The outbreak of the Coronavirus disease (COVID-19) leads to an outbreak of pandemic information in major online social networks (OSNs). In the constantly changing situation, OSNs are becoming a critical conduit for people in expressing opinions and seek up-to-the-minute information. Thus, social behaviour on OSNs may become a predictor or reflection of reality. This paper aims to study the social behaviour of the public in the Greater Region (GR) and related countries based on Twitter information with machine learning and representation learning methods. We find that tweets volume only can be a predictor of outbreaks in a particular period of the pandemic. Moreover, we map out the evolution of public behaviour in each country from 2020/01/22 to 2020/06/05, figuring out the main differences in public behaviour between GR and related countries. Finally, we conclude that tweets volume of anti-contiguous measures may affect the effeteness of the government policy.
An information outbreak occurs on social media along with the COVID-19 pandemic and leads to an infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its negative impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decisions to participate in diffusing certain information has not been studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures due to its special role in consolidating public efforts to slow down the spread of the virus. Through our collected Twitter dataset, we validate the existence of the spillover effects. Building on this finding, we propose extensions to three cascade prediction methods based on Graph Neural Networks (GNNs). Experiments conducted on our dataset demonstrated that the use of the identified spillover effects significantly improves the state-of-the-art GNN methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 messages.
The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.
We validate whether social media data can be used to complement social surveys to monitor the public's COVID-19 vaccine hesitancy. Taking advantage of recent artificial intelligence advances, we propose a framework to estimate individuals' vaccine hesitancy from their social media posts. With 745,661 vaccine-related tweets originating from three Western European countries, we compare vaccine hesitancy levels measured with our framework against that collected from multiple consecutive waves of surveys. We successfully validate that Twitter, one popular social media platform, can be used as a data source to calculate consistent public acceptance of COVID-19 vaccines with surveys at both country and region levels. In addition, this consistency persists over time although it varies among socio-demographic sub-populations. Our findings establish the power of social media in complementing social surveys to capture the continuously changing vaccine hesitancy in a global health crisis similar to the COVID-19 pandemic.
The outbreak of the COVID-19 pandemic triggers infodemic over online social networks. It is thus important for governments to ensure their official messages outpace misinformation and efficiently reach the public. Some countries and regions that are currently worst affected by the virus including Europe, South America and India, encounter an additional difficulty: multilingualism. Understanding the specific role of multilingual users in the process of information diffusion is critical to adjust their publishing strategies for the governments of such countries and regions. In this paper, we investigate the role of multilingual users in diffusing information during the COVID-19 pandemic on popular social networks. We collect a large-scale dataset of Twitter from a populated multilingual region from the beginning of the pandemic. With this dataset, we successfully show that multilingual users act as bridges in diffusing COVID-19 related information. We further study the mental health of multilingual users and show that being the bridges, multilingual users tend to be more negative. This is confirmed by a recent psychological study stating that excessive exposure to social media may result in a negative mood.
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