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 these evolving websites and detect in a timely manner their changes. 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 the changes and predict future change rates.

Tessera, D., Calzarossa, M. C., Time series analysis of the dynamics of news websites, Paper, in PDCAT 2012 Parallel and Distributed Computing, Applications and Technologies, (Beijing, 14-16 December 2012), IEEE Press, Beijing 2012: 529-533. 10.1109/PDCAT.2012.130 [http://hdl.handle.net/10807/43207]

Time series analysis of the dynamics of news websites

Tessera, Daniele;
2012

Abstract

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 these evolving websites and detect in a timely manner their changes. 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 the changes and predict future change rates.
2012
Inglese
PDCAT 2012 Parallel and Distributed Computing, Applications and Technologies
PDCAT 2012
Beijing
Paper
14-dic-2012
16-dic-2012
978-0-7695-4879-1
not yet published on-line on ieeexplore digital library (4/2013)
Tessera, D., Calzarossa, M. C., Time series analysis of the dynamics of news websites, Paper, in PDCAT 2012 Parallel and Distributed Computing, Applications and Technologies, (Beijing, 14-16 December 2012), IEEE Press, Beijing 2012: 529-533. 10.1109/PDCAT.2012.130 [http://hdl.handle.net/10807/43207]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/43207
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