Going In-Depth: Finding Longform on the Web

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.