In this paper, we analyze the relationship between web search behavior and confirmation bias, in which people prefer to browse information that supports their existing opinions and beliefs. We conducted an online user experiment in which 89 participants were asked to perform a web search task to obtain health information. In this experiment, we controlled the participants' prior beliefs by presenting them with prior information to manipulate their impressions of a search topic prior to performing the search task. We then analyzed their behavioral logs during the search task. The results demonstrate that participants with confirmation bias frequently browsed only the top search results and completed the search task quickly. The results also indicate that, even if participants with the confirmation bias possessed health literacy, they did not utilize this literacy, even though such literacy is essential when viewing health information on the web critically.

To obtain accurate information through web searches, people have to search for information carefully. This study investigates how the search behaviors and decision outcomes of searchers were affected by the documents they encountered during their search process. We focus on two document factors: (1) opinion (consistent and inconsistent) with the searchers' beliefs prior to the search task, and (2) credibility (high and low). We conducted a user study in which 260 participants were asked to perform health-related search tasks while controlling a search result with different opinions and credibility levels. The results revealed that (i) the participants spent more effort searching by issuing more queries, when belief-inconsistent documents were presented; (ii) the documents' opinion and credibility affected their belief dynamics, (i.e., how their beliefs changed after the search task); and (iii) their belief dynamics and search efforts had few relationships. These findings suggest that search engines could prevent users from polarization and thus, help them to obtain accurate information, by presenting documents that are inconsistent with users' beliefs on the higher-rank of the results.

In this study, we developed an Internet of Things (IoT) monitoring device to monitor over the people inside a room. We collected sensor data at a specific location using the device. Based on the data, we tried to predict the behavior of the person at that location. Monitoring and predicting human daily behavior is trivial task. Most of the research on monitoring and predicting daily life behavior are based on the data available from smart home [7] [16] [17]. But smart home is expensive compare to normal home, as different kind of sensor are attached in the room and have more facilities. So, we developed a low cost IoT monitoring device and predict the daily life behavior of human from the sensor data taken from the device. We can extract information from the daily life behavior and share it with the family living in distant places.

In this study, we propose a method to classify genre-based films using film screenplays. The proposed method vectorizes films into two aspects, i.e., film content (i.e., what films tell viewers) and screenplay structure (i.e., how the films narrate stories), and classifies film genres using the support vector machine method. We applied the Doc2Vec algorithm to screenplay structure and to handle film content. In film production, for vectorizing films, we used the statistics of the four screenplay elements: scene, action, dialogue, and transition. Compared with baseline methods, the evaluation showed that the proposed method is better for classifying films of specific genres.

This study proposes a system, which shows funny term pairs when searchers issue a query into Web search engines. The proposed system analyzes the following two factors for a term pair in a given query: the unexpectedness and the semantic conflict. The experimental result showed that the proposed method provided a larger number of funny term pairs for queries than the baseline methods. Although the proposed method was not the best based on the average value, it can still offer opportunities for searchers to laugh and feel cheery when issuing queries into the Web search engines.

We propose query priming to activate careful user information seeking in web searches. Query priming employs query auto-completion (QAC) and query suggestion (QS) to present search terms that stimulate critical thinking and encourages careful information seeking and decision making. We conducted an online user study using a crowdsourcing service. Analysis of search behavior logs and questionnaire responses confirmed the following. (1) With query priming, participants issued more queries and (re-)visited search engine result pages more frequently. (2) Query priming promoted webpage selection targeted at evidence-based decision making. (3) The query priming effect varied relative to participant educational background. This study contributes to search interaction design to enhance user engagement in critical thinking in web searches.

This study investigates how people carefully search for the Web to obtain credible and accurate information. The goal of this study is to better understand people's attitudes toward careful information seeking via Web search, and the relationship between such attitudes and their daily search behaviors. To this end, we conducted two experiments. We first administrated an online questionnaire to investigate how people's attitudes toward using the strategies for verifying information in the Web search process differ based on various factors such as their credulity toward Web information, individual thinking styles, educational background, and search expertise. We then analyzed their one-year and one-month query logs of a commercial Web search engine to explore how their daily search behaviors are different according to their attitudes. The analysis of the questionnaire and the query logs obtained from ¥subjects participants revealed that (i) the people's attitudes toward using the verification strategies in Web search are positively correlated to their Need for Cognition (NFC), educational background, and search expertise; (ii) people with strong attitudes are likely to click lower-ranked search results than those with intermediate levels of attitude; (iii) people with strong attitudes are more likely to use the terms such as "evidence'' or "truth'' in their queries, possibly to scrutinize the uncertain or incredible information; and (iv) the behavioral differences found in (ii) and (iii) are not identified from the differences in the participants' educational backgrounds. These findings help us explore future directions for a new Web search system that encourages people to be more careful in Web search, and suggest the need for an educational program or training to facilitate the attitudes and skills for using Web search engines to obtain accurate information.

Third-party applications (apps) drive the attractiveness of web and mobile application platforms. Many of these platforms adopt a decentralized control strategy, relying on explicit user consent for granting permissions that the apps request. Users have to rely primarily on community ratings as the signals to identify the potentially harmful and inappropriate apps even though community ratings typically reflect opinions about perceived functionality or performance rather than about risks. With the arrival of HTML5 web apps, such user-consent permission systems will become more widespread. We study the effectiveness of user-consent permission systems through a large scale data collection of Facebook apps, Chrome extensions and Android apps. Our analysis confirms that the current forms of community ratings used in app markets today are not reliable indicators of privacy risks of an app. We find some evidence indicating attempts to mislead or entice users into granting permissions: free applications and applications with mature content request more permissions than is typical; 'look-alike' applications which have names similar to popular applications also request more permissions than is typical. We also find that across all three platforms popular applications request more permissions than average.

In this paper, we propose a system for helping users to judge the credibility of Web search results and to search for credible Web pages. Conventional Web search engines present only titles, snippets, and URLs for users, which give few clues to judge the credibility of Web search results. Moreover, ranking algorithms of the conventional Web search engines are often based on relevance and popularity of Web pages. Towards credibility-oriented Web search, our proposed system provides users with the following three functions: (1) calculation and visualization of several scores of Web search results on the main credibility aspects, (2) prediction of user's credibility judgment model through user's credibility feedback for Web search results, and (3) re-ranking of Web search results based on user's predicted credibility model. Experimental results suggest that our system enables users - in particular, users with knowledge about search topics - to find credible Web pages from a list of Web search results more efficiently than conventional Web search interfaces.

This paper presents a novel musical performance system named onNote that directly utilizes printed music scores as a musical instrument. This system can make users believe that sound is indeed embedded on the music notes in the scores. The users can play music simply by placing, moving and touching the scores under a desk lamp equipped with a camera and a small projector. By varying the movement, the users can control the playing sound and the tempo of the music. To develop this system, we propose an image processing based framework for retrieving music from a music database by capturing printed music scores. From a captured image, we identify the scores by matching them with the reference music scores, and compute the position and pose of the scores with respect to the camera. By using this framework, we can develop novel types of musical interactions.

"furimifurazumi" is a lighting device that let users intuitively know about the sign of rain in architectural space by using ambient media, a light and sound. This device works with various expression of lighting and sound, which are generated by the precipitation data from internet and Live E! system[1]. Our system in connection with meteorological sensor node enables the user to feel the outside atmosphere in the room.