Information, comunication and COVID-19: a literature review of seeking models from academic libraries

The present text offers an approach from various theoretical and empirical references close to academic libraries;around the challenges they face from their object of research: the users of information. A rela-tionship is proposed that can be aimed at a dialogue with digital culture and the comunication field, see-king to establish a framework of encounter based on the adaptation that academic libraries have had to incrementally complex information environments. The circulation of information from various sources and approaches in everyday life represents a multiple challenge to produce knowledge and in the case of libraries. The pandemic by COVID-19 puts the subjectivity of the information process back at the center of the discussion, which is accentuated in contexts where uncertainty and the multiplicity of meanings prevail to interpret it. From a literature review and the selection of models of informational behavior and user studies, clues were detected to study digital culture from a research perspective of academic libra-ries. The argumentation developed allowed to detect clues to find an entry framework for the study of users from an interdisciplinary construction between communication, health and information sciences.

Exploring Google Searches for Out-of-Clinic Medication Abortion in the United States During 2020: Infodemiology Approach Using Multiple Samples.

Background: As access barriers to in-person abortion care increase due to legal restrictions and COVID-19-related disruptions, individuals may be turning to the internet for information and services on out-of-clinic medication abortions. Google searches allow us to explore timely population-level interest in this topic and assess its implications. Objective: We examined the extent to which people searched for out-of-clinic medication abortions in the United States in 2020 through 3 initial search terms: home abortion, self abortion, and buy abortion pill online. Methods: Using the Google Trends website, we estimated the relative search index (RSI)-a comparative measure of search popularity-for each initial search term and determined trends and its peak value between January 1, 2020, and January 1, 2021. RSI scores also helped to identify the 10 states where these searches were most popular. We developed a master list of top search queries for each of the initial search terms using the Google Trends application programming interface (API). We estimated the relative search volume (RSV)-the search volume of each query relative to other associated terms-for each of the top queries using the Google Health Trends API. We calculated average RSIs and RSVs from multiple samples to account for low-frequency data. Using the Custom Search API, we determined the top webpages presented to people searching for each of the initial search terms, contextualizing the information found when searching them on Google. Results: Searches for home abortion had average RSIs that were 3 times higher than self abortion and almost 4 times higher than buy abortion pill online. Interest in home abortion peaked in November 2020, during the third pandemic wave, at a time when providers could dispense medication abortion using telemedicine and by mail. Home abortion was most frequently queried by searching for Planned Parenthood, abortion pill, and abortion clinic, presumably denoting varying degrees of clinical support. Consistently lower search popularity for self abortion and buy abortion pill online reflect less population interest in mostly or completely self-managed out-of-clinic abortions. We observed the highest interest for home abortion and self abortion in states hostile to abortion, suggesting that state restrictions encourage these online searches. Top webpages provided limited evidence-based clinical content on self-management of abortions, and several antiabortion sites presented health-related disinformation. Conclusions: During the pandemic in the United States, there has been considerably more interest in home abortions than in minimally or nonclinically supported self-abortions. While our study was mainly descriptive, showing how infrequent abortion-related search data can be analyzed through multiple resampling, future studies should explore correlations between the keywords denoting interest in out-of-clinic abortion and abortion care measures and test models that allow for improved monitoring and surveillance of abortion concerns in our rapidly evolving policy context.

Hyperparameter Search for CT-Scan Classification Using Hyperparameter Tuning in Pre-Trained Model CNN With MLP

The common approach to find best hyperparameter in CNN training is grid search, by observing one set to another of hyperparameter for obtaining the best result. However, this approach is considered inefficient, time-consuming, and ineffectively computational. In this study, we are observing 2 hyperparameter tuning algorithms (bayesian optimization and random search) in search of the best hyperparameter for CT-Scan classification case. The used dataset is COVID-19 and non-COVID-19 lung CT-Scans. Several CNN architectures are also used such as: InceptionV4, MobileNetV3, and EfficientnetV2 with additional multi-layer perceptron on top layers. Based on the experiments, model EfficientnetV2-L architecture using hyperparameter from bayesian-optimization can outperform other models, with batch size of 32, learning rate of 0.01, dropout 0.5, Adam optimizer and SoftMax activation, resulting in the accuracy rate of 0.94% and a model training time of 50 minutes 40 seconds. © 2022 IEEE.

Technology Dependency and Impact during COVID-19: A Systematic Literature Review and Open Challenges

The COVID-19 pandemic is still a challenge in many countries, although life must proceed while ensuring the pandemic is managed critically. Due to the delay in producing permanent medical intervention, despite the availability of vaccines, there is still a need to depend on technology in performing several tasks. A systematic literature review that provides comprehensive evidence on technology dependence and the impact of technology on individuals during the pandemic is lacking. This study systematically reviewed scholarly works related to technology dependency from a broad view since the pandemic and mapped the research findings into a taxonomy, thus establishing the trend in technology type, major areas of technology dependency, and the impact of technology during the pandemic. The mapped taxonomy is used to expound on open challenges and recommendations. The final set from the systematic search was 76 articles. Technology might be an avenue for administering and enhancing health services, improving outreaches, and supporting curbing the spread of diseases. However, the impact of technology dependence is both positive and negative. A systematic mapping was conducted to explore the literature on the impacts of technology, where there is a need for further research. Notwithstanding the category, most of the reviewed articles emphasized the usage and impact of technology at such a time of the pandemic and provided insights on the manner of addressing them. Realistically, there has been an acceleration of digitalization trends in the present era of the COVID-19 pandemic and the possibility of rapid development of novel digital technologies. Author

Identifying Similar Questions in the Medical Domain Using a Fine-tuned Siamese-BERT Model

A large number of people search about their health related problems on the web. However, the number of sites with qualified and verified people answering their queries is quite low in comparison to the number of questions being put up. The rate of queries being searched on such sites has further increased due to the COVID-19 pandemic. The main reason people find it difficult to find solutions to their queries is due to ineffective identification of semantically similar questions in the medical domain. For most cases, answers to the queries people ask would be present, the only caveat being the question may be present in a different form than the one asked by the particular user. In this research, we propose a Siamese-based BERT model to detect similar questions using a fine-tuning approach. The network is fine-tuned with medical question-answer pairs and then with question-question pairs to get a better question similarity prediction. © 2022 IEEE.

Integrity 2023: Integrity in Social Networks and Media

Integrity 2023 is the fourth edition of the successful Workshop on Integrity in Social Networks and Media, held in conjunction with the ACM Conference on Web Search and Data Mining (WSDM) in the past three years. The goal of the workshop is to bring together researchers and practitioners to discuss content and interaction integrity challenges in social networks and social media platforms. The event consists of a combination of invited talks by reputed members of the Integrity community from both academia and industry and peer-reviewed contributed talks and posters solicited via an open call-for-papers. © 2023 Owner/Author.

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.

Health Anxiety, Information Anxiety, and Internet Self-Efficacy on Cyberchondria among Filipino Young Professionals during the COVID-19 Pandemic

The mobility restrictions due to COVID-19 lockdown impositions have forced people to stay at home in lieu of face-to-face activities. In effect, it has increased people's exposure to the Internet and its perils, brought by excessive information from different media that may lead to the development of health-related anxiety. This phenomenon is known as cyberchondria, where people may have experienced extreme anxiety about their physical health because of repeated internet searches concerning their medical conditions. This paper investigates the possible relationship between health anxiety, information anxiety, and computer self-efficacy toward cyberchondria. Data from a cross-sectional method using online surveys among fresh graduates aged 21-24 in several Philippine higher education institutions were analyzed. The results of the structural model test reveal that both health anxiety and information anxiety may contribute to cyberchondria. The study discusses the implication of the results and offers fruitful research directions for further studies. © ICCE 2022.All rights reserved.

Bioinspired CNN Approach for Diagnosing COVID-19 Using Images of Chest X-Ray

Introduction: The provision of medical facilities needed for COVID-19 diagnosis is a global concern. They must be a powerful tool for detecting and diagnosing the virus quickly using a variety of tests, as well as low-cost advancements. Whereas a chest X-ray image is an effective screening technique, the image acquisition by the instruments must be read appropriately and quickly if multiple tests are performed. Objectives: COVID-19 causes continuous respiratory parenchymal ground glass and integrates respiratory opacity, with a curved shape and peripheral pulmonary dissemination in some cases, which is difficult to anticipate earlier on. In this chapter, we intend to construct a good platform to identify COVID-19 characteristics from the image of chest X-ray to aid in early analysis. Methods: In particular, based on the Cuckoo search method, this chapter provides a bioinspired CNN-based model for COVID-19 diagnosis. This method identifies different deep learning strategies of COVID-19 patients' chest X-ray images for accurate infection identification. The suggested model's performance is estimated using the Cuckoo search approach. Furthermore, the bioinspired CNN characteristics are fine-tuned using optimization algorithm. Rigorous testing reveals that suggested method may accurately categorize chest X-ray images with high performance, remembrance, and sensitivity. Results: As a result, the suggested approach can be used to classify COVID-19 diseases from chest X-ray images in real time and also accuracy will be validated. Conclusion: Finally, the investigation of comparison results illustrates the Cuckoo algorithm is realized to determine the interested regions of the COVID-19 x-ray images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Explaining the Gamestop Short Squeeze using Ιntraday Data and Google Searches

This article examines the recent short squeeze of the GameStop (GME) stock in early 2021. This event, although not the only case of short squeeze, has some idiosyncratic features that makes it extremely interesting, mainly because it was organized by non-institutional investors through social media like Reddit. Using intraday data during the period 4/1/2021-26/3/2021, we conclude that volume and Google searches provide useful information which enable us to explain the GME performance. Moreover, we show that information on volume and Google searches can provide investors with valuable data, but the faster investors have access to this information, the greater the advantages. This analysis could be very useful for scholars and practitioners who examine profitable investment strategies when such conditions emerge in the markets, and it also provides some thoughts for regulators regarding the impact of networks, social or not, on the stability of the financial markets.

Hybridizing adaptive large neighborhood search with kernel search: a new solution approach for the nurse routing problem with incompatible services and minimum demand

The average age of the population has grown steadily in recent decades along with the number of people suffering from chronic diseases and asking for treatments. Hospital care is expensive and often unsafe, especially for older individuals. This is particularly true during pandemics as the recent SARS-CoV-2. Hospitalization at home has become a valuable alternative to face efficiently a huge increase in treatment requests while guaranteeing a high quality of service and lower risk to fragile patients. This new model of care requires the redefinition of health services organization and the optimization of scarce resources (e.g., available nurses). In this paper, we study a Nurse Routing Problem that tries to find a good balance between hospital costs reduction and the well-being of patients, also considering realistic operational restrictions like maximum working times for the nurses and possible incompatibilities between services jointly provided to the same patient. We first propose a Mixed Integer Linear Programming formulation for the problem and use some valid inequalities to strengthen it. A simple branch-and-cut algorithm is proposed and validated to derive ground benchmarks. In addition, to efficiently solve the problem, we develop an Adaptive Large Neighborhood Search hybridized with a Kernel Search and validate its performance over a large set of different realistic working scenarios. Computational tests show how our matheuristic approach manages to find good solutions in a reasonable amount of time even in the most difficult settings. Finally, some interesting managerial insights are discussed through an economic analysis of the operating context. © 2022 The Authors. International Transactions in Operational Research published by John Wiley & Sons Ltd on behalf of International Federation of Operational Research Societies.

Social Media Use in Higher Education During the COVID-19 Pandemic: A Systematic Literature Review

This study reviews the literature to gain an in-depth understanding of the pedagogical role of social media in higher education institutions (HEI's) during the COVID-19 pandemic. A systematic search in the Web of Science, Scopus, and EBSCO databases yielded 34 relevant empirical studies published between January 2020 and April 2021. The findings reveal that: a) the innovative possibilities furnished through social media facilitated the transition to a complete online learning setting, b) the majority of studies are oriented towards the perspectives of students, c) the lack of well-defined policy hinders the effective utilization of social media in the pedagogical process, and d) questionnaires were the mostly used data collection method overlooking the significance of digital tracing as a rich source of data. This article provides a research agenda to advance the knowledge of the pedagogical possibilities of social media, especially that these platforms were not used to their full potential for teaching and learning during the pandemic. This study also has practical implications for HEI's and policymakers to recognize the significance of social media in maintaining educational sustainability. © 2022,International Journal of Emerging Technologies in Learning. All Rights Reserved.

Developing a solution algorithm for the autonomous electric vehicle routing problem considering the mixed fleet

In recent years, some phenomena such as the COVID-19 pandemic have caused the autonomous vehicle (AV) to attract much attention in theoretical and applied research. This paper addresses the optimization problem of a heterogeneous fleet that consists of autonomous electric vehicles (AEVs) and conventional vehicles (CVs) in a Business-to-Consumer (B2C) distribution system. The absence of the driver in AEVs results in the necessity of studying two factors in modeling the problem, namely time windows in the routing plan and different compartments in the loading space of AEVs. The arrival and departure times of the AEV at the customer’s location must be pre-planned, because, the AEV is not able to decide what to do if the customer is late at this point. Also, due to increasing the security of the loads inside the AEVs and the lack of control of the driver during the delivery of the goods, each customer should only have access to his/her orders. Therefore, the compartmentation of the AEV’s loading area has been proposed in its conceptual model. We developed a mathematical model based on these properties and proposed a hybrid algorithm, including variable neighborhood search (VNS) via neighborhood structure of large neighborhood search (LNS), namely the VLNS algorithm. The numerical results shed light on the proficiency of the algorithm in terms of solution time and solution quality. In addition, employing AEVs in the mixed fleet is considered to be desirable based on the operational cost of the fleet. Author

A Comparative Analysis of the Transformation of the Other's Image: 2018-2022

The article is the final in a series of studies we conducted in the period from 2018 to 2022, and focuses on the transformation of the image of the Other in the period of the Covid-19 pandemic. For this study, we used Internet query statistics, extracting a series of markers, which we divided into three groups: food, clothing(appearance), and sexuality. The data was used to compile a correlation matrix and identify the strongest correlation between the markers. The study showed that the most diverse in the number of different markers is the food aspect. The appearance and sexual aspects are less distinctive during the pandemic but also play an important role in shaping the Other's image. It is also worth mentioning the fact that in the post-Covid time (2022) the difference between various models is blurred and some of them are enlarged by the inclusion of representatives of other ethnic groups. In particular, today we can distinguish several big clusters of the Other's models holding common structural markers: some models are united according to their "food" aspect (Far Eastern cluster), others according to their appearance and sexual aspects (cluster of the former Soviet Union ethnic groups). However, within these clusters, models also share structural markers, so that they can be combined into subgroups based on one feature or another.

Agent for Recommending Information Relevant to Web-based Discussion by Generating Query Terms using GPT-3

In Web discussions, which have become mainstream with COVID-19, the amount of information possessed and the level of understanding of the discussion differ among participants. As a result, some participants may not be able to speak up satisfactorily, and this can hinder consensus building in the discussion as a whole. Therefore, we develop an agent that automatically recommends information related to the discussion as information that facilitates participants to speak up. The agent first obtains necessary discussion data from on-going Web discussions. The information to be recommended is determined by real-time search. Query words for the search are generated using a pre-trained query-term-generation model. When selecting information to recommend from the information obtained in the search, a model that classifies the acquired information according to the discussion phase is used. The results of a discussion experiment in which an agent intervened in a Web-based discussion showed many results indicating the effectiveness of the agent, although there are some points that need to be improved. However, since the scale of the discussion experiment was small, it will be necessary to validate the agent in large-scale discussions in the future. © 2022 IEEE.

Blockchain Technology for Supply Chain Resilience

Nowadays, people search sincerely and deeply for authenticated products with low impact on the environment. Especially with the current risks of fraud and counterfeiting and the successive crisis worldwide. The pandemic crisis Covid-19 makes the decision-makers convinced that the supply chain must be more resilient, robust, and sustainable to decrease the time that the supply chain remains disrupted or unfunctional when it faces an unexpected disruption or an unforeseen event wreaks havoc on a supply chain. This paper aims to review the use of blockchain technology(BT) in supply chains(SC). It presents a framework for achieving resilient supply chains by reviewing the literature and allowing the managers to take advantage of the benefits of Blockchain technology, overcome drawbacks, and delete barriers to the adoption of Blockchain technology. This paper presents the crucial features of Blockchain technology that will be an added value if the researchers and managers could take advantage of the positive side and overcome the negative one, which can potentially improve supply chain resilience (SCR). This research enables the stakeholders an overview of the advantages, disadvantages, and barriers of Blockchain technology that must be well managed to ensure an efficient implementation of Blockchain technology within a resilient supply chain. © 2022 IEEE.

Modeling and Quantifying the Role of User Active Search in COVID-19 Multi-Information Analysis and Prediction in the Chinese Sina-microblog

The study of information spreading is important and necessary, especially during the Coronavirus, when plenty of opinions aroused in the Chinese Sina-microblog. On the basis of previous studies, we propose a comprehensive susceptible-reading-forwarding-immune (SRFI) model with considering user active search. We establish differential equations introducing average active reading rate to describe the multi-information propagation process. By using typical event about COVID-19 during the outbreak of public opinion to carry out the numerical fitting experiment to estimate model parameters, fit real data, and analyze the calculated information transmission indexes, we verify the validity of the model. We analyze the sensitivity of multiple parameters to multi-information transmission index based on reading and forwarding and the effect of average active reading rate to show the influence of the new parameter on multi-information transmission. In addition, to compare the predictive ability of the previous model with our new model, we use the early prediction method. Result shows that our new model can forecast the process of multi-information transmission faster and more exactly. The conclusions above indicate that the role of user active search is not negligible and the I-SRFI model can help us design effective communication strategies for rapid implementation of public health interventions. © 2022 IEEE.

Web search behaviors and infodemic attitudes regarding COVID-19 in Turkey: A framework study for improving response and informing policy on the COVID-19 infodemic.

Objective: This study aimed to develop a framework regarding COVID-19 infodemic response and policy informing through focusing on infodemic concepts circulating on the online search engine in Turkey in relation to the COVID-19 outbreak and comparing the contents of these concepts with Maslow's hierarchy of needs and disaster stages. Materials and methods: The universe of this descriptive epidemiological research consists of internet search activities on COVID-19 circulating online on Google Trends between March 10, 2020, when the first case was seen in Turkey, and June 01, 2020, when the lockdown restrictions were lifted. Findings: There was no internet trend regarding a misinformed attitude within the given date range. While an infodemic attitude toward superficial attitude and racist attitude in the internet environment was detected for 1 week, an infodemic attitude toward definitive attitude was detected for 2 weeks. The non-infodemic concepts were more common than the other infodemic attitudes. The infodemic concepts were able to reach Maslow's physiological, safety, and social need levels. With the infodemic concepts obtained, a COVID-19 development process framework was developed. The framework consists of three domains (COVID-19, applications and outcomes), including disaster phases and health/social impacts, built on seven public health epochs. Results: A systematized COVID-19 development process framework was modeled in order to conceptualize COVID-19 internet searches and to reveal the development processes and outcomes.

Investor attention and the COVID-19 concept stocks in China's stock market

Purpose This paper investigates the impact of investor attention on the COVID-19 concept stocks in China's stock market from the perspectives of the macroeconomy, the stock market and the COVID-19 pandemic. Design/methodology/approach On the basis of controlling the time effects and individual fixed effects, this paper studies the impact of investor attention on the COVID-19 concept stocks in China's stock market through a set of fixed effect panel data models. Among them, investor attention focuses on macroeconomy, stock market and the COVID-19 pandemic, respectively, while stock indicators cover return, volatility and turnover. In addition, this paper also examines the heterogeneity influence of investor attention on the COVID-19 concept stocks from the perspective of time and stock classification. Findings Findings indicate that the attention to macroeconomy does not have a statistically significant effect on the return, unlike the attention to stock market and COVID-19 incident. Three types of investor attention have significant positive effects on the volatility and turnover rate. During the outbreak of the domestic epidemic, the impact of investor attention was significantly higher than that during the outbreak of the epidemic overseas. A finer-grained analysis shows that the attention to stock market has significantly increased the return of preventive type and treatment type stocks, while diagnostic-related stocks have been most affected by the attention to COVID-19 incident. Research limitations/implications The major limitation of this work is the construction of investor attention. Although Baidu index is widely used, investor attention can be assessed more accurately based on more unstructured data. In addition, the effect of the COVID-19 can also be investigated in a longer time domain. Further research can be combined with the dynamics of the COVID-19 pandemic to more comprehensively evaluate its impact on the stock market. Originality/value The research proves that investor attention plays an important role in stock pricing and provides empirical evidence on the behavioral foundations of the conceptual sector of the stock market under uncertainty. It also has practical implications for regulators and investors interested in conducting accurate asset allocation and risk assessment.

Corporate governance and stock performance: The case of COVID-19 crisis

This paper analyzes the impact of COVID-19 on firm-level stock behaviors (including stock price volatility, trading volume and stock returns). Using US data, this paper examines whether confirmed cases (and deaths) of COVID-19 or COVID-19-associated online searches affect stock behaviors. The results show that our five COVID-19 proxies are all positively associated with stock price volatility and trading volume and negatively associ-ated with stock returns. This paper further investigates the mitigating effect of corporate governance (viz., board and ownership structures) in this COVID-19 crisis. Overall, the results suggest that good corporate governance can mitigate the impact of COVID-19 on stock price volatility and trading volume but may not help to enhance stock returns. This paper also considers key policies used to tackle the COVID-19 pandemic and finds that government intervention plays an important role in stabilizing stock markets in this COVID-19 crisis. (c) 2021 Elsevier Inc. All rights reserved.