What Users Ask a Search Engine: Analyzing One Billion Russian Question Queries

We analyze the question queries submitted to a large commercial web search engine to get insights about what people ask, and to better tailor the search results to the users’ needs. Based on a dataset of about one billion question queries submitted during the year 2012, we investigate askers’ querying behavior with the support of automatic query categorization. While the importance of question queries is likely to increase, at present they only make up 3–4% of the total search traffic. Since questions are such a small part of the query stream and are more likely to be unique than shorter queries, clickthrough information is typically rather sparse. Thus, query categorization methods based on the categories of clicked web documents do not work well for questions. As an alternative, we propose a robust question query classification method that uses the labeled questions from a large community question answering platform (CQA) as a training set. The resulting classifier is then transferred to the web search questions. Even though questions on CQA platforms tend to be different to web search questions, our categorization method proves competitive with strong baselines with respect to classification accuracy. To show the scalability of our proposed method we apply the classifiers to about one billion question queries and discuss the trade-offs between performance and accuracy that different classification models offer. Our findings reveal what people ask a search engine and also how this contrasts behavior on a CQA platform

Exploring Topic-based Language Models for Effective Web Information Retrieval

The main obstacle for providing focused search is the relative opaqueness of search request -- searchers tend to express their complex information needs in only a couple of keywords. Our overall aim is to find out if, and how, topic-based language models can lead to more effective web information retrieval. In this paper we explore retrieval performance of a topic-based model that combines topical models with other language models based on cross-entropy. We first define our topical categories and train our topical models on the .GOV2 corpus by building parsimonious language models. We then test the topic-based model on TREC8 small Web data collection for ad-hoc search.Our experimental results show that the topic-based model outperforms the standard language model and parsimonious model

The Influence of Commercial Intent of Search Results on Their Perceived Relevance

We carried out a retrieval effectiveness test on the three major web search engines (i.e., Google, Microsoft and Yahoo). In addition to relevance judgments, we classified the results according to their commercial intent and whether or not they carried any advertising. We found that all search engines provide a large number of results with a commercial intent. Google provides significantly more commercial results than the other search engines do. However, the commercial intent of a result did not influence jurors in their relevance judgments

Events and Controversies: Influences of a Shocking News Event on Information Seeking

It has been suggested that online search and retrieval contributes to the intellectual isolation of users within their preexisting ideologies, where people's prior views are strengthened and alternative viewpoints are infrequently encountered. This so-called "filter bubble" phenomenon has been called out as especially detrimental when it comes to dialog among people on controversial, emotionally charged topics, such as the labeling of genetically modified food, the right to bear arms, the death penalty, and online privacy. We seek to identify and study information-seeking behavior and access to alternative versus reinforcing viewpoints following shocking, emotional, and large-scale news events. We choose for a case study to analyze search and browsing on gun control/rights, a strongly polarizing topic for both citizens and leaders of the United States. We study the period of time preceding and following a mass shooting to understand how its occurrence, follow-on discussions, and debate may have been linked to changes in the patterns of searching and browsing. We employ information-theoretic measures to quantify the diversity of Web domains of interest to users and understand the browsing patterns of users. We use these measures to characterize the influence of news events on these web search and browsing patterns

Characterizing Search Behavior in Productivity Software

Complex software applications expose hundreds of commands to users through intricate menu hierarchies. One of the most popular productivity software suites, Microsoft Office, has recently developed functionality that allows users to issue free-form text queries to a search system to quickly find commands they want to execute, retrieve help documentation or access web results in a unified interface. In this paper, we analyze millions of search sessions originating from within Microsoft Office applications, collected over one month of activity, in an effort to characterize search behavior in productivity software. Our research brings together previous efforts in analyzing command usage in large-scale applications and efforts in understanding search behavior in environments other than the web. Our findings show that users engage primarily in command search, and that re-accessing commands through search is a frequent behavior. Our work represents the first large-scale analysis of search over command spaces and is an important first step in understanding how search systems integrated with productivity software can be successfully developed