Social Networks Analysis to Retrieve Critical Comments on Online Platforms
This work addresses public health challenges by targeting online users to improve behavior, but it appears incremental as it applies existing text analysis and clustering methods to pandemic data.
The paper tackles the problem of analyzing social media behavior during a pandemic to identify high-risk users and promote healthy lifestyles, resulting in a model that clusters users based on habits to reduce pandemic effects.
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.