Learning to classify inappropriate query-completions

Abstract

Query auto-completion is a powerful feature anywhere users are querying and is nowadays omnipresent in many forms and entry points, e.g. search engines, social networks, web browsers, operating systems. Suggestions not only speed up the process of entering a query but also shape how users query and can make the difference between a successful search and a frustrated user. The main source of these query completions is past, aggregated, user queries. A non-negligible fraction of these queries contain offensive, adult, illegal or otherwise inappropriate content. Surfacing these completions can have legal implications, offend users and give the incorrect impression companies providing the query completion service condone these views. In this paper, we describe existing methods to identify inappropriate queries and present a novel machine learned approach that does not require expensive, humancurated, blocklists and is superior to these in recall and competitive in F1-score.