Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: What types of information are popularly shared on social media sites such as Facebook, Twitter, Linked In and Google+? Does each of the social network cares about different topics? Can  uncover the different patterns behind the sharing behavior of social network users?  addresses the above questions by analyzing public information spreading data on different online social networks, identifying the spreading characteristics, and modeling the spreading patterns. In order to conduct statistical studies and build models to achieve our goals,  first extract data from the "share" buttons in news articles published by mainstream news bsites. Such buttons are important to initiate the propagation of the news in social media. Through statistical findings,  demonstrate that both the share counts and topics of news vary a lot in different social networks. Additionally, based on the time series data displaying how news articles accumulate their share counts,  propose a K-medoids based clustering scheme, Clustering based on Similar Volume and Shape (CSVS), on a newly designed Scaling Aware Shifting Invariant (SASI) distance measure to uncover the different types of sharing patterns of news articles in social media. Through experiments on the collected dataset,  demonstrate that the proposed CSVS is able to cluster news with similar sharing patterns into the same cluster, by taking into consideration both of their share counts and shapes.
['Qingbo Hu', 'Guan Wang', 'Philip S. Yu']
I am writing a research paper on a idea Public Information Sharing Behaviors Analysis over Different Social Media
[{"from": "human", "value": "I am writing a research paper on a idea Public Information Sharing Behaviors Analysis over Different Social Media"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: What types of information are popularly shared on social media sites such as Facebook, Twitter, Linked In and Google+? Does each of the social network cares about different topics? Can  uncover the different patterns behind the sharing behavior of social network users?  addresses the above questions by analyzing public information spreading data on different online social networks, identifying the spreading characteristics, and modeling the spreading patterns. In order to conduct statistical studies and build models to achieve our goals,  first extract data from the \"share\" buttons in news articles published by mainstream news bsites. Such buttons are important to initiate the propagation of the news in social media. Through statistical findings,  demonstrate that both the share counts and topics of news vary a lot in different social networks. Additionally, based on the time series data displaying how news articles accumulate their share counts,  propose a K-medoids based clustering scheme, Clustering based on Similar Volume and Shape (CSVS), on a newly designed Scaling Aware Shifting Invariant (SASI) distance measure to uncover the different types of sharing patterns of news articles in social media. Through experiments on the collected dataset,  demonstrate that the proposed CSVS is able to cluster news with similar sharing patterns into the same cluster, by taking into consideration both of their share counts and shapes."}]