{"title":"Social Media Data-Driven Sentiment Analysis for COVID-19 and COVID-19 Vaccines","authors":"Ghaida S. Alorini, D. Rawat","doi":"10.1109/GHTC55712.2022.9911056","DOIUrl":"https://doi.org/10.1109/GHTC55712.2022.9911056","url":null,"abstract":"In this paper, we analyze social media data (e.g., tweets) related to coronavirus disease 2019 (COVID-19) and COVID-19 vaccines. The main objective is to explore daily COVID-19 cases and vaccine rates in addition to analyzing sentiments and discussions related to COVID-19 vaccination on social media, e.g., Twitter. During the early days of the pandemic, there were rapid developments of vaccines that can prevent the novel COVID-19. However, the potential hurdles of developing COVID-19 vaccines faster than any other conventional vaccine has made some people apprehensive about taking the COVID-19 vaccine. Since social media keeps individuals connected locally and globally, Twitter as a social networking platform is a great way to collect information on tweets related to the coronavirus vaccine. Specifically, this paper studies various data analytic tools that can help study the changes in users’ opinions and emotions related to coronavirus vaccines, as well as studying the coronavirus cases and vaccine rates globally. Furthermore, this study will enable individuals to get real-time insights into the sentiments of COVID-19 vaccines based on social media tweets.","PeriodicalId":370986,"journal":{"name":"2022 IEEE Global Humanitarian Technology Conference (GHTC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126708015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Pietrosemoli, Marco Rainone, M. Zennaro, C. Mikeka
{"title":"Massive RF Simulation Applied to School Connectivity in Malawi","authors":"E. Pietrosemoli, Marco Rainone, M. Zennaro, C. Mikeka","doi":"10.1109/GHTC55712.2022.9910987","DOIUrl":"https://doi.org/10.1109/GHTC55712.2022.9910987","url":null,"abstract":"Providing Internet connectivity to schools has been identified as paramount for development, for instance in the Giga project, cosponsored by ITU and UNICEF, with the goal of connecting every school to the Internet by 2030. For a country wide deployment, it is imperative to perform a thorough planning of the whole installation, using radio frequency (RF) propagation models. While statistical models based on empirical RF propagation data gathered in different scenarios can be employed, for point to point links at microwave frequencies the existence of a clear line of sight (LOS) is normally a prerequisite. The Irregular terrain model which makes use of digital elevation maps (DEM) has proved quite effective for simulating point to point links, but its application to a great number of links becomes time consuming, so we have developed an automated framework to perform this task. As a case study we have applied this framework in the planning of a project mired at providing connectivity to primary and secondary schools all over the country of Malawi.","PeriodicalId":370986,"journal":{"name":"2022 IEEE Global Humanitarian Technology Conference (GHTC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116207554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}