  The ranking incentives of many authors of Web pages play an important role in
the Web dynamics. That is, authors who opt to have their pages highly ranked
for queries of interest, often respond to rankings for these queries by
manipulating their pages; the goal is to improve the pages' future rankings.
Various theoretical aspects of this dynamics have recently been studied using
game theory. However, empirical analysis of the dynamics is highly constrained
due to lack of publicly available datasets.We present an initial such dataset
that is based on TREC's ClueWeb09 dataset. Specifically, we used the WayBack
Machine of the Internet Archive to build a document collection that contains
past snapshots of ClueWeb documents which are highly ranked by some initial
search performed for ClueWeb queries. Temporal analysis of document changes in
this dataset reveals that findings recently presented for small-scale
controlled ranking competitions between documents' authors also hold for Web
data. Specifically, documents' authors tend to mimic the content of documents
that were highly ranked in the past, and this practice can result in improved
ranking.
