Predicting User Views in Online News

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Predicting User Views in Online News

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Title: Predicting User Views in Online News
Author: Hardt, Daniel; Rambow, Owen
Abstract: We analyze user viewing behavior on an online news site. We collect data from 64,000 news articles, and use text features to predict frequency of user views. We compare predictiveness of the headline and “teaser” (viewed before clicking) and the body (viewed after clicking). Both are predictive of clicking behavior, with the full article text being most predictive.
URI: http://hdl.handle.net/10398/9580
Date: 2017-12-18
Notes: Paper first published in Proceedings of the 2017 EMNLP Workshop on Natural Language Processing meets Journalism, pages 7–12 Copenhagen, Denmark, September 7, 2017

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