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arXiv · 2104.10807

COVID-19 and Big Data: Multi-faceted Analysis for Spatio-temporal Understanding of the Pandemic with Social Media Conversations

Shayan Fazeli·Davina Zamanzadeh·Anaelia Ovalle·Thu Nguyen·Gilbert Gee·Majid Sarrafzadeh
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Abstract

COVID-19 has been devastating the world since the end of 2019 and has continued to play a significant role in major national and worldwide events, and consequently, the news. In its wake, it has left no life unaffected. Having earned the world's attention, social media platforms have served as a vehicle for the global conversation about COVID-19. In particular, many people have used these sites in order to express their feelings, experiences, and observations about the pandemic. We provide a multi-faceted analysis of critical properties exhibited by these conversations on social media regarding the novel coronavirus pandemic. We present a framework for analysis, mining, and tracking the critical content and characteristics of social media conversations around the pandemic. Focusing on Twitter and Reddit, we have gathered a large-scale dataset on COVID-19 social media conversations. Our analyses cover tracking potential reports on virus acquisition, symptoms, conversation topics, and language complexity measures through time and by region across the United States. We also present a BERT-based model for recognizing instances of hateful tweets in COVID-19 conversations, which achieves a lower error-rate than the state-of-the-art performance. Our results provide empirical validation for the effectiveness of our proposed framework and further demonstrate that social media data can be efficiently leveraged to provide public health experts with inexpensive but thorough insight over the course of an outbreak.

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Shayan Fazeli, Davina Zamanzadeh, Anaelia Ovalle, Thu Nguyen, Gilbert Gee, Majid Sarrafzadeh. 2021-04-22. COVID-19 and Big Data: Multi-faceted Analysis for Spatio-temporal Understanding of the Pandemic with Social Media Conversations. https://arxiv.org/abs/2104.10807

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