ABSTRACT
This study was conducted in order to ascertain what role government and individuals should play in the event of a pandemic such as Coronavirus occurring in Korea in the future, using information deriving from news articles available at the Bigkinds news portal site in Korea. The analysis period ran from 11 March 2020, when the pandemic was declared by the World Health Organization, to 31 January 2023, almost three years later. Text mining analysis was conducted on all the articles, as a result of which six important roles that individuals should play, and ten roles that government should play, in a pandemic situation were suggested. © 2022, European Alliance for Innovation. All rights reserved.
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Identifying and exploring emerging trends in news is becoming more essential than ever with many changes occurring around the world due to the global health crises. However, most of the recent research has focused mainly on detecting trends in social media, thus, benefiting from social features (e.g. likes and retweets on Twitter) which helped the task as they can be used to measure the engagement and diffusion rate of content. Yet, formal text data, unlike short social media posts, comes with a longer, less restricted writing format, and thus, more challenging. In this paper, we focus our study on emerging trends detection in financial news articles about Microsoft, collected before and during the start of the COVID-19 pandemic (July 2019 to July 2020). We make the dataset accessible and we also propose a strong baseline (Contextual Leap2Trend) for exploring the dynamics of similarities between pairs of keywords based on topic modeling and term frequency. Finally, we evaluate against a gold standard (Google Trends) and present noteworthy real-world scenarios regarding the influence of the pandemic on Microsoft. ©2022 Association for Computational Linguistics.
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Over the last two years, the COVID-19 pandemic has affected hundreds of millions of people around the world. As in many crises, people turn to social media platforms, like Twitter, to communicate and share information. Twitter datasets have been used over the years in many research studies to extract valuable information. Therefore, several large COVID-19 Twitter datasets have been released over the last two years. However, none of these datasets contains only Portuguese Tweets, despite the Portuguese Language being reported as one of the top five languages used on Twitter. In this paper, we present the first large-scale Portuguese COVID-19 Twitter dataset. The dataset contains over 19 million Tweets spanning 2020 and 2021, allowing the entire pandemic to be analyzed. We also conducted a sentiment analysis on the dataset and correlated the various spikes in Tweet count and sentiment scores to various news articles and government announcements in Portugal and Brazil. The dataset is available at: https://github.com/bioinformatics-ua/Portuguese-Covid19-Dataset © 2022 IEEE.
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In this paper, we present CoviIS, an emergency Covid Information System that utilizes digital media to provide helpful information in uncertain times of the Covid pandemic. Since people require different types of information during times of crisis, the findings obtained from this work integrate various pieces of information into a form of coherency, thereby aiding people during an emergency and reducing further damage. The study brings together real-time Covid informatics employing multiple methods such as general search, social media search, and geographical analysis. To assist people in this emergency, we also conduct a comprehensive analysis of news articles and social media activities to provide an economically feasible solution. CoviIS helps locate the nearest hospitals and Covid isolation centers for seeking medical attention during an emergency. CoviIS also provides emergency information through news articles and social media posts, thereby serving as an important Covid emergency tool. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The spread of fake news related to COVID-19 is an infodemic that leads to a public health crisis. Therefore, detecting fake news is crucial for an effective management of the COVID-19 pandemic response. Studies have shown that machine learning models can detect COVID-19 fake news based on the content of news articles. However, the use of biomedical information, which is often featured in COVID-19 news, has not been explored in the development of these models. We present a novel approach for predicting COVID-19 fake news by leveraging biomedical information extraction (BioIE) in combination with machine learning models. We analyzed 1164 COVID-19 news articles and used advanced BioIE algorithms to extract 158 novel features. These features were then used to train 15 machine learning classifiers to predict COVID-19 fake news. Among the 15 classifiers, the random forest model achieved the best performance with an area under the ROC curve (AUC) of 0.882, which is 12.36% to 31.05% higher compared to models trained on traditional features. Furthermore, incorporating BioIE-based features improved the performance of a state-of-the-art multi-modality model (AUC 0.914 vs. 0.887). Our study suggests that incorporating biomedical information into fake news detection models improves their performance, and thus could be a valuable tool in the fight against the COVID-19 infodemic. © 2023 by the authors.
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With the spread of the SARS-CoV-2, enormous amounts of information about the pandemic are disseminated through social media platforms such as Twitter. Social media posts often leverage the trust readers have in prestigious news agencies and cite news articles as a way of gaining credibility. Nevertheless, it is not always the case that the cited article supports the claim made in the social media post. We present a cross-genre ad hoc pipeline to identify whether the information in a Twitter post (i.e., a "Tweet") is indeed supported by the cited news article. Our approach is empirically based on a corpus of over 46.86 million Tweets and is divided into two tasks: (i) development of models to detect Tweets containing claim and worth to be fact-checked and (ii) verifying whether the claims made in a Tweet are supported by the newswire article it cites. Unlike previous studies that detect unsubstantiated information by post hoc analysis of the patterns of propagation, we seek to identify reliable support (or the lack of it) before the misinformation begins to spread. We discover that nearly half of the Tweets (43.4%) are not factual and hence not worth checking - a significant filter, given the sheer volume of social media posts on a platform such as Twitter. Moreover, we find that among the Tweets that contain a seemingly factual claim while citing a news article as supporting evidence, at least 1% are not actually supported by the cited news and are hence misleading. © 2022 Association for Computing Machinery.
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Purpose: The purpose of this study is to analyze the topics of COVID-19 news articles for better obtaining the relationship among and the evolution of news topics, helping to manage the infodemic from a quantified perspective. Design/methodology/approach: To analyze COVID-19 news articles explicitly, this paper proposes a prism architecture. Based on epidemic-related news on China Daily and CNN, this paper identifies the topics of the two news agencies, elucidates the relationship between and amongst these topics, tracks topic changes as the epidemic progresses and presents the results visually and compellingly. Findings: The analysis results show that CNN has a more concentrated distribution of topics than China Daily, with the former focusing on government-related information, and the latter on medical. Besides, the pandemic has had a big impact on CNN and China Daily's reporting preference. The evolution analysis of news topics indicates that the dynamic changes of topics have a strong relationship with the pandemic process. Originality/value: This paper offers novel perspectives to review the topics of COVID-19 news articles and provide new understandings of news articles during the initial outbreak. The analysis results expand the scope of infodemic-related studies. [ABSTRACT FROM AUTHOR] Copyright of Aslib Journal of Information Management is the property of Emerald Publishing Limited 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 abstract 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 abstract. (Copyright applies to all Abstracts.)
ABSTRACT
The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. Therefore, it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery. To this end, various types of social sensing tools, such as tweeting and publicly released news, have been employed to understand individuals' and communities' thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience. We use fact-checking organizations to classify news as real, mixed, or fake, and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience (CR). Based on the news articles and tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution, where resource distribution is assessed by the level of economic resilience and community capital. Based on the estimates of these two factors, we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets. To improve the operationalization and sociological significance of this work, we use dimension reduction techniques to integrate the dimensions. © 2020 Tsinghua University Press.
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Newspapers and News Websites have become a part and a crucial medium in society. They provide information regarding the events that are happening around and how society is getting influenced by these events. For example, a pandemic like Covid-19 has raised the importance of these mediums. They have been giving detailed news to society on a variety of topics, such as how to detect the strains of the coronavirus, reasons for lockdown along with what are the other restrictions to be followed during the pandemic. They also provided information about the government policies which were built to be taken care of in case of pandemics and so on and they kept updated with the details about the development of the vaccines. Due to this lot of information on Covid-19 is generated. Examining the different topics/themes/issues and the sentiments expressed by different countries will aid in the understanding of the covid-19. This paper discusses the various models which were built to identify the topics, sentiments, and summarization of news headlines and articles regarding Covid-19. The proposed topic model has achieved a Silhouette score of 0.6407036, 0.6645274, 0.6262914, and 0.6234863 for 4 countries like South Korea, Japan, the UK, India on the news articles dataset, and it was found that the United Kingdom was the worst-hit, and it had the largest percentage of negative sentiments. The proposed XlNet sentiment classification model obtained a validation accuracy of 93.75%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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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.
Subject(s)
COVID-19 , Humans , COVID-19/prevention & control , COVID-19/epidemiology , SARS-CoV-2 , Pandemics/prevention & control , Philippines , Reproducibility of ResultsABSTRACT
The Covid-19 pandemic affects maternal health both directly and indirectly, and direct and indirect effects are intertwined. To provide a comprehensive overview on this broad topic in a rapid format behooving an emergent pandemic we conducted a scoping review. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
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This paper presents a named entity recognition system for the specific domain of Vietnamese COVID-19 news articles. By incorporating manually selected and domain-specific features into a simple deep learning architecture, the system can identify a wide range of custom named entities relevant in the context of COVID-19 and future epidemics. Using high-dimensional embedding vectors in combination with part-of-speech tags and additional features, the system achieves an F score of about 90.41%, surpassing or coming close to results by other models that are more complicated or pre-Trained and fine-Tuned. © 2022 IEEE.
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This paper presents a named entity recognition system for the specific domain of Vietnamese COVID-19 news articles. By incorporating manually selected and domain-specific features into a simple deep learning architecture, the system can identify a wide range of custom named entities relevant in the context of COVID-19 and future epidemics. Using high-dimensional embedding vectors in combination with part-of-speech tags and additional features, the system achieves an F score of about 90.41%, surpassing or coming close to results by other models that are more complicated or pre-Trained and fine-Tuned. © 2022 IEEE.
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Throughout the COVID-19 pandemic, a parallel infodemic has also been going on such that the information has been spreading faster than the virus itself. During this time, every individual needs to access accurate news in order to take corresponding protective measures, regardless of their country of origin or the language they speak, as misinformation can cause significant loss to not only individuals but also society. In this paper we train several machine learning models (ranging from traditional machine learning to deep learning) to try to determine whether news articles come from either a reliable or an unreliable source, using just the body of the article. Moreover, we use a previously introduced corpus of news in Swedish related to the COVID-19 pandemic for the classification task. Given that our dataset is both unbalanced and small, we use subsampling and easy data augmentation (EDA) to try to solve these issues. In the end, we realize that, due to the small size of our dataset, using traditional machine learning along with data augmentation yields results that rival those of transformer models such as BERT. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.
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The Covid-19 pandemic affects maternal health both directly and indirectly, and direct and indirect effects are intertwined. To provide a comprehensive overview on this broad topic in a rapid format behooving an emergent pandemic we conducted a scoping review. Copyright © 2022 Wolters Kluwer Medknow Publications. 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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To cope with the COVID-19 pandemic, many jurisdictions have introduced new or altered existing legislation. Even though these new rules are often communicated to the public in news articles, it remains challenging for laypersons to learn about what is currently allowed or forbidden since news articles typically do not reference underlying laws. We investigate an automated approach to extract legal claims from news articles and to match the claims with their corresponding applicable laws. We examine the feasibility of the two tasks concerning claims about COVID-19-related laws from Berlin, Germany. For both tasks, we create and make publicly available the data sets and report the results of initial experiments. We obtain promising results with Transformer-based models that achieve 46.7 F1 for claim extraction and 91.4 F1 for law matching, albeit with some conceptual limitations. Furthermore, we discuss challenges of current machine learning approaches for legal language processing and their ability for complex legal reasoning tasks. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.
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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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The COVID-19 pandemic and other global health events are unfortunately excellent environments for the creation and spread of misinformation, and the language associated with health misinformation may be typified by unique patterns and linguistic markers. Allowing health misinformation to spread unchecked can have devastating ripple effects;however, detecting and stopping its spread requires careful analysis of these linguistic characteristics at scale. We analyze prior investigations focusing on health misinformation, associated datasets, and detection of misinformation during health crises. We also introduce a novel dataset designed for analyzing such phenomena, comprised of 2.8 million news articles and social media posts spanning the early 1900s to the present. Our annotation guidelines result in strong agreement between independent annotators. We describe our methods for collecting this data and follow this with a thorough analysis of the themes and linguistic features that appear in information versus misinformation. Finally, we demonstrate a proof-of-concept misinformation detection task to establish dataset validity, achieving a strong performance benchmark (accuracy = 75%;F1 = 0.7). © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.
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Topic modeling is widely used to obtain the most vis-ible topics from a given text corpus. In this work, a demonstration of the most discussed topic modeling is presented from articles on the Reuters news website. These articles are collected and consequently processed with a Latent Dirichlet Allocation (LDA) unsupervised learning algorithm. The main goal is to build the best model(s) that accurately produces the most discussed topics. Such a model(s) can be used in real life to instantly get information about actual news to classify documents in a given dataset and extract dominated topics with their keywords. This helps to build, for example, correlations with user preferences and recommend interesting content. There are works which use different models to evaluate texts and obtain statistics about them, such as the most popular people's opinions about some question or to obtain popular and dominating subtopics of the specific topic dataset (e.g., medicine articles). As a result of the work, we were able to create a generic LDA model, trained on Wikipedia articles. The model successfully analyzes Reuters articles and extracted their topics as keyword sets. Then, they can be used to recommend content that is interesting to the target user, for example, based on the recommended content tags. © 2022 IEEE.