ﻻ يوجد ملخص باللغة العربية
We present a corpus of 7,500 tweets annotated with COVID-19 events, including positive test results, denied access to testing, and more. We show that our corpus enables automatic identification of COVID-19 events mentioned in Twitter with text spans that fill a set of pre-defined slots for each event. We also present analyses on the self-reporting cases and users demographic information. We will make our annotated corpus and extraction tools available for the research community to use upon publication at https://github.com/viczong/extract_COVID19_events_from_Twitter
The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide which resulted in polarization of individu
We introduce the well-established social scientific concept of social solidarity and its contestation, anti-solidarity, as a new problem setting to supervised machine learning in NLP to assess how European solidarity discourses changed before and aft
The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms -- a f
We investigate predictors of anti-Asian hate among Twitter users throughout COVID-19. With the rise of xenophobia and polarization that has accompanied widespread social media usage in many nations, online hate has become a major social issue, attrac
The outbreak COVID-19 virus caused a significant impact on the health of people all over the world. Therefore, it is essential to have a piece of constant and accurate information about the disease with everyone. This paper describes our prediction s