to address nonlinearity, chaos and deviations from normality, the analysis uncovers a pronounced impact of social media sentiment on stock returns in the United States. This influence overshadows the effect of news media sentiment across the employed methods. Interestingly, in other markets, news media exhibits a greater effect on stock returns compared to social media sentiment. By emphasising the convergence of news media and social media, the study highlights the important interplay between these sources, offering valuable insights into understanding the complex dynamics of modern financial markets., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

This study explores how the political interests of different ethnic groups influence mainstream televisions' news discourses in Ethiopia. It was based on data generated from a corpus of 1167 news stories in three TV channels from 2019 to 2021 and analyzed stories threatening multiculturalism. Based on the theoretical lenses of critical theory, it employed a Discourse Historical Approach to analyze discursive strategies used in TV news. It underscores that in a diverse society like Ethiopia, achieving public consensus through news discourse might become tougher. The results also show that recent political polarization, conflicts, and civil wars have potentially compromised the objectivity of news reporting, undermining the utilization of inclusive language for representing diversity. Newsrooms employ antagonistic narratives that impede the cultivation of tolerance and respect among diverse ethnic and political factions. Consequently, these narratives cunningly neutralize the political undertones of ethnic conflicts by labeling the culprits as terrorists. Hostile mode of discourse perilous to the enactment of multiculturalism characterizes the news discourses in TV channels. While discussing political conflicts and insecurities, the news stories intensify divisions among diverse political groups. Therefore, the study suggests that news reporting in a diverse society ought to focus on peace bargaining, the use of impartial and inoffensive discourses to lay the groundwork for multicultural integration., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The present study delves into the underexplored nexus between online real estate news and societal welfare news. A novel methodological approach is adopted herein: web scraping techniques amass online newspaper articles, the sentiment of which is subsequently evaluated through a neural network-informed natural language processing framework. The sentiment-oriented data is transitioned into a panel data configuration for deeper analysis. Results indicate a prevailing negative link between real estate news sentiments and those of welfare news. Intriguingly, the ascension of the conservative party amplifies this negative correspondence. This paper augments the political economy and political communication discourse by underscoring the conceivable potential influence of digital media framing on shaping public perceptions and political inclinations. It offers pertinent insights regarding the interrelation between housing wealth and political outcomes in a distinct national milieu., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author.)

News dissemination is an important way for people to obtain information. With the development of new technologies, traditional news dissemination models have been impacted. It has problems with information filtering and bias, and has certain limitations in news quality, dissemination efficiency, etc., which makes it difficult to effectively meet people's information needs. In order to improve the quality and efficiency of news dissemination, promote the positive impact of news dissemination on society, this article combined artificial intelligence and big data technology to conduct in-depth research on the difficulties and coping strategies of news dissemination. This article first analyzed the characteristics and functions and influencing factors of news dissemination, then provided an overview of the difficulties and coping strategies in news dissemination. Finally, using association rule algorithms, personalized recommendations for news dissemination are achieved. To verify the effectiveness of artificial intelligence and big data in coping with the dilemma of news dissemination, this article conducted experimental analysis from the perspectives of news content quality, dissemination efficiency, objectivity, and dissemination cost. The experimental results show that under the application of news dissemination strategies based on artificial intelligence and big data, the quality of news content and dissemination efficiency have been improved by 4.76 % and 3.63 %, respectively. The conclusion indicates that artificial intelligence and big data can help improve the quality and dissemination efficiency of news content, and meet the diverse needs of the public for information., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author.)

In the digital age, where information is a cornerstone for decision-making, social media's not-so-regulated environment has intensified the prevalence of fake news, with significant implications for both individuals and societies. This study employs a bibliometric analysis of a large corpus of 9678 publications spanning 2013-2022 to scrutinize the evolution of fake news research, identifying leading authors, institutions, and nations. Three thematic clusters emerge: Disinformation in social media, COVID-19-induced infodemics, and techno-scientific advancements in auto-detection. This work introduces three novel contributions: 1) a pioneering mapping of fake news research to Sustainable Development Goals (SDGs), indicating its influence on areas like health (SDG 3), peace (SDG 16), and industry (SDG 9); 2) the utilization of Prominence percentile metrics to discern critical and economically prioritized research areas, such as misinformation and object detection in deep learning; and 3) an evaluation of generative AI's role in the propagation and realism of fake news, raising pressing ethical concerns. These contributions collectively provide a comprehensive overview of the current state and future trajectories of fake news research, offering valuable insights for academia, policymakers, and industry., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

The growth of digital media usage has accelerated the development of big data technology. According to the agenda-setting theory, news media inform the public regarding major agendas and business cycles. This study investigated 168,786 news documents from 2016 to 2020 related the South Korea fashion business using Python. A total of 19 topics were extracted through latent Dirichlet allocation and then transformed into structured data using a time series approach to analyze significant changes in trends. The results indicate that major fashion industry topics include business management strategies to increase sales, diversification of the retail structure, influence of CEOs, and merchandise marketing activities. Thereafter, statistically significant hot and cold topics were derived to identify the shifts in topic themes. This study expands the fashion business contexts with agenda-setting theory through big data time series analyses and can be referenced for the government agencies to support fashion industry policies., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

In light of the significance of regulatory authorities and the rising demand for information disclosure, a vast amount of information on food safety news reports is readily accessible on the Internet. The extraction of such information for precise classification and provision of appropriate safety alerts based on their respective categories has emerged as a challenging problem for academic research. Given that most food safety-related events in news reports comprise lengthy text, the pre-trained language models currently employed for text analysis are generally limited in their capability to handle long documents. This paper proposes a long-text classification model utilising hierarchical Transformers. We categorise information in long documents into two distinct types: (1) multiple text chunks meeting the length constraint and (2) essential sentences within long documents, such as headings, paragraph start and end sentences, etc. Initially, our proposed model utilises the text chunks as input to the BERT model. Then, it concatenates the output of the BERT model with the important sentences from the document and use them as input to the Transformer model for feature transformation. Finally, we utilise a classifier for food safety news classification. We conducted several comparative experiments with various commonly used text classification models on a dataset constructed from publicly available information on food regulatory websites. Our proposed method outperforms existing methods, establishing itself as the leading approach in terms of performance., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

The rapid development of the Internet and Internet of Things has rapidly introduced human society into the information age, and the way of fake news production has been updated, which has greatly affected the normal life of human beings. In order to identify worthless fake news and trace massive fake news data from unknown sources, and share valuable news data to fully disseminate effective real news, news owners usually store news data in cloud. Users of IoT terminals can access news data on demand without storing it locally. However, the authenticity of the fictive newspaper numbers source, which is easy to destroy, and the social media platform. Besides, when massive news data is saved on cloud server, the news owners have to at the risk of lose physical control over news data and it will face the risk of fake news being disseminated and real news being falsified. Thus, this paper proposes a novel mechanism for secure storage of news data using blockchain technology. Firstly, traceability and verification of fake news data is improved by the cooperative storage model on and off the chain. Secondly due to the inability of past polynomial commitment to update the commitment, we will be a hindrance to use polynomial commitment to build a secure authentication protocol. Therefore, in this paper, we design the update algorithm for polynomial commitment in order to be able to guarantee the consistency of on-chain and blockchain database news data., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Published by Elsevier Ltd.)

This paper aims to investigate the impact of global financial, economic, and gold price uncertainty indices (VIX, EPU, and GVZ) and investor sentiment based on media coverage news on the returns of Bitcoin and Ethereum during the COVID-19 pandemic. We adopt an asymmetric framework based on the Quantile-on-Quantile approach, which examines the quantiles of the cryptocurrency returns, investor sentiment, and the various uncertainties indicators. The empirical findings suggest that the COVID-19 pandemic has significantly impacted cryptocurrency returns. Specifically, (i) the results demonstrate the predictive power of Economic Policy Uncertainty (EPU) during this period, as evidenced by a strong negative association between EPU and cryptocurrency returns across all quantiles; ( ii ) the correlation between cryptocurrency returns and the VIX index was negative but weak, across various quantile combinations of Ethereum and Bitcoin returns; ( iii ) an increase in COVID-19 news negatively affected Bitcoin returns across all quantiles; ( iv ) Bitcoin and Ethereum cannot be relied upon as effective hedging tools against global financial and economic uncertainty during the COVID-19 pandemic. Studying the behavior of cryptocurrency during uncertainty like pandemics is extremely important because it provides investors with insights on diversifying their portfolios and hedging their risks., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (© 2023 The Authors.)