Abstract: While Web search has become increasingly effective over thelast decade, for many users' needs the required answers maybe spread across many documents, or may not exist on theWeb at all. Yet, many of these needs could be addressedby asking people via popular Community Question Answering (CQA) services, such as Baidu Knows, Quora, or YahooAnswers. In this paper, we perform the first large-scale analysis of how searchers become askers. For this, we study thelogs of a major web search engine to trace the transformationof a large number of failed searches into questions posted ona popular CQA site. Specifically, we analyze the characteristics of the queries, and the patterns of searcher behaviorthat precede posting a question; the relationship betweenthe attempted queries and the posted question content; andthe subsequent actions the user performs on the CQA site.Our work develops novel insights into searcher intent andbehavior that lead to asking questions to the community,providing a foundation for more effective integration of automated web search and social information seeking.