Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19
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Abstract
In the era of advanced mobile technology, freedom of expression over social media has become prevalent
among online users. This generates a huge amount of communication that eventually forms a ground
for extensive research and analysis. The social network analysis allows identifying the influential people
in society over microblogging platforms. Twitter, being an evolving social media platform, has become
increasingly vital for online dialogues, trends, and content virality. Applications of discovering influential
users over Twitter are manifold. It includes viral marketing, brand analysis, news dissemination, health
awareness spreading, propagating political movement, and opinion leaders for empowering governance.
In our research, we have proposed a sustainable approach, namely Weighted Correlated Influence (WCI),
which incorporates the relative impact of timeline-based and trend-specific features of online users. Our
methodology considers merging the profile activity and underlying network topology to designate online
users with an influence score, which represents the combined effect. To quantify the performance of our
proposed method, the Twitter trend #CoronavirusPandemic is used. Also, the results are validated for
another social media trend. The experimental outcomes depict enhanced performance of proposed WCI
over existing methods that are based on precision, recall, and F1-measure for validation.
Palabras clave
Social Network; Influence measure; Weighted Correlated Influence; Sustainable Computing; Twitter; Covid-19Link to resource
https://doi.org/10.1016/j.chaos.2020.110037Collections
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