As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
This paper studies the problem of identifying influencers on specific topics in the microblog sphere. Prior works usually use the cumulative number of social links to measure users' topic-level influence, which ignores the dynamics of influence. As a result, they usually find faded influencers. To address the limitations of prior methods, we propose a novel probabilistic generative model to capture the variation of influence over time. Then a influence decay method is proposed to measure users' current topic-level influence.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.