  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.
