An Approach to Model and Predict the Popularity of Online Contents with Explanatory Factors

In this paper, we propose a methodology to predict the popularity of online contents. More precisely, rather than trying to infer the popularity of a content itself, we infer the likelihood that a content will be popular. Our approach is rooted in survival analysis where predicting the precise lifetime of an individual is very hard and almost impossible but predicting the likelihood of one's survival longer than a threshold or another individual is possible. We position ourselves in the standpoint of an external observer who has to infer the popularity of a content only using publicly observable metrics, such as the lifetime of a thread, the number of comments, and the number of views. Our goal is to infer these observable metrics, using a set of explanatory factors, such as the number of comments and the number of links in the first hours after the content publication, which are observable by the external observer. We use a Cox proportional hazard regression model that divides th...

• Type: Journal
• Year: 2010
• Where: WEBI
• Authors: Jong Gun Lee, Sue Moon, Kavé Salamatian
• Keywords: External Observer, Infer, Internet Technology, Observable Metrics, WEBI 2010