PP234 Analysis Of Discussions On Twitter On The Topic Of COVID-19 Tests: Exploring A Natural Language Processing Approach

Abstract

Introduction

Various strategies to suppress the Coronavirus have been adopted by governments across the world; one such strategy is diagnostic testing. The anxiety of testing on individuals is difficult to quantify. This analysis explores the use of soft intelligence from Twitter (USA, UK & India) in helping better understand this issue.

Methods

A total of 650,000 tweets were collected between September and October 2020, using Twitter API using hashtags such as ‘#oxymeter’, ‘#oximeter’, ‘#antibodytest’, ‘#infraredthermometer’, ‘#swabtest’, ‘#rapidtest’, and ‘#antigen’. We applied natural language processing (TextBlob) to assign sentiment and categorize the tweets by emotions and attitude. WordCloud was then used to identify the single topmost 500 words in the whole tweet dataset.

Results

Global analysis and pre-processing of the tweets indicate that 21 percent, seven percent and four percent of tweets originated from the USA, UK, and India respectively. The tweets from #antibody, #rapid, #antigen, and #swabtest were positive sentiments, whereas #oxymeter, #infraredthermometer were mostly neutral. The underlying emotions of the tweets were approximately 2.5 times more positive than negative. The most used words in the tweets included ‘hope’ ‘insurance’, ‘symptoms’, ‘love’, ‘painful’, ‘cough’, ‘fast test’, ‘wife’, and ‘kids’.

Conclusions

The finding suggests that it may be reasonable to infer that people are generally concerned about their personal and social wellbeing, wanting to keep themselves safe and perceive testing to deliver some component of that feeling of safety. There are several limitations to this study such as it was restricted to only three countries, and includes only English language tweets with a limited number of hashtags.