Keyword advertising on general web search engines is a multi-billion dollar business. Keyword advertising turns contentious, however, when businesses target ads against their competitors' brand names---a practice known as "competitive poaching." To stave off poaching, companies defensively bid on ads for their own brand names. Google, in particular, has faced lawsuits and regulatory scrutiny since it altered its policies in 2004 to allow poaching. In this study, we investigate the sources of advertising revenue earned by Google, Bing, and DuckDuckGo by examining ad impressions, clicks, and revenue on navigational and brand searches. Using logs of searches performed by a representative panel of US residents, we estimate that ads on these searches account for 28--36% of Google's search revenue, while Bing earns even more. We also find that the effectiveness of these ads for advertisers varies. We conclude by discussing the implications of our findings for advertisers and regulators.

Is Google Search a monopoly with gatekeeping power? Regulators from the US, UK, and Europe have argued that it is based on the assumption that Google Search dominates the market for horizontal (a.k.a. “general”) web search. Google disputes this, claiming that competition extends to all vertical (a.k.a. “specialized”) search engines, and that under this market definition it does not have monopoly power. In this study we present the first analysis of Google Search’s market share under vertical segmentation of online search. We leverage observational trace data collected from a panel of US residents that includes their web browsing history and copies of the Google Search Engine Result Pages they were shown.We observe that participants’ search sessions begin at Google greater than 50% of the time in 24 out of 30 vertical market segments (which comprise almost all of our participants’ searches). Our results inform the consequential and ongoing debates about the market power of Google Search and the conceptualization of online markets in general.

The results returned by image search engines have the power to shape peoples' perceptions about social groups. Existing work on image search engines leverages hand-selected queries for occupations like "doctor" and "engineer" to quantify racial and gender bias in search results. We complement this work by analyzing peoples' real-world image search queries and measuring the distributions of perceived gender, skin tone, and age in their results. We collect 54,070 unique image search queries and analyze 1,481 open-ended people queries (i.e. not queries for named entities) from a representative sample of 643 US residents. For each query, we analyze the top 15 results returned on both Google and Bing Images.

The contemporary Google Search Engine Results Page (SERP) supplements classic blue hyperlinks with complex components. These components produce tensions between searchers, 3rd-party websites, and Google itself over clicks and attention. In this study, we examine 12 SERP components from two categories: (1) extracted results (e.g., featured-snippets) and (2) Google Services (e.g., shopping-ads) to determine their effect on peoples’ behavior. We measure behavior with two variables: (1) click- through rate (CTR) to Google’s own domains versus 3rd-party domains and (2) time spent on the SERP. We apply causal inference methods to an ecologically valid trace dataset comprising 477,485 SERPs from 1,756 participants. We find that multiple components substantially increase CTR to Google domains, while others decrease CTR and increase time on the SERP. These findings may inform efforts to regulate the design of powerful intermediary platforms like Google.

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.

Search engines are a primary means through which people obtain information in today»s connected world. Yet, apart from the search engine companies themselves, little is known about how their algorithms filter, rank, and present the web to users. This question is especially pertinent with respect to political queries, given growing concerns about filter bubbles, and the recent finding that bias or favoritism in search rankings can influence voting behavior. In this study, we conduct a targeted algorithm audit of Google Search using a dynamic set of political queries. We designed a Chrome extension to survey participants and collect the Search Engine Results Pages (SERPs) and autocomplete suggestions that they would have been exposed to while searching our set of political queries during the month after Donald Trump»s Presidential inauguration. Using this data, we found significant differences in the composition and personalization of politically-related SERPs by query type, subjects» characteristics, and date.