WI 08
Predicting News Story Importance using Language Features
Abstract
In this age of awareness, people have access to information like never before.
Hundreds of newspapers and millions of bloggers present news and their
interpretations in an openly accessible manner. With globalization, distant
events can have impact on people thousands of miles away. While expert humans
can recognize a potentially important piece of news, this is still a difficult
problem for an automatic system. Since people are increasingly relying on
multiple online sources of information, it is important to support users in
filtering news automatically. In this work, we consider the problem of
anticipating news story importance, i.e. given a news item, predicting if it
will be of interest for a majority of users. Such ranking is currently done
manually for newspapers, and we explore automatic approaches and indicative
features for the same. Our main conclusion is that importance prediction is a
hard problem, and pure textual features are not sufficient for classifiers with
90\% accuracy.
Full Paper
WI08.pdf
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WI-IAT 2008
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