Concern Levels During COVID-19: An AI-Based Approach for Social Media Analysis

Unknown nature of COronaVIrus Disease (COVID-19) has had a huge impact on community, raising worries. Being confined to homes as a safeguard against it, the community voiced its concerns in large numbers via microblogging. The increased tweeting can reflect the severity of the concerns about pandemic. As a result, it is critical to keep an eye on the amount of worry in order to establish a resilient community. Understanding the importance, we propose to analyse the concern and awareness levels during pandemic. We begin with fine-tuning RoBERTa to identify COVID-19-related tweets. Then, the model categorizes tweets published from January 2021 to January 2022. This time period corresponds to the fading stage of the first wave to the third wave of the pandemic. From the classification results, we generate the time series of a social media metric, Public Concern Index (PCI). Then, we subject the obtained time series to change point detection. Our classification model achieves an F1 score of 97.5%. Further, time series analysis suggests an increase in concern levels during the pandemic. During the fading stage of the wave, the levels decline, reflecting that people’s levels of concern are decreasing. Furthermore, we observe a similar acceleration in PCI levels during the second and third waves.