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

Channels, consumers and communication: online and offline communication in service consumption

This paper reports on a study that investigated consumer use of e-services in a multichannel context. To develop a deeper understanding of what makes consumers decide to use the online channel, and contrary to most HCI studies on the use of e-services that focus on the use of the online channel in relative isolation, this study examined consumer channel-choice beyond the instances of internet use. The consumption behaviour of its participants was investigated across channels in an in-depth qualitative study. The analysis of the elicited rich data focused specifically on the investigation of voluntary consumer movements between online and offline channels during the course of a consumption process. The results indicate that participants often use multiple channels in parallel and frequently switch between channels. Literature from marketing and consumer research was used as the perspective to explore the rationale for the complex and dynamic reported consumer behaviour

Using contextual information to understand searching and browsing behavior

There is great imbalance in the richness of information on the web and the succinctness and poverty of search requests of web users, making their queries only a partial description of the underlying complex information needs. Finding ways to better leverage contextual information and make search context-aware holds the promise to dramatically improve the search experience of users. We conducted a series of studies to discover, model and utilize contextual information in order to understand and improve users' searching and browsing behavior on the web. Our results capture important aspects of context under the realistic conditions of different online search services, aiming to ensure that our scientific insights and solutions transfer to the operational settings of real world applications

Search Bias Quantification: Investigating Political Bias in Social Media and Web Search

Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources—input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems—Twitter social media search and Google web search—for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.publishe

Software Engineering Methods for the Internet of Things: A Comparative Review

Accessing different physical objects at any time from anywhere through wireless network heavily impacts the living style of societies worldwide nowadays. Thus, the Internet of Things has now become a hot emerging paradigm in computing environments. Issues like interoperability, software reusability, and platform independence of those physical objects are considered the main current challenges. This raises the need for appropriate software engineering approaches to develop effective and efficient IoT applications software. This paper studies the state of the art of design and development methodologies for IoT software. The aim is to study how proposed approaches have been solved issues of interoperability, reusability, and independence of the platform. A comparative study is presented for the different software engineering methods used for the Internet of Things. Finally, the key research gaps and open issues are highlighted as future directions