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In this paper we propose a classification for different observable trends over time for user web queries. The focus is on the identification of general collective trends, based on search query keywords, of the user community in Internet and how they behave over a given time period. We give some representative examples of real search queries and their tendencies. From these examples we define a set of descriptive features which can be used as inputs for data modelling. Then we use a selection of non supervised (clustering) and supervised modelling techniques to classify the trends. The results show that it is relatively easy to classify the basic hypothetical trends we have defined, and we identify which of the chosen learning techniques are best able to model the data. However, the presence of more complex, noisy or mixed trends make the classification more difficult.

COLLECTIVE BEHAVIOUR IN INTERNET - Tendency Analysis of the Frequency of User Web Queries