Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the rapid advance of the social media, the challenge is to develop new techniques and standards to measure the influence of people or brands in the online social networks. Each bsite has its way of ranking the display of the most influential members of its virtual society. Hover, most of the current measurement methods are incomplete and one-dimensional.  presents a new measurement model, W-entropy, which has been developed based on information theory to study the influence of individuals based on different social networks. The model was tested using data from Facebook, Twitter, YouTube, and Google search. The proposed model can be extended to other platforms. To evaluate the effectiveness, the developed method was compared with Famecount ranking using the same data with different ight distributions. The result shows that W-entropy method is suitable for index ranking to reflect uneven information distribution across various social networks.
['Li Weigang', 'Zheng Jianya', 'Guiqiu Liu']
I am writing a research paper on a idea W-entropy method to measure the influence of the members from social networks
[{"from": "human", "value": "I am writing a research paper on a idea W-entropy method to measure the influence of the members from social networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the rapid advance of the social media, the challenge is to develop new techniques and standards to measure the influence of people or brands in the online social networks. Each bsite has its way of ranking the display of the most influential members of its virtual society. Hover, most of the current measurement methods are incomplete and one-dimensional.  presents a new measurement model, W-entropy, which has been developed based on information theory to study the influence of individuals based on different social networks. The model was tested using data from Facebook, Twitter, YouTube, and Google search. The proposed model can be extended to other platforms. To evaluate the effectiveness, the developed method was compared with Famecount ranking using the same data with different ight distributions. The result shows that W-entropy method is suitable for index ranking to reflect uneven information distribution across various social networks."}]