Time Series Analysis of the Dynamics of News Websites

The content of news websites changes frequently and rapidly and its relevance tends to decay with time. To be of any value to the users, tools, such as, search engines, have to cope with the dynamics of websites and detect changes in a timely manner. In this paper we apply time series analysis to study the properties and the temporal patterns of the change rates of the content of three news websites. Our investigation shows that changes are characterized by large fluctuations with periodic patterns and time dependent behavior. The time series describing the change rate is decomposed into trend, seasonal and irregular components and models of each component are then identified. The trend and seasonal components describe the daily and weekly patterns of the change rates. Trigonometric polynomials best fit these deterministic components, whereas the class of ARMA models represents the irregular component. The resulting models can be used to describe the dynamics of websites and predict future change rates.