Auditing the Partisanship of Google Search Snippets

Christo Wilson, Desheng Hu, Ronald E. Robertson, Shan Jiang

Abstract:

The text snippets presented in web search results provide users
with a slice of page content that they can quickly scan to help inform their click decisions. However, little is known about how these
snippets are generated or how they relate to a user’s search query.
Motivated by the growing body of evidence suggesting that search
engine rankings can influence undecided voters, we conducted an
algorithm audit of the political partisanship of Google Search snippets relative to the webpages they are extracted from. To accomplish
this, we constructed lexicon of partisan cues to measure partisanship and construct a set of left- and right-leaning search queries.
Then, we collected a large dataset of Search Engine Results Pages
(SERPs) by running our partisan queries and their autocomplete
suggestions on Google Search. After using our lexicon to score the
machine-coded partisanship of snippets and webpages, we found
that Google Search’s snippets generally amplify partisanship, and
that this effect is robust across different types of webpages, query
topics, and partisan (left- and right-leaning) queries.