social media tecake

In a new study, researchers have revealed that bigger is not always better and smaller is now considered as smarter when it comes to influential superspreaders of information within social media giants like Facebook and Twitter.

According to researchers from the City College of New York (CCNY) there has been a huge shift in perception of people against “bigger is better” and this could have huge consequences for a broad range of social, natural and living networked systems.

Lead study author and CCNY physicist Flaviano Morone along with Hernan A. Makse said that major study in the field of network science has been in solving the problem of  identifying the minimal set of influential nodes in complex networks for maximizing viral marketing in social media, optimizing immunization campaigns and protecting networks under attack and researchers have only been able to develop intuitive strategies based mainly on attacking the hubs to identify crucial nodes.

For the research, study authors employed optimal percolation and state-of-the-art spin glass theory to develop an algorithm called Collective influence algorithm. Scientists believe that the algorithm is far better than the existing algorithms including methods used in social network sites like Facebook and Twitter or the famous PageRank algorithm by Google. Makse said that the set of optimal superspreaders radically differ and is much smaller than that obtained by these algorithms.

With the study, researchers showed that top influencers are highly counterintuitive: weakly connected people strategically surrounded by hierarchical coronas of hubs are the most powerful influencers.

The study will give an insight into mathematics, networks, epidemiology, marketing and scientists believe that it will also help in monitoring the spreading up of contagious diseases including Ebola.

The study appeared in the scientific journal Nature

 

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  • Nem Singh

    Can we read this as ‘Richer isn’t better’ instead ‘Poorer is smarter’ not only self but for all