Design Principles and a Software Reference Architecture for Big Data Question Answering Systems

Companies continuously produce several documents containing valuable information for users. However, querying these documents is challenging, mainly because of the heterogeneity and volume of documents available. In this work, we investigate the challenge of developing a Big Data Question Answering system, i.e., a system that provides a unified, reliable, and accurate way to query documents through naturally asked questions. We define a set of design principles and introduce BigQA, the first software reference architecture to meet these design principles. The architecture consists of high-level layers and is independent of programming language, technology, querying and answering algorithms. BigQA was validated through a pharmaceutical case study managing over 18k documents from Wikipedia articles and FAQ about Coronavirus. The results demonstrated the applicability of BigQA to real-world applications. In addition, we conducted 27 experiments on three open-domain datasets and compared the recall results of the well-established BM25, TF-IDF, and Dense Passage Retriever algorithms to find the most appropriate generic querying algorithm. According to the experiments, BM25 provided the highest overall performance. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains

Open-retrieval question answering systems are generally trained and tested on large datasets in well-established domains. However, low-resource settings such as new and emerging domains would especially benefit from reliable question answering systems. Furthermore, multilingual and cross-lingual resources in emergent domains are scarce, leading to few or no such systems. In this paper, we demonstrate a cross-lingual open-retrieval question answering system for the emergent domain of COVID-19. Our system adopts a corpus of scientific articles to ensure that retrieved documents are reliable. To address the scarcity of cross-lingual training data in emergent domains, we present a method utilizing automatic translation, alignment, and filtering to produce English-to-all datasets. We show that a deep semantic retriever greatly benefits from training on our English-to-all data and significantly outperforms a BM25 baseline in the cross-lingual setting. We illustrate the capabilities of our system with examples and release all code necessary to train and deploy such a system1 © 2023 Association for Computational Linguistics.

New Challenges in Scientific Publications: Referencing, Artificial Intelligence and ChatGPT.

The COVID-19 pandemic has led to a surge in scientific publications, some of which have bypassed the usual peer-review processes, leading to an increase in unsupported claims being referenced. Therefore, the need for references in scientific articles is increasingly being questioned. The practice of relying solely on quantitative measures, such as impact factor, is also considered inadequate by many experts. This can lead to researchers choosing research ideas that are likely to generate favourable metrics instead of interesting and important topics. Evaluating the quality and scientific value of articles requires a rethinking of current approaches, with a move away from purely quantitative methods. Artificial intelligence (AI)-based tools are making scientific writing easier and less time-consuming, which is likely to further increase the number of scientific publications, potentially leading to higher quality articles. AI tools for searching, analysing, synthesizing, evaluating and writing scientific literature are increasingly being developed. These tools deeply analyse the content of articles, consider their scientific impact, and prioritize the retrieved literature based on this information, presenting it in simple visual graphs. They also help authors to quickly and easily analyse and synthesize knowledge from the literature, prepare summaries of key information, aid in organizing references, and improve manuscript language. The language model ChatGPT has already greatly changed the way people communicate with computers, bringing it closer to human communication. However, while AI tools are helpful, they must be used carefully and ethically. In summary, AI has already changed the way we write articles, and its use in scientific publishing will continue to enhance and streamline the process.

Citation advantage of COVID-19-related publications

With the global spread of the COVID-19 pandemic, scientists from various disciplines responded quickly to this historical public health emergency. The sudden boom of COVID-19-related papers in a short period of time may bring unexpected influence to some commonly used bibliometric indicators. By a large-scale investigation using Science Citation Index Expanded and Social Sciences Citation Index, this brief communication confirms the citation advantage of COVID-19-related papers empirically through the lens of Essential Science Indicators' highly cited paper. More than 8% of COVID-19-related papers published during 2020 and 2021 were selected as Essential Science Indicators highly cited papers, which was much higher than the set global benchmark value of 1%. The citation advantage of COVID-19-related papers for different Web of Science categories/countries/journal impact factor quartiles was also demonstrated. The distortions of COVID-19-related papers' citation advantage to some bibliometric indicators such as journal impact factor were discussed at the end of this brief communication.

Lodestar in the Time of Coronavirus? Interpreting International Obligations to Realise the Right to Health During the COVID-19 Pandemic

While the right to health has gained significant momentum in international law over the past two years, there is little clarity on what it means for States to comply with this right in times of COVID-19. Taking Articles 2(1) and 12 of the International Covenant on Economic, Social and Cultural Rights as a starting point, our article follows an approach guided by the rules of treaty interpretation under the Vienna Convention on the Law of Treaties to suggest how right to health obligations to prevent, treat and control infectious diseases should be interpreted in relation to COVID-19, and how these obligations interact with general obligations of immediacy, progressive realisation, minimum core and international assistance and cooperation in this context. This article makes a novel contribution to clarifying the right to health during COVID-19, thus enhancing capacity for the oversight of this right;its incorporation in global health law;and the understanding of its corresponding obligations in future global health emergencies. © 2023 The Author(s) [2023].

Lodestar in the Time of Coronavirus? Interpreting International Obligations to Realise the Right to Health During the COVID-19 Pandemic

While the right to health has gained significant momentum in international law over the past two years, there is little clarity on what it means for States to comply with this right in times of COVID-19. Taking Articles 2(1) and 12 of the International Covenant on Economic, Social and Cultural Rights as a starting point, our article follows an approach guided by the rules of treaty interpretation under the Vienna Convention on the Law of Treaties to suggest how right to health obligations to prevent, treat and control infectious diseases should be interpreted in relation to COVID-19, and how these obligations interact with general obligations of immediacy, progressive realisation, minimum core and international assistance and cooperation in this context. This article makes a novel contribution to clarifying the right to health during COVID-19, thus enhancing capacity for the oversight of this right;its incorporation in global health law;and the understanding of its corresponding obligations in future global health emergencies.

The Impact of COVID-19 on Educational Research: A Bibliometric Analysis

As a result of the COVID-19 pandemic and the major challenges generated in education, thousands of scientific papers have been published, contributing to the establishment of a distinct research line in the field. This study provides a bibliometric overview of the educational publications linked to COVID-19 indexed by the Web of Science Core Collection for the years 2020 and 2021. The findings show a growing interest of researchers in education in this area. The proportion of articles among the types of documents proved to be dominant. Journals dedicated to chemistry and medical education stood out for the high number of pandemic-related papers. Higher education has been an intensively explored area during the pandemic. The USA and its universities were the most productive in publishing studies on COVID-19 in education. Our study indicated research themes that have been explored by the researchers, such as online learning in different educational settings, curriculum and instructional approaches in the online learning setting, and the psychological consequences of COVID-19 on the educational actors. The implications of the pandemic on potential research avenues for education research were also emphasized.

Empirical relationship between the number of review and research articles.

In this contribution, an empirical relationship between the number of review and research articles published per year was searched. The simple idea based on proportionality (linearity) between the numbers of both kinds of articles was expressed in terms of a quadratic relationship, in which the quadratic member can reflect negative or positive deviations from the assumed linearity. The quadratic relationship was able to describe beginning periods of research fields as well as their mature phases and to detect the unpredictably high number of review articles. It was verified by the articles published in 20 various research fields taken from the Web of Science during different time spans. Supplementary Information: The online version contains supplementary material available at 10.1007/s11192-023-04654-0.

Analysis of the Role of Individuals and Governments in Post-COVID-19 Health Issues: The Case of Korea

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.

Contextualizing Emerging Trends in Financial News Articles

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.

Portuguese Twitter Dataset on COVID-19

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.

The development of statistical literacy among students: Analyzing messages in media articles with Gal's worry questions

Real‐world data are fundamental to modern teaching methodologies that aim to improve statistical knowledge and reasoning in students. Statistical information is encountered in everyday life, such as media articles and involves real‐world contexts. However, information could be biased or (mis)represented and students should be concerned about the validity of such articles, as well as the nature and trustworthiness of the evidence presented, while considering alternative interpretations of the findings conveyed to them. Statistics educators could make use of media articles to create opportunities for students to reflect on such (mis)representations and build statistical literacy. The purpose of this article is to show how information and data on the Omicron COVID‐19 variant have been (mis)represented in the media and by government entities. I also demonstrate how these examples may be utilized in the statistics classroom as they relate to concepts covered in most basic statistics courses. [ FROM AUTHOR] Copyright of Teaching Statistics is the property of Wiley-Blackwell 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.)

Virtual Reality and Augmented Reality Applied to E-Commerce: A Literature Review

The massification of technologies, the implementation of 5G and the Internet of Things (IoT), allow implementing systems that contain virtual or augmented reality or implementation of both. In this sense, the covid 19 pandemic in the last years, has also affected people's behavior and leaned to shop without leaving their homes. VR, RA, and/or MR techniques are currently widely used for medicine, education, and entertainment, among others. In this study, we combine both elements to analyze the literature on e-commerce and the use of VR, AR, and/or RM. Searching and analyzing recent scientific articles were defined, and virtual reality is the most used, followed by the mixture of RV and RA, the above to improve the shopping experience, providing the customer with a more authentic and immersive environment. In future works, we will expect to expand this study, including how to evaluate the shopping experience and relate it to the customer experience. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-QA: A question answering dataset for COVID-19

We present COVID-QA, a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. To evaluate the dataset we compared a RoBERTa base model fine-tuned on SQuAD with the same model trained on SQuAD and our COVID-QA dataset. We found that the additional training on this domain-specific data leads to significant gains in performance. Both the trained model and the annotated dataset have been open-sourced at: https://github.com/deepset-ai/COVID-QA. © ACL 2020.All right reserved.

CoviIS: A Real-Time Covid Help Information System Using Digital Media

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.

Machine Learning-Based Identifications of COVID-19 Fake News Using Biomedical Information Extraction

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.

Information retrieval and extraction on COVID-19 clinical articles using graph community detection and Bio-BERT embeddings

In this paper, we present an information retrieval system on a corpus of scientific articles related to COVID-19. We build a similarity network on the articles where similarity is determined via shared citations and biological domain-specific sentence embeddings. Ego-splitting community detection on the article network is employed to cluster the articles and then the queries are matched with the clusters. Extractive summarization using BERT and PageRank methods is used to provide responses to the query. We also provide a Question-Answer bot on a small set of intents to demonstrate the efficacy of our model for an information extraction module. © ACL 2020.All right reserved.

Seeing Should Probably Not Be Believing: The Role of Deceptive Support in COVID-19 Misinformation on Twitter

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.

Reviewing topics of COVID-19 news articles: case study of CNN and China daily

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.)

Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

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.