论文标题
通过社交网络采矿调查Covid-19对教育的影响
Investigating the Impact of COVID-19 on Education by Social Network Mining
论文作者
论文摘要
Covid-19病毒一直是2020年和2021年社交网络上讨论最多的主题之一，并影响了全球经典的教育范式。在这项研究中，在GeOnames地理数据库的帮助下，考虑并将许多与Covid-19病毒和教育有关的推文都被考虑和地理标记了，其中包含大量的地点名称。为了检测用户的感觉，使用基于罗伯塔语言的模型进行情感分析。最后，我们获得了数量众多的COVID-19确认病例的国家的总，正和负推文频率的趋势。调查结果表明，推文频率的趋势与几个国家确认案件的官方统计数据之间存在相关性。
The Covid-19 virus has been one of the most discussed topics on social networks in 2020 and 2021 and has affected the classic educational paradigm, worldwide. In this research, many tweets related to the Covid-19 virus and education are considered and geo-tagged with the help of the GeoNames geographic database, which contains a large number of place names. To detect the feeling of users, sentiment analysis is performed using the RoBERTa language-based model. Finally, we obtain the trends of frequency of total, positive, and negative tweets for countries with a high number of Covid-19 confirmed cases. Investigating the results reveals a correlation between the trends of tweet frequency and the official statistic of confirmed cases for several countries.
