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Online news and information sources are convenient and accessible ways to learn about current issues. For instance, more than 300 million people engage with posts on Twitter globally, which provides the possibility to disseminate misleading information. There are numerous cases where violent crimes have been committed due to fake news. This research presents the CovidMis20 dataset (COVID-19 Misinformation 2020 dataset), which consists of 1,375,592 tweets collected from February to July 2020. CovidMis20 can be automatically updated to fetch the latest news and is publicly available at: https://github.com/everythingguy/CovidMis20. This research was conducted using Bi-LSTM deep learning and an ensemble CNN+Bi-GRU for fake news detection. The results showed that, with testing accuracy of 92.23% and 90.56%, respectively, the ensemble CNN+Bi-GRU model consistently provided higher accuracy than the Bi-LSTM model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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A number of the people who have recovered from the acute effects of COVID-19 are facing long term sequelae from the infection. As the COVID-19 pandemic is still evolving, so is knowledge of the long-term effects of the virus on patients who still experience symptoms. Clearly, news media play a crucial role in distributing information and this distribution of information can, in turn, influence the actions of the public. The purpose of this study was to describe the content of news coverage of COVID-19 long haul symptoms currently posted on the internet. This study utilized Google News, a news aggregator service, and included the first 100 English language pieces of news. Video content and news article content were coded in depth for information on COVID-19 long haul symptoms. A total of 41% of news reports mentioned the length of time that the COVID-19 related symptoms persist. The length of time was reported to range from 1 month to more than 1 year. The symptom most commonly mentioned was tiredness or fatigue (74%), followed by difficulty breathing or shortness of breath (62 cases; 62%), and difficulty thinking or concentrating (50 cases; 50%). Other symptoms were mentioned less frequently. There were no statistically significant differences in any of the content including having video, written news reports, or both video and written news reports by source of the news reports based on consumer, professional, or television or internet-based news (p = .14). More complete coverage by online news media of the long-term effects of COVID-19 enhances public awareness of the post-acute syndromes, augments health providers' awareness of the range of chronic COVID-19 effects and the possibility of a second infection, increases the probability of patients' seeking and obtaining the proper care for their symptoms, and contributes to preventive actions for enhancing public health.
Subject(s)
COVID-19 , Humans , Mass Media , Pandemics , SARS-CoV-2 , TelevisionABSTRACT
The novel Coronavirus (Covid-19) has badly affected individuals and organizations all around the globe. There are many efforts undertaken by organizations to assist governments in combating the pandemic. This include spending funds to curb the Coronavirus which is the corporate social responsibility (CSR) activities. This paper aims to investigate the extent of CSR activation by Malaysian companies in battling the Covid-19 pandemic via content analysis of online news during the implementation of the Movement Control Order by the government using the legitimacy theory and coercive isomorphism under the umbrella of institutional theory. The analysis includes the examination of online news which was captured from a Google search from a period 18 March to 17 May 2020. There were 95 online news captured during the period. The results found out that the types of CSR activation were classified into: monetary, non-monetary and both. It is also found that the CSR activation reported on online news is part of the companies' legitimation strategies in addition to the on-going business's marketing strategies. Several implications and limitations are also provided in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Class imbalance is an important classification problem where failure to identify events can be hazardous due to failure of solution preparation or opportune handling. Minorities are mostly more consequential in such cases. It is necessary to know a reliable classifier for imbalanced classes. This study examines several conventional machine learning and deep learning methods to compare the performance of each method on dataset with imbalanced classes. We use COVID-19 online news titles to simulate different class imbalance ratios. The results of our study demonstrate the superiority of the CNN with embedding layer method on a news titles dataset of 16,844 data points towards imbalance ratios of 37%, 30%, 20%, 10%, and 1%. However, CNN with embedding layer showed a noticeable performance degradation at an imbalance ratio of 1%. © 2022 IEEE.
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The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models.
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This essay explores the news media's portrayal of humor during the early phase of COVID-19-related lockdowns. Examining a collection of online news articles reveals the media tended to frame the issue as an ethical one (e.g., "is it okay to laugh at the coronavirus?"). After reviewing work on humor ethics, a qualitative content analysis of 20 news media articles is presented. Three issues from the news stories are identified, allowing comparison of the media's claims against the ethical principles articulated. The essay concludes with a consideration of how news media's coverage of humor fits within a broader pandemic narrative. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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The impact of technology on people's lives has grown continuously. The consumption of online news is one of the important trends as the share of population with internet access grows rapidly over time. Global statistics have shown that the internet and social media usage has an increasing trend. Recent developments like the Covid 19 pandemic have amplified this trend even more. However, the credibility of online news is a very critical issue to consider since it directly impacts the society and the people's mindsets. Majority of users tend to instinctively believe what they encounter and come into conclusions based upon them. It is essential that the consumers have an understanding or prior knowledge regarding the news and its source before coming into conclusions. This research proposes a hybrid model to predict the accuracy of a particular news article in Sinhala text. The model combines the general news content based analysis techniques using machine learning/ deep learning classifiers with social network related features of the news source to make predictions. A scoring mechanism is utilized to provide an overall score to a given news item where two independent scores- Accuracy Score (by analyzing the news content) and Credibility Score (by a scoring mechanism on social network features of the news source) are combined. The hybrid model containing the Passive Aggressive Classifier has shown the highest accuracy of 88%. Also, the models containing deep neural netWorks has shown accuracy around 75-80%. These results highlight that the proposed method could efficiently serve as a Fake News Detection mechanism for news content in Sinhala Language. Also, since there's no publicly available dataset for Fake News detection in Sinhala, the datasets produced in this work could also be considered as a contribution from this research. © 2022 IEEE.
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OBJECTIVES: Globally, adherence to COVID-19 health and safety protocols played a crucial role in preventing the spread of the virus. Thus, this study analyzed online news articles reporting adherence to COVID-19 health and safety protocols in the Philippines. DESIGN: Manifest content analysis. SAMPLE: News articles (n = 192) from three major online news portals in the Philippines. MEASUREMENT: Published online news articles were collected during the peak of the COVID-19 pandemic (March 2020 to March 2021). Bengtsson's content analysis approach was used to analyze the data. Member-checking and intercoder reliability validated the study's results. RESULTS: Three main themes emerged: (a) adherence, (b) non-adherence, and (c) partial adherence. The subthemes were labeled who, what, when, where, and why. The same behavior, social distancing, was the most adhered to and non-adhered COVID-19 health protocol. This protocol has the highest occurrences in political protest, religious-related activities, and self-initiated quarantine. Leisure activities both showed non-adherence and partial adherence. CONCLUSIONS: Online news articles depicted Filipinos' adherence to health and safety protocols. Their adherence was primarily determined by one's group or community, social norms, and values. The government and its public health agencies should strengthen current efforts and continuously re-evaluate existing policies to modify ineffective and confusing safety health protocols.
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COVID-19 , Humans , COVID-19/prevention & control , COVID-19/epidemiology , SARS-CoV-2 , Pandemics/prevention & control , Philippines , Reproducibility of ResultsABSTRACT
Accurate prediction of oil consumption plays a dominant role in oil supply chain management. However, because of the effects of the coronavirus disease 2019 (COVID-19) pandemic, oil consumption has exhibited an uncertain and volatile trend, which leads to a huge challenge to accurate predictions. The rapid development of the Internet provides countless online information (e.g., online news) that can benefit predict oil consumption. This study adopts a novel news-based oil consumption prediction methodology-convolutional neural network (CNN) to fetch online news information automatically, thereby illustrating the contribution of text features for oil consumption prediction. This study also proposes a new approach called attention-based JADE-IndRNN that combines adaptive differential evolution (adaptive differential evolution with optional external archive, JADE) with an attention-based independent recurrent neural network (IndRNN) to forecast monthly oil consumption. Experimental results further indicate that the proposed news-based oil consumption prediction methodology improves on the traditional techniques without online oil news significantly, as the news might contain some explanations of the relevant confinement or reopen policies during the COVID-19 period.
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BACKGROUND: Stigma relating to health can result in a broad range of vulnerabilities and risks for patients and healthcare providers. The media play a role in people's understanding of health, and stigma is socially constructed through many communication channels, including media framing. Recent health issues affected by stigma include monkeypox and COVID-19. OBJECTIVES: This research aimed to examine how The Washington Post (WP) framed the stigma around monkeypox and COVID-19. Guided by framing theory and stigma theory, online news coverage of monkeypox and COVID-19 was analyzed to understand the construction of social stigma through media frames. METHODS: This research used qualitative content analysis to compare news framings in The Washington Post's online news coverage of monkeypox and COVID-19. RESULTS: Using endemic, reassurance, and sexual-transmission frames, The Washington Post predominantly defined Africa as the source of monkeypox outbreaks, indirectly labeled gays as a specific group more likely to be infected with monkeypox, and emphasized that there was no need to worry about the spread of the monkeypox virus. In its COVID-19 coverage, The Washington Post adopted endemic and panic frames to describe China as the source of the coronavirus and to construct an image of panic regarding the spread of the virus. CONCLUSIONS: These stigma discourses are essentially manifestations of racism, xenophobia, and sexism in public health issues. This research confirms that the media reinforces the stigma phenomenon in relation to health through framing and provides suggestions for the media to mitigate this issue from a framing perspective.
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COVID-19 , Monkeypox , Male , Humans , Mass Media , Washington , Social StigmaABSTRACT
Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy: 95.16%, precision: 94.71%, recall: 94.32%, F1-score: 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners.
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BACKGROUND: The SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored. OBJECTIVE: The main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources. METHODS: We first used core terms and symptom-related keyword-based methods to extract COVID-19-related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis. RESULTS: Our proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t198=-8.722, P<.001; model 2, t198=-5.000, P<.001, model 3, t198=-1.882, P=.06; model 4, t198=-4.644, P<.001; model 5, t198=-4.488, P<.001). In Hubei province, our proposed model's forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t198=-1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks. CONCLUSIONS: Our approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.
Subject(s)
COVID-19 , Epidemics , Social Media , COVID-19/epidemiology , Disease Outbreaks , Humans , SARS-CoV-2ABSTRACT
Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy: 95.16%, precision: 94.71%, recall: 94.32%, F1-score: 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners. © 2023 Institute of Advanced Engineering and Science. All rights reserved.
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This study aimed to find the overall issues of unmanned stores from the media in South Korea. For analysis, 5,261 online news articles published between 2018 to 2021 were collected. As an overview, we analyzed the annual word frequency. The results showed emerging terms related to the COVID-19 after 2020. Then, latent dirichlet allocation (LDA) topic modeling analysis was conducted to explore the agendas of unmanned stores, and as a result, 12 topics were derived. The results showed that most topics focused on the various types of unmanned stores including specific franchise brand names. In addition, as a structural aspect of the unmanned store field, agendas such as business prospects, major countries, and leading companies in unmanned stores were derived. Furthermore, social issues such as the spread of COVID-19 and crime cases were found. The results of this study provide an understanding of the various agendas related to unmanned stores. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Understanding the relationship between online media and vaccine-related information is essential for public inoculation strategies. Despite the advent of automated methods for this purpose, there is a gap in terms of applying Natural Language Processing techniques (NLP) to understand information regarding COVID-19 vaccines in Romanian online news. In this sense, this pilot study aims to close the gap by using NLP techniques to analyze information related to vaccines in online news articles. A corpus of 5,670 vaccine-related online news articles published between January and December 2021 was analyzed using sentiment and word cloud analyses to understand the valence and content of COVID-19 vaccine -related information. The results indicate the utility of the proposed method for public and private actors, as well as further required efforts for using NLP techniques to understand and monitor information regarding vaccines present in Romanian online news articles.
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Misinformation exposure has attracted growing scholarly attention. While much research highlights misinformation exposure's negative impacts, this study argues that its positive effects should also be noted. By using a more precise measurement of misinformation exposure and a path model, this study outlines a path from misinformation exposure to anti-misinformation behaviors, partially mediated by misperceptions in the context of COVID-19. Findings indicate that exposure to popular but widely-denounced COVID-19 misinformation via social media had positive effects on verification intention. Frequent exposure to misinformation on social media is associated with lower misperceptions, suggesting a healthy dose of skepticism toward the platform and low internalization of misinformation. Special attention, however, needs to be paid to online news websites and personal contacts as misinformation sources. More tailored interventions and communication strategies to reduce misperceptions and increase media-literate behaviors are needed for younger, conservative, and ethnic minority individuals. Theoretical and practical implications are further discussed.
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In the last years, there has been an ever-increasing interest in profiling various aspects of city life, especially in the context of smart cities. This interest has become even more relevant recently when we have realized how dramatic events, such as the Covid-19 pandemic, can deeply affect the city life, producing drastic changes. Identifying and analyzing such changes, both at the city level and within single neighborhoods, may be a fundamental tool to better manage the current situation and provide sound strategies for future planning. Furthermore, such fine-grained and up-to-date characterization can represent a valuable asset for other tools and services, e.g., web mapping applications or real estate agency platforms. In this article, we propose a framework featuring a novel methodology to model and track changes in areas of the city by extracting information from online newspaper articles. The problem of uncovering clusters of news at specific times is tackled by means of the joint use of state-of-the-art language models to represent the articles, and of a density-based streaming clustering algorithm, properly shaped to deal with high-dimensional text embeddings. Furthermore, we propose a method to automatically label the obtained clusters in a semantically meaningful way, and we introduce a set of metrics aimed at tracking the temporal evolution of clusters. A case study focusing on the city of Rome during the Covid-19 pandemic is illustrated and discussed to evaluate the effectiveness of the proposed approach.
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Purpose - News consumption is critical in creating informed citizenry;however, in the current context of media convergence, news consumption becomes more complex as social media becomes a primary news source rather than news media. The current study seeks to answer three questions: why the shifted pattern of news seeking only happens to some but not all of the news consumers;whether the differentiated patterns of news seeking (news media vs social media) would result in different misinformation engagement behaviors;and whether misperceptions would moderate the relationship between news consumption and misinformation engagement. Design/methodology/approach - A survey consisted of questions related to personality traits, news seeking, misperceptions and misinformation engagement was distributed to 551 individuals. Multiple standard regression and PROCESS Macro model 1 were used to examine the intricate relationships between personality, news use and misinformation engagement. Findings - Results indicate that extroversion was positively associated with social media news consumption while openness was inversely related to it. Social media news consumption in turn positively predicted greater misinformation sharing and commenting. No association was found between Big Five personality traits and news media news seeking. News media news seeking predicted higher intention to reply to misinformation. Both relationships were further moderated by misperceptions that individuals with greater misperceptions were more likely to engage with misinformation. Originality/value - The current study integrates personality traits, news consumption and misperceptions in understanding misinformation engagement behaviors. Findings suggest that news consumption via news media in the digital era merits in-depth examinations as it may associate with more complex background factors and also incur misinformation engagement. Social media news consumption deserves continuous scholarly attention. Specifically, extra attention should be devoted to extrovert and pragmatic individuals in future research and interventions. People with these characteristics are more prone to consume news on social media and at greater risk of falling prey to misinformation and becoming a driving force for misinformation distribution. Peer review -The peer review history for this article is available at:https://publons.com/publon/10.1108/OIR-10-2021-0520
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This study incorporates in-depth interviews with 43 journalists from the digitally native, venture-capital-backed sports journalism organization the Athletic. Through the lens of gatekeeping and utilizing the concept of market orientation, findings illustrate how having a somewhat strong market orientation could positively impact gatekeeping processes. Data illustrated that, during the pandemic, journalists at the Athletic collaborated more and included more diversity in content. This positive result, which led to a subscription increase, is primarily due organization-level influences on gatekeeping. This study concludes with analysis on how these findings can affect journalism in general and sports journalism specifically now and after COVID-19. [ FROM AUTHOR] Copyright of Newspaper Research Journal is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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The polarity of online news publications thematically related to COVID-19 is analysed. A collection of sentiment annotations for news articles written in the Croatian language was created and compose a new Cro-CoV-Senti-articles-2020 dataset. The news article's sentiment is derived from the reactions of portal readers. In addition, well-known sentiment analysis approaches that use lexicons and machine learning algorithms have been implemented to automatically determine the sentiment of online news. Besides, the VADER framework was used in parallel. It has been found that for the purposes of crisis communication analysis when rapid analysis solutions are needed, existing tools can be used for preliminary sentiment analysis despite some technical shortcomings. However, for a more extensive analysis of the media space and highly valuable insights, some refinements are needed. This preliminary analysis, on a sample of approximately 3,400 newspaper articles related to COVID-19, finds that readers perceive as many as two-thirds of articles negatively rather than positively.