Journal of Information & Knowledge Management

During the COVID-19 outbreak that took place in early 2020, restrictions on human activities were imposed by government-mandated lockdowns and stay-at-home orders. In this study, we analyse the impact of reduced anthropogenic activities on the detection of seismic events. We hypothesise and show that with reduced background noise levels due to the COVID-19 lockdowns, low-magnitude earthquakes are more easily detectable. We investigate the magnitudes of earthquakes recorded at the seismometers before and after COVID-19 lockdowns for two regions — Cascadia Subduction Zone and New Zealand. Gutenberg–Richter law, which gives a relationship between the number of earthquakes and their magnitudes (b-value), was applied in these two areas. Our results point to an increase in detection of smaller-magnitude earthquakes, as observed by an increased b-value during the COVID-19 period compared to those obtained in the pre-COVID time periods. Previous studies have shown that changes in b-value of an area over a sustained period of time affect the short-time probabilistic risk assessment. The variability of b-value also gives useful insights into the prevailing stress state of the region, crustal heterogeneity, pore pressure and tectonic setting of the area.

Context: After its emergence in China in 2019, the coronavirus disease (COVID-19) emerged in Bangladesh on March 8, 2020. Since then, this virus has got wide coverage in print and electronic media. Rumours and fake news have also begun to spread across online and offline media. Objectives: This study aimed to discuss how social media is being used to spread fake news during the COVID-19 pandemic and the reasons behind this. In addition, this paper examines the overall use of social media by university students and their role in sharing fake news on social media. Methodology: An online survey was used to collect data from students who had a minimum of one social media account. Facebook groups, messengers and emails were used for data collection and 264 responses were recorded. Findings: This study found that 92.8% of students received COVID-19-related news on social media, 61% experienced fake news in many cases. This is because most users of social media share news without checking its authenticity and reliability, and without checking facts against reliable sources. It was also found that most students were fairly confident in detecting fake news and checked the authenticity of the news before sharing it on social media. Originality/Value: This is the initial effort in Bangladesh to recognise the role of social media in propagating false news during the COVID-19 pandemic. However, the sample size of this study was very small. Further studies with larger sample sizes may reveal a more evident understanding of this topic.

The COVID-19 pandemic has had a devastating effect on the mental health of people from different backgrounds; these effects have been particularly acute among the lower-income households and in slums. There has been a steep rise in mental illness and behaviours associated with it since 2020, especially in the slums characterised by poverty, poor housing, high density and unhealthy environments. This paper aims to examine the effect of COVID-19 on the mental health of people living in the slums of Vijayawada and Visakhapatnam in the state of Andhra Pradesh in India. The results of the study confirm that more than three-fourth of people suffer from mental stress due to the spread of COVID-19 and the lockdown imposed by the government. Also, 80% of those surveyed stated that stress affected their decision-making. The study also confirms that the number of dependents, monthly income, number of living rooms, maintenance of physical distancing norms, avoidance of social gatherings, government support, health insurance and living in a rented house are significantly related to the mental stress of people living in the slums in Andhra Pradesh in India.

Context: From the past few years, Application Programming Interface (API) is widely used for mobile- and web-based application developments. Software developers can integrate third-party services into their projects to achieve their development goals efficiently using APIs; however, with the rapid increase in the number of APIs, the manual selection of Mashup-oriented API is becoming more difficult for the developer. Objective: In the COVID-19 pandemic, everyone wants an update about the latest Standard Operating Procedures (SOPs) and the latest information on COVID-19. Additionally, a software developer wants to develop an application that provides the SOPs and latest information of COVID-19; a developer can add these functionalities into an application using COVID-19-based APIs. Moreover, the current work aims at proposing a COVID-19-based API recommendation system for the developers. Method: In this study, we propose a COVID-19-based API recommendation system for developers. The recommendation system takes a developer query as input and recommends top-3 APIs and supported features, which help the developer during software development. Furthermore, the proposed COVID-19-based API recommendation system ensures the maximum participation of the developers by validating the recommended APIs and recommendation system from the expert developers using research questionnaires. Results: Additionally, the proposed COVID-19-based API recommendation system’s output is validated by expert developers and evaluated on 120 expert developers’ queries. In addition, experiment results show that single value decomposition achieves better prediction. Conclusion: We conclude that it is significantly important to recommend APIs along with supported features to the developer for project development, and future work is needed to take more developer’s queries also to build Integrated Development Environment for the developers.

Blended Learning (BL) essentially fuses high-tech digital learning with traditional ones to involve students in “network learning”, which helps in transcending physical boundaries. Especially during the ongoing COVID-19 pandemic, BL has been embraced as a realistic possibility to ensure seamless knowledge distribution regardless of time and space. Knowledge Management (KM)-based approaches are typically used to collect, coordinate, and control processes that may be utilised efficiently to both recognise and address customer needs. This study aims to explain how to apply KM techniques within the BL environment to increase educational excellence and quality. We used a quantitative approach, specifically by adopting an online questionnaire circulated to about 272 undergraduate students, primarily from the Department of Information Science and Library Management (ISLM) of the University of Dhaka, Bangladesh. We received completed answers from 74 students; the data gathered were analysed using Statistical Package for the Social Sciences (SPSS) version 20. The findings reveal that KM can be used with BL, especially during crisis times like the one we have today, as it includes integrated tasks and online and e-learning. Finally, the study also proposed an intuitive model for applying BL through KM. Importantly, this study seems to be among the first to examine the students’ perceptions about BL and KM integration during a pandemic. Therefore, this research would possibly prompt further research on different aspects of the combined learning process at different universities, especially within Bangladesh.

In the COVID-19 era, the use of social media platforms has significantly increased leading to misinformation being produced whose management is quite necessary for the domain experts, such as the Reddit social platform where people disseminate extensive information about their health issues using relevant posts and comments. The management of misinformation about COVID-19 impact on mental illness could be quite beneficial for the domain experts. In this regard, we proposed a two-step methodology which could aid domain experts to manage and group the posts and comments information with respect to COVID-19 impact on mental illness. First, we extract the information of well-known mental illnesses (such as depression, anxiety, OCD and PTSD) from the Raddit platform. Second, we leverage the capabilities of unsupervised learning algorithms and text categorisation approach to manage the information. We also proposed the evaluation model to assess the efficacy of the proposed method according to expert opinion. The experimental results indicate the efficacy of the proposed method. Moreover, we observed fuzzy c-means as an outperformed learner (with $\mathrm{\text{ARI}}=0.76$) as compared to K-means ($\mathrm{\text{ARI}}=0.70$) and Agglomerative ($\mathrm{\text{ARI}}=0.69$).

Pair trading strategy is a well-known profitable strategy in stock, forex, and commodity markets. As most of the world stock markets declined during COVID-19 period, therefore this study is going to observe whether this strategy is still profitable after COVID-19 pandemic. One of the powerful algorithms of DBSCAN under the umbrella of unsupervised machine learning is applied and three clusters were formed by using market and accounting data. The formation of these three clusters was based on book value per share, earning per share, classification of sector, market capitalisation and with other factors formed from PCA on the returns of daily data of six months of the 80 sample firms for year 2019–2020. An average of $-0.32\%$ average excess monthly return with Sharpe ratio of $-0.0012$ and Treynor ratio of $-0.0231$ is to be observed in COVID-19 pandemic period. However, the result of risk-adjusted performance under Jensen’s alpha is observed to be insignificant. The policy implication of this study, for different portfolios and fund managers is suggested to use machine learning approach to get positive and higher returns for their clients.

The widespread transmission of misinformation regarding the COVID-19 pandemic on social media has become a severe concern for various reasons such as containing the spread of the virus, taking preventive measures, and so on. According to the recent studies, misinformation and conspiracy theories spread on social media have hampered efforts to limit the infection, which has been exacerbated in some instances by politicians and celebrities. Misunderstandings about COVID-19 and wearing a mask sparked much debate. As time went on, a sizable portion of the population continued to refuse to wear masks, owing to extrinsic considerations, such as politics, ideology, personal views, and health concerns. In this study, we look at the concerns surrounding three Twitter hashtags (#masks, #maskup, and #maskoff) in order to understand better how social noise can lead to unintended misinformation. Sentiment analysis, topic modelling, and contextual analysis were used to compare and contrast two datasets relevant to these hashtags, one gathered in 2020 and the other in 2021. According to sentiment analysis, people’s emotions differed between hashtags, and the majority of tweets were based on social media users’ personal opinions. Topic modelling results revealed the prevalence of social noise leading to the unintended spread of misinformation on Twitter. The content analysis results show that while the #maskoff hashtag is used to resist masking influenced by factors, such as misinformation, conspiracy theories, and ideology, the #masks and #maskup hashtags were generally positive and used more to raise awareness of the benefits of wearing masks.

In 2020, COVID-19 became one of the most critical concerns in the world. This topic is even still widely discussed on all social networks. Each day, many users publish millions of tweets and comments around this subject, implicitly showing the public’s ideas and points of view regarding this subject. In this regard, to extract the public’s point of view in various countries at the early stages of this outbreak, a dataset of Coronavirus-related tweets in the English language has been collected, which consists of more than two million tweets starting from 23 March until 23 June 2020. To this end, we first use a lexicon-based approach with the GeoNames geographic database to label each tweet with its location. Next, a method based on the recently introduced and widely cited Roberta model is proposed to analyse each tweet’s sentiment. Afterwards, some analysis showing the frequency of the tweets and their sentiments is reported for each country and the world as a whole. We mainly focus on the countries with Coronavirus as a hot topic. Graph analysis shows that the frequency of the tweets for most countries is significantly correlated with the official daily statistics of COVID-19. We also discuss some other extracted knowledge that was implicit in the tweets.

Social media platforms have become an integral source to spread and consume information. Twitter has emerged as the fastest medium to disseminate any information. This blind trust on social media has raised the concern to quantify the truth or fakeness of what we are consuming. During COVID-19, the usage of social platforms has dramatically increased in everyone’s life. It is high time to distinguish between the type of users involved in spreading fake and true news content. Our study aims to answer two questions. First, what is the complex network structure of users involved in spreading any news? How two types (i.e. Fake and True) of networks are different in terms of network topology. Second, what is the role of influential users in spreading both types of news? To answer these, the fake and true news of COVID-19 are collected which have been classified by fact-checking websites. Diffusion networks have been created to perform the experiments. Network topological analysis revealed that despite having differences, most properties show similar behaviour. Though, it can be stated that during COVID-19, behaviour of users remained the same in spreading fake or true content. Resilience analysis discovered that fake networks were more densely connected than true ones. There were more centric nodes or influential users were present in Fake news networks than True news networks.