Monitoring Cyber SentiHate Social Behavior During COVID-19 Pandemic in North America

Alzamzami, F.; El Saddik, A.

With communications being shifted to online social networks (OSNs) as a result of travel and social restrictions during COVID-19 pandemic, the need has arisen for discovering emerging trends and concerns formed during the pandemic as well as understanding the corresponding online social behavior that reflects its offline settings. The online connectivity of devices through social media is one example of Internet of Things (IoT) in which a two-way communication between societies and officials, could be created. Therefore, it is possible to monitor people's behavior through OSNs, especially during pandemics, to prevent potential social and psychological instabilities that might lead to undesired consequences. This is particularly crucial for governmental and non-governmental organizations to ensure the stability and well-being in societies. In response, we propose a pandemic-friendly real-time framework for monitoring cyber social behavior by utilizing unsupervised and supervised learning approaches. Two BERT-based supervised classifiers are trained and constructed to analyze two types of online social behaviors, hate and sentiment. Unsupervised framework is proposed for OSNs data exploration and coherent interpretation that is used as a complementary tool to facilitate the analysis of online social behaviors during pandemics. Extensive experimentation and evaluation have been conducted to validate the effectiveness of the proposed work. Our results have shown superior performance of our BERT-based models in two classification tasks: 1) binary classification for hate behavior detection and 2) multi-class classification for sentiment behavior detection. In addition to our experimentation results, our large-scale analysis of COVID-19 pandemic has illustrated the capability of our unsupervised framework for concerns and trends discoveries using OSNs data, along with reliability in automatically and dynamically providing phrase-based interpenetration of the inferred trends and concerns. This paper provides a twelve-month comparison analysis of data discoveries and online social behavior between Canada and USA during COVID-19 pandemic.