Detecting Early Warning Indicators of Covid-19 Pandemic in the Context of United States: an Exploratory Data Analysis.

This work aims to investigate if social media data, Twitter in particular can be used to detect early warning indicators of COVID-19 pandemic in the United States (US). To demonstrate the viability of this work, English tweets were collected with a hasghtag of COVID-19 related topics ranges from 12th March to end of April 2020. With the help of with N-gram language model and Term Frequency and Inverse Document Frequency (TF-IDF) significant bi-grams such as (“new york”), (“social, distancing”), (“stay, safe”), (“toilet, paper”), (“wash, hand”), (“tested, positive”), (look, like), (“front, line”), (“grocery, store”) etc. are extracted. Our analysis shows that, the natures of the bi-grams directly reflect the characteristics of the infection cases and are almost similarly distributed over different clusters. This study also reveals that, the tweets of (“new york”) increases with (“stay, home”), (“social, distancing”), (“stay, safe”), (“look, like”) and (“tested positive”); and decreases with (“toilet, paper”). Bi-grams with such relationships are recognized as indicators and are validated with the number of infection cases on each day. Results show that, social media data can project the actual scenario of infection curve and able to detect warning indicators once the pandemic is moderately recognized.