Real-Time Detection of COVID-19 Events From Twitter: A Spatial-Temporally Bursty-Aware Method

ABSTRACT

In the last two years, the outbreak of COVID-19 has significantly affected human life, society, and the economy worldwide. To prevent people from contracting COVID-19 and mitigate its spread, it is crucial to timely distribute complete, accurate, and up-to-date information about the pandemic to the public. In this article, we propose a spatial-temporally bursty-aware method called STBA for real-time detection of COVID-19 events from Twitter. STBA has three consecutive stages. In the first stage, STBA identifies a set of keywords that represent COVID-19 events according to the spatiotemporally bursty characteristics of words using Ripley's K function. STBA will also filter out tweets that do not contain the keywords to reduce the interference of noise tweets on event detection. In the second stage, STBA uses online density-based spatial clustering of applications with noise clustering to aggregate tweets that describe the same event as much as possible, which provides more information for event identification. In the third stage, STBA further utilizes the temporal bursty characteristic of event location information in the clusters to identify real-world COVID-19 events. Each stage of STBA can be regarded as a noise filter. It gradually filters out COVID-19-related events from noisy tweet streams. To evaluate the performance of STBA, we collected over 116 million Twitter posts from 36 consecutive days (from March 22, 2020 to April 26, 2020) and labeled 501 real events in this dataset. We compared STBA with three state-of-the-art methods, EvenTweet, event detection via microblog cliques (EDMC), and GeoBurst+ in the evaluation. The experimental results suggest that STBA outperforms GeoBurst+ by 13.8%, 12.7%, and 13.3% in terms of precision, recall, and F ₁score. STBA achieved even more improvements compared with EvenTweet and EDMC.