Driving the Herd: Search Engines as Content Influencers

TL;DR

This paper investigates how search engines influence content creation by publishers through ranking manipulation, demonstrating that targeted content effects can be achieved via herding and biasing techniques, impacting the overall corpus.

Contribution

It introduces methods for search engines to steer content in the corpus using herding and relevance biasing, highlighting their potential to shape online information.

Findings

Content effects like topical focus and document properties can be driven by search engine techniques.

Publishers respond to ranking incentives, confirming the influence of search engine algorithms.

Techniques can reliably induce targeted content modifications in practice.

Abstract

In competitive search settings such as the Web, many documents' authors (publishers) opt to have their documents highly ranked for some queries. To this end, they modify the documents - specifically, their content - in response to induced rankings. Thus, the search engine affects the content in the corpus via its ranking decisions. We present a first study of the ability of search engines to drive pre-defined, targeted, content effects in the corpus using simple techniques. The first is based on the herding phenomenon - a celebrated result from the economics literature - and the second is based on biasing the relevance ranking function. The types of content effects we study are either topical or touch on specific document properties - length and inclusion of query terms. Analysis of ranking competitions we organized between incentivized publishers shows that the types of content effects we target can indeed be attained by applying our suggested techniques. These findings have important implications with regard to the role of search engines in shaping the corpus.