Authors:
Elena Hensinger
;
Ilias Flaounas
and
Nello Cristianini
Affiliation:
University of Bristol, United Kingdom
Keyword(s):
Pattern analysis, Ranking SVM, News appeal, Text analysis, User preference modelling.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Classification
;
Learning of Action Patterns
;
Pattern Recognition
;
Ranking
;
Software Engineering
;
Theory and Methods
;
Web Applications
Abstract:
We model readers’ preferences for online news, and use these models to compare different news outlets with each other. The models are based on linear scoring functions, and are inferred by exploiting aggregate behavioural information about readers’ click choices for textual content of six given news outlets over one year of time. We generate one model per outlet, and while not extremely accurate – due to limited information – these models are shown to predict the click choices of readers, as well as to being stable over time. We use those six audience preference models in several ways: to compare how the audiences’ preferences of different outlets relate to each other; to score different news topics with respect to user appeal; to rank a large number of other news outlets with respect to their content appeal to all audiences; and to explain this measure by relating it to other metrics. We discover that UK tabloids and the website of the “People” magazine contain more appealing conten
t for all audiences than broadsheet newspapers, news aggregators and newswires, and that this measure of readers’ preferences correlates with a measure of linguistic subjectivity at the level of outlets.
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