Detecting controversy on the web

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Detecting controversy on the web is a 2013 conference paper written in English by Dori-Hacohen S., Allan J. and published in International Conference on Information and Knowledge Management, Proceedings.

[edit] Abstract

A useful feature to facilitate critical literacy would alert users when they are reading a controversial web page. This requires solving a binary classification problem: does a given web page discuss a controversial topic? We explore the feasibility of solving the problem by treating it as supervised k-nearest-neighbor classification. Our approach (1) maps a webpage to a set of neighboring Wikipedia articles which were labeled on a controversiality metric; (2) coalesces those labels into an estimate of the webpage's controversiality; and finally (3) converts the estimate to a binary value using a threshold. We demonstrate the applicability of our approach by validating it on a set of webpages drawn from seed queries. We show absolute gains of 22% in F 0.5 on our test set over a sentiment-based approach, highlighting that detecting controversy is more complex than simply detecting opinions. Copyright is held by the owner/author(s).

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