ABSTRACT
This study investigates public sentiments and the essential topics of discussion on Africa's innovation amidst COVID-19. Web scraping techniques were used to collect and parse data from Twitter platform using the keywords "Africa Innovation COVID-19". A total of 54,318 cleaned English tweets were gathered and analysed using Twint Python Libraries. Our sentiment analysis findings revealed that 28,084 tweets (52 per cent) were positive, 21,037 (39 per cent), and 5197 (9 per cent) of tweets were neutral and negative, respectively, for Polarity sentiments. Notably, Healthcare, Imagination, Support, Webinar, Learning, Future, Rwanda, and Challenge were the most discussed topics on Africa's innovation during COVID-19. The topic labelling sentiments on the themes identified were positive, neutral, and negative, respectively. The study also revealed a cluster relationship between all identified topics. The relationship among these themes divulged how COVID-19 is positively shaping social and technological innovation in Africa. The study further presented practical implications to better position African leaders and policymakers to capitalise on the current innovation ecosystems and institutional capacities to transform the continent into a digital and innovation hub. The research concludes with theoretical recommendations and study limitations that will guide researchers and academicians in conducting future research in the subject area.
ABSTRACT
The current educational disruption caused by the COVID-19 pandemic has fuelled a plethora of investments and the use of educational technologies for Emergency Remote Learning (ERL). Despite the significance of online learning for ERL across most educational institutions, there are wide mixed perceptions about online learning during this pandemic. This study, therefore, aims at examining public perception about online learning for ERL during COVID-19. The study sample included 31,009 English language Tweets extracted and cleaned using Twitter API, Python libraries and NVivo, from 10 March 2020 to 25 July 2020, using keywords: COVID-19, Corona, e-learning, online learning, distance learning. Collected tweets were analysed using word frequencies of unigrams and bigrams, sentiment analysis, topic modelling, and sentiment labeling, cluster, and trend analysis. The results identified more positive and negative sentiments within the dataset and identified topics. Further, the identified topics which are learning support, COVID-19, online learning, schools, distance learning, e-learning, students, and education were clustered among each other. The number of daily COVID-19 related cases had a weak linear relationship with the number of online learning tweets due to the low number of tweets during the vacation period from April to June 2020. The number of tweets increased during the early weeks of July 2020 as a result of the increasing number of mixed reactions to the reopening of schools. The study findings and recommendations underscore the need for educational systems, government agencies, and other stakeholders to practically implement online learning measures and strategies for ERL in the quest of reopening of schools.
ABSTRACT
This study examined the effect of socio-economic features of low-income communities and COVID-19 related cases in New York City. The study developed hypotheses and conceptual framework of low-income communities and COVID-19 associated cases based on literature and theoretical review. The proposed framework was then tested using Structural Equation Model (SEM) with secondary data collected from New York Health and Mental Hygiene Department, US Census Bureau, and the Centers for Disease Control and Prevention. The findings revealed that unfavourable working conditions, underlying health conditions, and poor living conditions significantly and positively affects the number of COVID-19 confirmed cases. The study further revealed a positive and significant relationship between confirmed COVID-19 cases and COVID-19 related deaths. Theoretically, this study provides empirical results and a conceptual framework that could be used by other researchers to investigate low-income communities and COVID-19 related topics. Practically, this study called on the federal and state governments to effectively apply the health justice approach to eliminate healthcare discrimination for people living in low-income and marginalised communities as well as providing accessible, safe housing for the more vulnerable who need a place to self-quarantine due to COVID-19 exposure. Further practical and theoretical implications policies are discussed.