The wealth of information and social resources online has raised the bar for the quality of decisions that individuals and businesses can make. Human computation and social mediums have also increased the potential for finding relevant information or opinions and making them a part of a decision-making process. However, the strategies that individuals employ when confronted with too much information—satisficing, information foraging, etc.—are more difficult to apply with a large, distributed group. Appsheet is a new technology foundation that uses a spreadsheet model of a decision to guide distributed search parties in support of decision-making applications.

Understanding query reformulation patterns is a key task towards next generation web search engines. If we can do that, then we can build systems able to understand and possibly predict user intent, providing the needed assistance at the right time, and thus helping users locate information more effectively and improving their web-search experience. As a step in this direction, we build a very accurate model for classifying user query reformulations into broad classes (generalization, specialization, error correction or parallel move), achieving 92 % accuracy. We then apply the model to automatically label two very large query logs sampled from different geographic areas, and containing a total of approximately 17 million query reformulations. We study the resulting reformulation patterns, matching some results from previous studies performed on smaller manually annotated datasets, and discovering new interesting reformulation patterns, including connections between reformulation types and topical categories. We annotate two large query-flow graphs with reformulation type information, and run several

Abstract. The World Wide Web has been showing an incredible capacity

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