Predicting User Views in Online News

Daniel Hardt, Owen Rambow


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.
Anthology ID:
W17-4202
Volume:
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7–12
Language:
URL:
https://www.aclweb.org/anthology/W17-4202
DOI:
10.18653/v1/W17-4202
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/W17-4202.pdf