A Study on the Relevance of Information in Discriminative and Non-Discriminative Media

Abstract

In this paper we compare the relevance of information obtained from "discriminative" media and from "non-discriminative" media. Discriminative media are the ones which accumulate and deliver information using a heuristic selection of it. This can be made by humans, or by artificial intelligent systems, exhibiting some form of "knowledge". Non-discriminative media just collect and return information without any distinction. This can also be made by humans or by artificial systems, but there is no "knowledge" involved in the process. We ranked the words occurring in an edited electronic publication specialized in complex systems research, and we found that they approximate a modified Zipf distribution. We compared occurrences of representative words from the distribution with the occurrences in non-discriminative media. We found that a non-discriminative medium (Google) has a higher variance from of our original distribution than a semi-discriminative one (NEC Research Index), even when both appear to have their own modified Zipf distribution. We conclude that discriminative media have a higher efficiency rating, at least in the area in which they specialize, than non-discriminative media. Using the same search method, the discriminative media should deliver more relevant information. This relevancy also depends on the skills of the user, but non-discriminative media are more sensitive to poor searching skills, as there is a higher probability of delivering irrelevant information. This leads us to suggest the incorporation of intelligent classifications in different media (such as the ones suggested by the Semantic Web project), in order to increase the relevance of the delivered information.