Going In-Depth: Finding Longform on the Web
Pages 2109 - 2118
Abstract
tl;dr: Longform articles are extended, in-depth pieces that often serve as feature stories in newspapers and magazines. In this work, we develop a system to automatically identify longform content across the web. Our novel classifier is highly accurate despite huge variation within longform in terms of topic, voice, and editorial taste. It is also scalable and interpretable, requiring a surprisingly small set of features based only on language and parse structures, length, and document interest. We implement our system at scale and use it to identify a corpus of several million longform documents. Using this corpus, we provide the first web-scale study with quantifiable and measurable information on longform, giving new insight into questions posed by the media on the past and current state of this famed literary medium.
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- Going In-Depth: Finding Longform on the Web
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Published In
August 2015
2378 pages
ISBN:9781450336642
DOI:10.1145/2783258
- General Chairs:
- Longbing Cao,
- Chengqi Zhang,
- Program Chairs:
- Thorsten Joachims,
- Geoff Webb,
- Dragos D. Margineantu,
- Graham Williams
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Published: 10 August 2015
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KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 10 - 13, 2015
NSW, Sydney, Australia
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KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%
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