The predictive power of online chatter

TLDR

Online discourse increasingly migrates to blogs, bulletin boards, wikis, and other collaborative technologies, enabling the tracking of topic popularity. The study analyzes about half a million sales‑rank values for 2,340 books over four months, correlating them with blog, media, and web‑page postings to assess predictive relationships. The authors find that hand‑crafted queries yield postings whose volume predicts sales ranks, that many queries can be automatically generated, and that algorithmic predictors can use online postings to forecast sales‑rank spikes.

Abstract

An increasing fraction of the global discourse is migrating online in the form of blogs, bulletin boards, web pages, wikis, editorials, and a dizzying array of new collaborative technologies. The migration has now proceeded to the point that topics reflecting certain individual products are sufficiently popular to allow targeted online tracking of the ebb and flow of chatter around these topics. Based on an analysis of around half a million sales rank values for 2,340 books over a period of four months, and correlating postings in blogs, media, and web pages, we are able to draw several interesting conclusions.First, carefully hand-crafted queries produce matching postings whose volume predicts sales ranks. Second, these queries can be automatically generated in many cases. And third, even though sales rank motion might be difficult to predict in general, algorithmic predictors can use online postings to successfully predict spikes in sales rank.