Practices and Challenges of Popularizing Digital Public Humanities During the COVID-19 Pandemic in Japan

Collecting material related to the COVID-19 pandemic is one of the essential works of public humanities. Within this context, the community archive project titled "????????@????(Corona Archive @ Kansai University)" began as a digital public history practice. However, promoting public humanities, which has not yet taken root in Japan, has been met with various challenges. Introducing the Corona Archive @ Kansai University, this chapter answers the following questions: What are the challenges in engaging the public in public humanities activities in Japan? How can public humanities practitioners ensure that public humanities take root in Japan? How should public humanities practitioners communicate and disseminate this work?. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022, corrected publication 2023.

Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices.

The use of voice recordings in both research and industry practice has increased dramatically in recent years-from diagnosing a COVID-19 infection based on patients' self-recorded voice samples to predicting customer emotions during a service center call. Crowdsourced audio data collection in participants' natural environment using their own recording device has opened up new avenues for researchers and practitioners to conduct research at scale across a broad range of disciplines. The current research examines whether fundamental properties of the human voice are reliably and validly captured through common consumer-grade audio-recording devices in current medical, behavioral science, business, and computer science research. Specifically, this work provides evidence from a tightly controlled laboratory experiment analyzing 1800 voice samples and subsequent simulations that recording devices with high proximity to a speaker (such as a headset or a lavalier microphone) lead to inflated measures of amplitude compared to a benchmark studio-quality microphone while recording devices with lower proximity to a speaker (such as a laptop or a smartphone in front of the speaker) systematically reduce measures of amplitude and can lead to biased measures of the speaker's true fundamental frequency. We further demonstrate through simulation studies that these differences can lead to biased and ultimately invalid conclusions in, for example, an emotion detection task. Finally, we outline a set of recording guidelines to ensure reliable and valid voice recordings and offer initial evidence for a machine-learning approach to bias correction in the case of distorted speech signals.

Competitive gamification in crowdsourcing-based contextual-aware recommender systems.

During the COVID-19 outbreak, crowdsourcing-based context-aware recommender systems (CARS) which capture the real-time context in a contactless manner played an important role in the "new normal". This study investigates whether this approach effectively supports users' decisions during epidemics and how different game designs affect users performing crowdsourcing tasks. This study developed a crowdsourcing-based CARS focusing on restaurant recommendations. We used four conditions (control, self-competitive, social-competitive, and mixed gamification) and conducted a two-week field study involving 68 users. The system provided recommendations based on real-time contexts including restaurants' epidemic status, allowing users to identify suitable restaurants to visit during COVID-19. The result demonstrates the feasibility of crowdsourcing to collect real-time information for recommendations during COVID-19 and reveals that a mixed competitive game design encourages both high- and low-performance users to engage more and that a game design with self-competitive elements motivates users to take on a wider variety of tasks. These findings inform the design of restaurant recommender systems in an epidemic context and serve as a comparison of incentive mechanisms for gamification of self-competition and competition with others.

Crowdsourcing and language learning habits and practices in Turkey, Bosnia and Herzegovina, the Republic of North Macedonia and Poland in the pre-pandemic and pandemic periods

The popularity of online crowdsourcing platforms was slowly increasing among language learners before the pandemic, but COVID-19 changed the educational systems worldwide. This study aims to uncover whether or not, and if ‘YES', how the attitudes and habits of language learners concerning the use of crowdsourcing materials in Turkey, Bosnia and Herzegovina, the Republic of North Macedonia and Poland changed during the pandemic. To compare the pre-and during the covid crowdsourcing tool usage, the cross-culturally appropriate questionnaire utilised in the pre-COVID-19 period was used again. The collected data were analysed qualitatively and quantitatively to identify the differences between the periods. The study's findings showed that the shift from face-to-face to online learning significantly affected the development of crowdsourcing platforms worldwide and their employment in the studied countries. The results also demonstrated that a combination of factors, such as reduced interactions with teachers and peers, an increase in workload, and a lack of support on the part of institutions, led to students taking responsibility for their learning. The number and characteristics of the popular platforms changed from country to country since expectations from students varied. © 2022 Ljubljana University Press, Faculty of Arts. All rights reserved.

Wisdom of Two Crowds: Misinformation Moderation on Reddit and How to Improve this Process - -A Case Study of COVID-19

Past work has explored various ways for online platforms to leverage crowd wisdom for misinformation detection and moderation. Yet, platforms often relegate governance to their communities, and limited research has been done from the perspective of these communities and their moderators. How is misinformation currently moderated in online communities that are heavily self-governed? What role does the crowd play in this process, and how can this process be improved? In this study, we answer these questions through semi-structured interviews with Reddit moderators. We focus on a case study of COVID-19 misinformation. First, our analysis identifies a general moderation workflow model encompassing various processes participants use for handling COVID-19 misinformation. Further, we show that the moderation workflow revolves around three elements: content facticity, user intent, and perceived harm. Next, our interviews reveal that Reddit moderators rely on two types of crowd wisdom for misinformation detection. Almost all participants are heavily reliant on reports from crowds of ordinary users to identify potential misinformation. A second crowd - participants' own moderation teams and expert moderators of other communities - provide support when participants encounter difficult, ambiguous cases. Finally, we use design probes to better understand how different types of crowd signals - -from ordinary users and moderators - -readily available on Reddit can assist moderators with identifying misinformation. We observe that nearly half of all participants preferred these cues over labels from expert fact-checkers because these cues can help them discern user intent. Additionally, a quarter of the participants distrust professional fact-checkers, raising important concerns about misinformation moderation. © 2023 ACM.

Facilitating Real-Time, Multidirectional Learning for Clinicians in a Low-Evidence Pandemic Response.

As COVID-19 was declared a health emergency in March 2020, there was immense demand for information about the novel pathogen. This paper examines the clinician-reported impact of Project ECHO COVID-19 Clinical Rounds on clinician learning. Primary sources of study data were Continuing Medical Education (CME) Surveys for each session from the dates of March 24, 2020 to July 30, 2020 and impact surveys conducted in November 2020, which sought to understand participants' overall assessment of sessions. Quantitative analyses included descriptive statistics and Mann-Whitney testing. Qualitative data were analyzed through inductive thematic analysis. Clinicians rated their knowledge after each session as significantly higher than before that session. 75.8% of clinicians reported they would 'definitely' or 'probably' use content gleaned from each attended session and clinicians reported specific clinical and operational changes made as a direct result of sessions. 94.6% of respondents reported that COVID-19 Clinical Rounds helped them provide better care to patients. 89% of respondents indicated they 'strongly agree' that they would join ECHO calls again.COVID-19 Clinical Rounds offers a promising model for the establishment of dynamic peer-to-peer tele-mentoring communities for low or no-notice response where scientifically tested or clinically verified practice evidence is limited.

Disaster Management Through Digital Platforms: Online Crowdfunding Communities Respond to the COVID-19 Pandemic

We study how digital crowdfunding platforms can help replenish the sudden economic deficiencies that accompany a global crisis. Specifically, we examine whether public schools, which suffered severe setbacks during the COVID-19 crisis, were able to generate support from online fundraising communities. We study how the shutdown of schools and the shift to online learning in the United States affected private fundraising on the DonorsChoose.org platform. We find evidence that, after the exogenous shock caused by stay-at-home orders, donations to schools increased and the increased level of concern moves toward high-need schools. Moreover, we find a shift in donation patterns, wherein donors swiftly adapted to renewed priorities and redistributed their resources to immediate needs around digital learning infrastructure. Our findings reveal the pivotal role digital platforms can play in facilitating community resilience during times of crisis. © 2022 IEEE Computer Society. All rights reserved.

The Role of Crowdfunding in Delivering Entrepreneurship Within the Hospitality Sector: A Case Study Analysis

Over the last decade, crowdfunding, has emerged as a hugely disruptive force within the financial landscape worldwide. Crowdfunding, the process of raising relatively small sums of money from the crowd, via the internet, enables entrepreneurs, particularly at the innovative and new start-ups stage, to access much needed funding, overcoming a "funding gap”. The growth of crowdfunding has been phenomenal. In 2019, an estimated €14 billion was crowdfunded worldwide (Statistia 2020) and the forecast is for the sector to grow to €30 billion by 2025 (Mordo Intelligence, 2020). Initially, crowdfunding gained prominence through funding creative and artistic projects, but over the last number of years, this appeal has spread across a diverse range of businesses and sectors (Bradford 2012 and Research and Markets 2022). In the wake of the 2007-2008 financial crisis and more recently the COVID-19 pandemic, crowdfunding offers entrepreneurs and businesses access to much needed seed funding, but also non-financial benefits in the form of market and product testing, media exposure and customer feedback. Crowdfunding is a relatively new domain for businesses in the hospitality sector. As noted by Belavin, Marinesi and Tsoukalas (2020), crowdfunding offers huge potential for the sector, who often face funding challenges thereby limiting new innovative start-ups, critical for the sector's long term viability. This case study examined how one entrepreneur in the hospitality sector, successfully crowdfunded an innovative business idea in the midst of the COVID-19 pandemic. The case traces the idea and the factors that shaped the decision to crowdfund. Additionally, the case examines the benefits and challenges involved in successfully crowdfunding the business idea and closes with the entrepreneur reflecting on the key learning from the experience. The contribution of this case study is twofold. Firstly, it serves to highlight the potential of crowdfunding as a funding source of enterprise development, particularly among new, innovative businesses. Secondly, it adds to the current debate, as noted by Belavin, Marinesi and Tsoukalas (2020), of the potential crowdfunding in fostering entrepreneurship and economic development within the hospitality sector. © 2022, Academic Conferences and Publishing International Limited. All right reserved.

'It all stems from relationship': Effectiveness of a crowdsourcing contest to elicit community ideas on how to build trust between healthcare providers and community about COVID-19

Objective The COVID-19 pandemic requires a nimble approach to building trust between healthcare providers and community. Crowdsourcing is one community-engaged approach that may be effective at engaging marginalised communities to identify ways to build trust. This early-stage innovation report assesses the effectiveness of using a crowdsourcing contest to elicit community ideas on how to build trust between healthcare providers and community about COVID-19 and promote community engagement about vaccines. Methods This mixed-methods study conducted a qualitative assessment of crowdsourcing contest entries and evaluated online community engagement via social media analytics (reach, video views, engagement). Themes from contest entries informed the development of community leader video interviews. Qualitative data from contest entries were digitally transcribed and analysed using axial coding. Results Contest participants (n=19) were European Americans (n=10), African Americans (n=8, 87%) and American Indians (n=1), the majority of whom identified as women (n=18) and were 18-80 years old. Contest entry recommendations included: (1) partner with community stakeholders and providers, (2) improve access to credible information from trusted sources, (3) use multiple channels of communication, and (4) use clear and plain language. Conclusion Crowdsourcing contests coupled with public education are beneficial community engagement tools to identify new ways to promote trust between medical professionals and diverse community members about COVID-19. Crowdsourcing contests also provide opportunity for partnership and critical dialogue between healthcare professionals and community leaders.Copyright © 2023 BMJ Publishing Group. All rights reserved.

Crowdsourced product returns in C2B e-commerce: a post-pandemic no-contact consumer incentive-based model

PurposeThis study aims to investigate the conditions for the financial feasibility of an incentive-based model for self-drop or crowdsourced drop of the product to be returned at designated drop boxes (thereby ensuring a contactless process).Design/methodology/approachConstraint-based non-linear mathematical modeling was done for cost differential with and without crowdsourcing. This was analyzed against returns on investment for the installed infrastructure. Scenarios were looked into from the linear, iso-elastic and logarithmic demand functions to identify the optimal incentive policy. The results were further evaluated using "willingness to return” for customer willingness for product returns via drop boxes.FindingsCrowdsourcing is viable when product returns are no more than 15%–20% of the overall products, with a logistics cost differential of 15%–25%. These were only viable when the product return incentive was within the range of 15%–20% of the product cost, as well as the penalty was in the range of 25 to 40% for wrong returns.Research limitations/implicationsThe findings are expected to aid the organizations in successfully designing product return policies while adhering to the post-COVID-19 norms, including contactless transactions and social distancing.Originality/valueThe study provides a look into the viability sensitivity of effective gains/profitability against the required level of service for returns, wrong returns, penalties and incentives for crowdsourcing in a developing country like India.

A Meta-framework for Spatiotemporal Quantity Extraction from Text

News events are often associated with quantities (e.g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events. This paper thus formulates the NLP problem of spatiotemporal quantity extraction, and proposes the first meta-framework for solving it. This meta-framework contains a formalism that decomposes the problem into several information extraction tasks, a shareable crowdsourcing pipeline, and transformer-based baseline models. We demonstrate the meta-framework in three domains-the COVID-19 pandemic, Black Lives Matter protests, and 2020 California wildfires-to show that the formalism is general and extensible, the crowdsourcing pipeline facilitates fast and high-quality data annotation, and the baseline system can handle spatiotemporal quantity extraction well enough to be practically useful. We release all resources for future research on this topic. © 2022 Association for Computational Linguistics.

Examining the Influence of Trustworthiness, Financial Rewards, and Admiration for Crowdsourcing in the Post COVID-19 Period

Over the past two decades, crowdsourcing activities have expanded considerably. More recently, the COVID-19 pandemic has radically changed the way people live and work, and the way organizations do business. So far, not many studies have analyzed if and to what extent trustworthiness can influence the admiration to practice crowdsourcing and could reward financial benefits in the COVID-19 period. Against this background, in this article, the aim is to investigate the influence of crowdfunding trustworthiness and financial rewards on the success of crowdsourcing activities. The analysis is made more complete by including technology leadership support as a moderator. With the help of the existing literature and theories, a research model has been developed conceptually, which was later validated using the partial least square-structural equation modeling technique on a sample of 319 responses from participants based in Europe and Asia. The study found that lucidity, gamification, exposure, and coordination along with financial rewards positively influence admiration for crowdsourcing, which, in turn, positively impacts successful crowdsourcing practices in the COVID-19 period. The study also shows that there is a significant moderating impact of technological leadership support on successful crowdsourcing practices in the COVID-19 period. IEEE

Crowd-Resourcing in Response to the COVID-19 Pandemic: A Resource Transformation Perspective

Driven by recent calls for more research that examines forms of crowdsourcing used to address social challenges, in this article, we contribute to the broader literature on open innovation and crowdsourcing by investigating how crowdsourcing platforms enable the transformation of crowd-based resources. We have focused on initiatives with broader social purposes, rather than those that are for-profit and single firm-driven, where the resulting resources are usually solely controlled by a specific organization. By analyzing 19 crowd-based initiatives with a similar context—responding to the coronavirus disease pandemic—we studied a variety of initiatives and identified three distinct types of crowdsourcing platforms that enable resource transformation: resource pooling;resource cocreation;and resource enabling beyond the platform boundaries. We depict how access to and control of resources vary across initiatives. We have framed our contribution as crowd-resourcing, providing a reference model for the design of platforms based on the type of involvement and expected degree of resource transformation. IEEE

The Polish Market of Equity Crowdfunding - Pre and During Pandemic COVID-19 Situation

The main aim of this paper is to identify the changes on the Polish market of equity crowdfunding on the cusp of disturbing external factors, such as the pandemic COVID-19. The authors analyzed data from the four leading equity crowdfunding platforms: beesfund, crowdway, findfunds and crowdconnect through the prism of the basic efficiency factors. Comparing the reached results within the time before pandemic to the period of years 2020 and 2021 shows that Polish equity crowdfunding market is very resistant to such unpredictable conditions as pandemic COVID-19 and develops in a very stable way, in meantime experiencing a challenging process of professionalization and matching the rules of law to the requirements of the EU. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.

A Citizen Science Approach for Analyzing Social Media With Crowdsourcing

Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among the millions of posts being added every day can be difficult, and in current approaches developing an automatic data analysis project requires time and technical skills. This work presents a new approach for the analysis of social media posts, based on configurable automatic classification combined with Citizen Science methodologies. The process is facilitated by a set of flexible, automatic and open-source data processing tools called the Citizen Science Solution Kit. The kit provides a comprehensive set of tools that can be used and personalized in different situations, particularly during natural emergencies, starting from images and text contained in the posts. The tools can be employed by citizen scientists for filtering, classifying, and geolocating the content with a human-in-the-loop approach to support the data analyst, including feedback and suggestions on how to configure the automated tools, and techniques to gather inputs from citizens. Using flooding scenario as a guiding example, this paper illustrates the structure and functioning of the different tools proposed to support citizens scientists in their projects, and a methodological approach to their use. The process is then validated by discussing three case studies based on the Albania earthquake of 2019, the Covid-19 pandemic, and the Thailand floods of 2021. The results suggest that a flexible approach to tools composition and configuration can support a timely setup of an analysis project by citizen scientists, especially in case of emergencies in unexpected locations. © 2013 IEEE.

Screening for COVID-19 via Acoustics Using Artificial Intelligence

This research presents deep learning concepts used on crowd-sourced audio files of people who have had or are having positive COVID-19 symptoms to help determine how to diagnose them just by analyzing their telephone calls to the artificial intelligence machine as compared to healthy subjects for medical screening. First the (.wav file) samples are processed by audio means, using the python code library librosa and the resulting output is then converted to images, specifically log-power spectrograms. Then those images are processed using the Convolutional Neural Networks (CNN) computer vision methods. The proposed model, trained on 70% of the data, validated on 20% of the data and finally tested on 10% of the data, gave good initial results of 85% Area Under Receiver Operating Characteristics (ROC) Curve (AUC). The Coswara crowd-sourced dataset contains 1433 healthy and 681 positive samples. This proposed method may improve patient outcomes, reduce cost of testing, and prevent false negative test results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Blockchain-Based Crowdsourcing Loan Platform for Funding Higher Education in Developing Countries

In developing countries, funding is a significant obstacle to receiving higher education. Brilliant but needy students cannot complete their studies since their parents are unemployed and their countries' economies are poor. As a result, the students' talents are not harnessed to their full potential. In order to help students obtain higher education and harness their full potential, governments provide student loans to students in higher education. The government provides loans to students through the ministry of education. The students pay back the loan with interest when they start working. Governments have been the sole funders of student loans. The emergence of COVID-19 and the Russia-Ukraine war have resulted in a global economic crisis. Because of the global economic crisis, the government's spending has increased. In order to help reduce the burden of government and thereby reduce spending, we intend to revolutionize the student loan program through blockchain and crowdsourcing. This work presents a blockchain-based crowdsourcing decentralized loan platform where investors will be brought on board to provide funds for students in higher education. The platform will allow students to apply for loans from investors through registered financial institutions. The students will pay back the loans with interest when they enter the workforce. The proposed platform will allow students to fund their education, investors will get interest on the money they invest, and governments can channel the money they put into student loan programs into other avenues. We perform a thorough security analysis and back the efficiency of our work with numerical results. © 2013 IEEE.

Sustainable crowdsourced delivery: A study of technological, health, value, and trust antecedents of consumer loyalty

Crowdsourced delivery has various advantages over conventional delivery methods, including a decrease in emissions and road congestion. These benefits grow as consumer loyalty is established due to network externalities. This study seeks to identify the factors influencing customer loyalty to crowdsourced delivery through the unified theory of acceptance and use of technology, the health belief model, the perceived value theory, and the trust theory. First, a questionnaire was administered to 500 respondents in Singapore, and the data was analyzed using structural equation modeling. The findings show that technology and health belief constructs have direct impacts on the perceived value of crowdsourced delivery, while perceived value has direct and indirect effects on consumer loyalty through trust. Overall, this study contributes to the literature theoretically and practically by developing a paradigm for understanding the growth of customer loyalty to crowdsourced delivery from the perspectives of consumers and health beliefs. It also offers operators and policymakers concrete areas for improvement in resource allocation, security, and marketing to increase overall consumer loyalty to crowdsourced delivery. © 2023 The Authors

Acoustic contamination assessment during the transition between the COVID-19 restrictions and reactivation: A exploratory analysis in Guayaquil

Noise pollution is becoming a critical health risk for city life. In 2020, the COVID-19 pandemic forced many cities to implement several mobility restrictions. These restrictions changed human activity patterns and decreased the noise levels and noise pollution that often affect urban settings. As the number of infections decreased, so did the outdoor activities, influencing the population's perception of noise. This paper aims to evaluate the changes in noise levels associated with mobility restrictions between 2020 and 2021 in Guayaquil, Ecuador. This study used crowdsourcing with the help of smartphones and mobile applications to collect geo-referenced environmental noise data. The data was used to generate noise maps in different time frames. Finally, noise level maps were created using GIS-based tools to identify the urban areas that experienced the highest noise level variation during the study period. The results show that the most significant noise increase occurred at night. Furthermore, when analyzing noise level changes in different urban areas, the western area of Guayaquil was the one that experienced the most significant noise level variation. Findings inform the perception of noise pollution and could potentially serve as a reference for decision-makers during the proposal of public policies that ensure a better quality of life for its citizens. © 2023 The Authors

An Evidence Study of Long-term Impacts on Mobility Patterns Brought by COVID-19

The outbreak of COVID-19 at the end of 2019 has posed an enormous threat to people's physical and psychological health, especially those who are infected during the epidemic. Understanding how the infected people behaved during the pandemic and whether long-term effects are exerted even after they were cured is essential for guiding them to conduct a more comprehensive recovery. Large scale crowd-sourced data provides a chance to investigate their behavior patterns. In this paper, we explore the possible differences in mobility patterns between the infected and the uninfected, relying on a large volume of crowd -sourced location data contributed by smartphone users consisting of 11,414 infected cases and 12,793 uninfected people between Jun. 1, 2019 and Dec 31, 2020 in Wuhan, China. We characterize mobility distinctions of the two groups by introducing five mobility indicators that accurately capture spatio-temporal patterns of human mobility. We reveal that the infected kept higher mobility level during the pandemic. Moreover, the COVID-19 caused lower recovery efficiency on mobility of the infected, including later recovery time, lower speed and worse status. © 2022 IEEE.