Analytics Challenge Webinar Series - Using Social Media to Analyze Public Concerns and Policy Responses to COVID-19 in Hong Kong

This is part 3 of a 5-part webinar series featuring leading academic experts in big data analytics, information systems, machine learning, data mining, and large-scale data processing. Students interested in the CBSA-Wisers Analytics Challenge @HKUST 2021/22 are highly encouraged to attend the webinars. 

ABSTRACT

The outbreak of COVID-19 has caused huge economic and societal disruptions. To fight against the coronavirus, it is critical for policymakers to take swift and effective actions. In this article, we take Hong Kong as a case study, aiming to leverage social media data to support policy-making activities in different phases. First, in the agenda-setting phase, we identify key issues to be addressed during COVID-19. In particular, we design a novel epidemic awareness index to continuously monitor the public discussion hotness of COVID-19 based on large-scale data collected from social media platforms. Then we identify the key issues by analyzing the posts and comments of the extensively discussed topics. Second, in the policy evaluation phase, we conduct a real-time evaluation of anti-epidemic policies. Specifically, we develop an accurate Cantonese sentiment classification model to measure the public satisfaction with anti-epidemic policies and propose a keyphrase extraction technique to further extract public opinions. To the best of our knowledge, this is the first work that conducts a large-scale social media analysis of COVID-19 in Hong Kong.

KEYWORDS

COVID-19, Social Media, Sentiment Analysis, Public Opinion, Natural Language Processing