Social media can be used to monitor the adverse effects of vaccines. The goal of this project is to develop a machine learning and natural language processing approach to identify COVID-19 vaccine adverse events (VAE) from Twitter data. Based on COVID-19 vaccine-related tweets (1 December 2020–1 August 2021), we built a machine learning-based pipeline to identify tweets containing personal experiences with COVID-19 vaccinations and to extract and normalize VAE-related entities, including dose(s); vaccine types (Pfizer, Moderna, and Johnson & Johnson); and symptom(s) from tweets. We further analyzed the extracted VAE data based on the location, time, and frequency. We found that the four most populous states (California, Texas, Florida, and New York) in the US witnessed the most VAE discussions on Twitter. The frequency of Twitter discussions of VAE coincided with the progress of the COVID-19 vaccinations. Sore to touch, fatigue, and headache are the three most common adverse effects of all three COVID-19 vaccines in the US. Our findings demonstrate the feasibility of using social media data to monitor VAEs. To the best of our knowledge, this is the first study to identify COVID-19 vaccine adverse event signals from social media. It can be an excellent supplement to the existing vaccine pharmacovigilance systems.

Due to Coronavirus outbreaks, almost all universities carried out teaching online by using a variety of learning platforms such as web-based learning, LMS combined with conducting video conferences through Zoom Meeting or Google Meet and the like. Although Ho Chi Minh City Open University (HCMCOU) made great efforts to invest state-of-the-art facilities and well-trained teaching team in the online educational process, students seemed not to be satisfied with this training system. Particularly, in English-speaking classes, students seemed to be in stock. Thus, this paper aims to explore the challenges encountered by major English learners towards learning English speaking skills online and then suggest some possible solutions for such problems that existed. To conduct this study, 35 major English freshmen joining in a speaking class of Schools of Advanced studies at HCMCOU were asked to respond questionnaire survey, and then five hard-working students were selected to participate in in-depth interviews relevant to barriers of online learning and their expectation during Covid 19 period. The findings illustrated that majority of students expressed their neglected attitude towards learning speaking skills online in the Covid 19 pandemic when they had to cope with various problems of technological advances, Wi-Fi connection, significant characteristics of speaking skills, and sociolinguistic competence. With such challenges exposed, more project-based learning and more video conferences were expected to apply.

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