Social Networks Analysis to Retrieve Critical Comments on Online Platforms

TL;DR

This paper presents a novel social network analysis model using text analysis and clustering to identify user behaviors during a pandemic, aiming to promote healthy lifestyles and reduce virus spread through targeted online interventions.

Contribution

It introduces a new approach combining text analysis and clustering of social media user behaviors to enhance public health strategies during pandemics.

Findings

Clustering of user habits reveals patterns of virus spread.

Promoting healthy online behaviors can significantly impact public health.

A machine learning model effectively recommends health-promoting actions.

Abstract

Social networks are rich source of data to analyze user habits in all aspects of life. User's behavior is decisive component of a health system in various countries. Promoting good behavior can improve the public health significantly. In this work, we develop a new model for social network analysis by using text analysis approach. We define each user reaction to global pandemic with analyzing his online behavior. Clustering a group of online users with similar habits, help to find how virus spread in different societies. Promoting the healthy life style in the high risk online users of social media have significant effect on public health and reducing the effect of global pandemic. In this work, we introduce a new approach to clustering habits based on user activities on social media in the time of pandemic and recommend a machine learning model to promote health in the online platforms.