Reducing Misinformation in Query Autocompletions
This addresses misinformation issues affecting search engine users and organizations, though it is incremental as it modifies an existing component.
The paper tackles the problem of misinformation in search engine query autocompletions by proposing an alternative method using anchor texts from web crawls instead of query logs, showing that anchor text autocompletions outperform query log autocompletions for queries of 2 words or more.
Query autocompletions help users of search engines to speed up their searches by recommending completions of partially typed queries in a drop down box. These recommended query autocompletions are usually based on large logs of queries that were previously entered by the search engine's users. Therefore, misinformation entered -- either accidentally or purposely to manipulate the search engine -- might end up in the search engine's recommendations, potentially harming organizations, individuals, and groups of people. This paper proposes an alternative approach for generating query autocompletions by extracting anchor texts from a large web crawl, without the need to use query logs. Our evaluation shows that even though query log autocompletions perform better for shorter queries, anchor text autocompletions outperform query log autocompletions for queries of 2 words or more.