COVID-19 infection has been a major topic of discussion on social media platforms since its pandemic outbreak in the year 2020. From daily activities to direct health consequences, COVID-19 has undeniably affected lives significantly. In this paper, we especially analyze the effect of COVID-19 on education by examining social media statements made via Twitter. We first propose a lexicon related to education. Then, based on the proposed dictionary, we automatically extract the education-related tweets and also the educational parameters of learning and assessment. Afterwards, by analyzing the content of the tweets, we determine the location of each tweet. Then the sentiments of the tweets are analyzed and examined to extract the frequency trends of positive and negative tweets for the whole world, and especially for countries with a significant share of COVID-19 cases. According to the analysis of the trends, individuals were globally concerned about education after the COVID-19 outbreak. By comparing between the years 2020 and 2021, we discovered that due to the sudden shift from traditional to electronic education, people were significantly more concerned about education within the first year of the pandemic. However, these concerns decreased in 2021. The proposed methodology was evaluated using quantitative performance metrics, such as the F1-score, precision, and recall.

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.

The Internet of things (IoT) continues to “smartify” human life while influencing areas such as industry, education, economy, business, medicine, and psychology. The introduction of the IoT in psychology has resulted in various intelligent systems that aim to help people—particularly those with special needs, such as the elderly, disabled, and children. This paper proposes a framework to investigate the role and impact of the IoT in psychology from two perspectives: (1) the goals of using the IoT in this area, and (2) the computational technologies used towards this purpose. To this end, existing studies are reviewed from these viewpoints. The results show that the goals of using the IoT can be identified as morale improvement, diagnosis, and monitoring. Moreover, the main technical contributions of the related papers are system design, data mining, or hardware invention and signal processing. Subsequently, unique features of state-of-the-art research in this area are discussed, including the type and diversity of sensors, crowdsourcing, context awareness, fog and cloud platforms, and inference. Our concluding remarks indicate that this area is in its infancy and, consequently, the next steps of this research are discussed.

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