Acknowledgement

This research that was undertaken was a topic proposed by Wei Liu

Introduction

The usage of social media during global crises and natural disasters surges drastically; evidently seen through the early onset of the COVID-19 pandemic. Social media has become a crucial communication tool that has been used for information generation, consumption and expeditious information transmission. Amidst the height of the global COVID-19 pandemic in 2020, user’s on ‘twitter’ took their ideals and paranoia to publish their own ‘tweets’ on the platform that often clashed against other well established authoritative health figures in an attempt to combat the high influx of engagement created. In doing so, The World Health Organisation (WHO) has classified social media platforms to cause the first ever recorded phenomenon known as an ‘infodemic’ which is the occurrence of an overabundance of both accurate and inaccurate information being readily available. Individuals with access to ‘high quality’ information that are backed by internationally renowned institutions approved by governments are conflicted with spreading knowledge as it will further the infodemic. Thus in turn making it harder for user’s who primarily rely on the strenuous use of social media platforms to acquire reliable information in a sea of misinformation being a catalyst in furthering the global pandemic.

This project aims to convey through visualisation the data set that was mined throughout the Twitter sever to show the rise and fall of particular trends. Final dataset that was created alongside the pipeline used to source the data is available on the github repoistory.