The Identification of the Top Positive Influential Users of the Social Networks to Help in the Control of Covid-19 Spread

Abstract

Covid-19 pandemic is considered the most worldwide problem, and causes horrible crises for all human being. Social networks can play a vital role in the prevention of the spread of the Covid-19 pandemic. The top influential users of social networks like Twitter can have positive or negative effect in the broadcast of useful and same time harmful information about how to deal with the virus, and encourage people to follow up the rules announced by World Health Organization (WHO). So the detection of the top positive and negative influential users can help in the control of the spread of the virus. The proposed approach is based on applying influence maximization solutions to identify the top influential users from Twitter social network graph, and to determine if the influence is positive or not.The proposed approach has four main phases, the first phase is collecting Covid-19 pandemic related tweets dataset and extract the related users and their followers. The second phase is creating a social network graph from the collected dataset. The third phase is using LKG influence maximization approach to identify the most effective users from the social network graph. The last phase is based on using hashtags frequency analysis to be able to identify the type of influence of each top influential user.

Weighted Entity-Linking and Integration Algorithm for Medical Knowledge Graph Generation

“…This integrated knowledge graph aims to be used as a knowledge base for any intelligent healthcare system that could predict diseases or give alerts whenever any disease ailments are detected [11,12]. The system could be used by social networks users and medical professionals, taking into consideration user experiences [15]. Entity linking or entity normalization [16] is the task of using the ontology concepts as a dictionary containing a set of entities E and given a text containing a set of entity mentions M, each mention m ϵ M is mapped to its corresponding entity e where e ϵ E.…”