Monitoring human behaviour during pandemic — Attacks on healthcare personnel scenario

Abstract

People exhibit all sorts of irrational behaviour during pandemics; occurrences during the Covid-19 pandemic also agree with this statement. Millions of people died during Covid-19 pandemic and people observed these deaths very closely. Therefore, it is highly probable that these deaths have a mental toll on people. One of the specific irrational incident types is - attacks on healthcare personnel (AoHP). These attacks include both verbal and physical abuse. Naturally, it is the duty of governing agencies to provide protection for these pandemic front-liners. In this work, we have explored the data related to AoHP incidents to fully understand the gravity of the situation. The current scenario is analysed through the utilization of data visualization techniques, thereby offering an insightful perspective. We have noticed that maximum cases are reported in India. In addition, civilians are mainly responsible for most occurrences. All things considered, we put forward an AoHP-type news detection mechanism, which is proposed through real experiments by applying several generic machine learning (ML) algorithms (Naive Bayes, Logistic Regression, Support Vector Machine, Extreme Gradient Boosting, Random Forest) and deep learning (DL) models (Artificial Neural Network, Long Short Term Memory, and Gated Recurrent Unit) over AoHP type news & reports for quick identification of occurrence, which would be helpful to alert the authorities. We have deployed four feature engineering techniques along with generic ML algorithms and pinpointed the most suitable one for our case. We declare Random Forest as the best fit for our purpose of classification, which achieved an F1-score of 97% in our experiments.

Keywords

• Natural Language Processing (NLP)
• Deep learning
• Text classification
• Data exploration
• Attack on healthcare personnel