A New Social Media Analytics Method for Identifying Factors Contributing to COVID-19 Discussion Topics

Abstract

Since the onset of the COVID-19 crisis, scholarly investigations and policy formulation have harnessed the potent capabilities of artificial intelligence (AI)-driven social media analytics. Evidence-driven policymaking has been facilitated through the proficient application of AI and natural language processing (NLP) methodologies to analyse the vast landscape of social media discussions. However, recent research works have failed to demonstrate a methodology to discern the underlying factors influencing COVID-19-related discussion topics. In this scholarly endeavour, an innovative AI- and NLP-based framework is deployed, incorporating translation, sentiment analysis, topic analysis, logistic regression, and clustering techniques to meticulously identify and elucidate the factors that are relevant to any discussion topics within the social media corpus. This pioneering methodology is rigorously tested and evaluated using a dataset comprising 152,070 COVID-19-related tweets, collected between 15th July 2021 and 20th April 2023, encompassing discourse in 58 distinct languages. The AI-driven regression analysis revealed 37 distinct observations, with 20 of them demonstrating a higher level of significance. In parallel, clustering analysis identified 15 observations, including nine of substantial relevance. These 52 AI-facilitated observations collectively unveil and delineate the factors that are intricately linked to five core discussion topics that are prevalent in the realm of COVID-19 discourse on Twitter. To the best of our knowledge, this research constitutes the inaugural effort in autonomously identifying factors associated with COVID-19 discussion topics, marking a pioneering application of AI algorithms in this domain. The implementation of this method holds the potential to significantly enhance the practice of evidence-based policymaking pertaining to matters concerning COVID-19.

A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

“…Owing to the dearth of officially curated disaster data repositories, recent scholarly endeavors have turned to harnessing social media posts to identify and evaluate natural calamities such as landslides, floods, earthquakes, typhoons, wildfires, and others [6][7][8]. Moreover, the dissemination, evaluation, and repercussions of the COVID-19 pandemic have been elucidated through the innovative application of ML and AI algorithms to live social media content, exemplified by platforms such as Twitter [9,10]. As shown in [9,10], through AI-driven methods researchers could identify and categorize the vast array of discussions surrounding the pandemic, enabling policymakers to tailor responses more effectively to specific contexts and needs.…”

“…Moreover, the dissemination, evaluation, and repercussions of the COVID-19 pandemic have been elucidated through the innovative application of ML and AI algorithms to live social media content, exemplified by platforms such as Twitter [9,10]. As shown in [9,10], through AI-driven methods researchers could identify and categorize the vast array of discussions surrounding the pandemic, enabling policymakers to tailor responses more effectively to specific contexts and needs. These analytics facilitate a comprehensive understanding of global perspectives and multilingual expressions related to COVID-19, ensuring that disaster response strategies are informed by a diverse range of voices and concerns, ultimately leading to more effective and responsive public health communication and intervention strategies.…”