Understanding Global Reaction to the Recent Outbreaks of COVID-19: Insights from Instagram Data Analysis

TL;DR

This study analyzes Instagram posts during the COVID-19 pandemic to understand public reactions, using image classification and caption analysis to reveal social media norms and sentiments in crisis moments.

Contribution

It introduces a methodology combining CNNs and LSTMs for automated sentiment analysis of Instagram data during COVID-19, revealing social norms and reactions.

Findings

Identified common social media reactions to COVID-19

Demonstrated effectiveness of CNN and LSTM in analyzing Instagram content

Revealed emerging norms during global crises

Abstract

The coronavirus disease, also known as the COVID-19, is an ongoing pandemic of a severe acute respiratory syndrome. The pandemic has led to the cancellation of many religious, political, and cultural events around the world. A huge number of people have been stuck within their homes because of unprecedented lockdown measures taken globally. This paper examines the reaction of individuals to the virus outbreak-through the analytical lens of specific hashtags on the Instagram platform. The Instagram posts are analyzed in an attempt to surface commonalities in the way that individuals use visual social media when reacting to this crisis. After collecting the data, the posts containing the location data are selected. A portion of these data are chosen randomly and are categorized into five different categories. We perform several manual analyses to get insights into our collected dataset. Afterward, we use the ResNet-50 convolutional neural network for classifying the images associated with the posts, and attention-based LSTM networks for performing the caption classification. This paper discovers a range of emerging norms on social media in global crisis moments. The obtained results indicate that our proposed methodology can be used to automate the sentiment analysis of mass people using Instagram data.