Centrality of any node in social network analysis

In the contemporary landscape of advanced mobile technology, social media platforms have emerged as prominent arenas for expression, fostering vast volumes of communication ripe for research and analysis. Social network analysis (SNA) stands as a pivotal tool in identifying influential individuals within microblogging platforms, notably Twitter, which serves as a catalyst for online dialogues, trends, and content virality. The applications of discerning influential users on Twitter are multifaceted, ranging from viral marketing and brand analysis to news dissemination, health awareness campaigns, political activism, and governance empowerment. In this study, we introduce a sustainable method, termed Weighted Correlated Influence (WCI), designed to capture the relative impact of both activity on a timeline and characteristics of online users that are trend-specific. Weighted Correlated Influence in social network analysis evaluates the significance of nodes by combining their connectivity and influence, considering both the weight and correlation of their relationships. This metric helps identify key influencers and the overall structure of the network, highlighting nodes with high influence and strong, correlated connections. Our methodology integrates profile activity with underlying network topology to assign influence scores to users, synthesizing their combined effect. To evaluate the efficacy of our proposed methodology, we focus on the Twitter trend #Coronavirus Pandemic and validate the results against another social media trend. Experimental findings demonstrate the superior performance of WCI over existing methods.