Sharpening the Search Saw: Lessons from Expert Searchers

Many students consider themselves to be proficient searchers and yet are disappointed or frustrated when faced with the task of locating relevant scholarly articles for a literature review. This bleak experience is common among higher education students, even for those in library and information science programs who have heightened appreciation for information resources and yet may settle for “good enough Googling” (Plosker, 2004, p. 34). This is in large part due to reliance on web search engines that have evolved relevance ranking into a vastly intelligent business, one in which we are both its customers and product (Vaidhyanathan, 2011). Google’s Hummingbird nest of search algorithms (Sullivan, 2013) provides quick and targeted hits, yet it can trigger blinders-on trust in first-page results. Concern for student search practices ranges from this permissive trust all the way to lost ability to recall facts and formulate questions (Abilock, 2015), lack of confidence in one’s own knowledge (Carr, 2010), and increased dependence on single search boxes that encourage stream-of-consciousness user input (Tucker, 2013); indeed, students may be high in tech savvy but lacking the critical thinking skills needed for information research tasks (Katz, 2007). Students have come to rely on web search engine intelligence—and it is inarguably colossal—to such an extent that they may fail to formulate a question before charging forward to search for its answer. “Google is known as a search engine, yet there is barely any searching involved anymore. The gap between a question crystallizing in your mind and an answer appearing at the top of your screen is shrinking all the time. As a consequence, our ability to ask questions is atrophying” (Leslie, 2015, para. 4). Highly accomplished students often lament their lack of skills for higher-level searching that calls for formulating pointed questions when struggling to develop a solid literature review. In addition, many are unaware that search results are filtered based on previous searches, location, and other factors extracted from personal search patterns by the search engine. Two students working side by side and entering the same search terms may receive quite different results on Google, yet the extent to which this ‘filter bubble’ (Pariser, 2011) is personalizing their search results is difficult to assess and to overcome. Just as important, it can be impossible to know what a search might be missing: how to know what’s not there? This portrayal of the information landscape may appear gloomy but, in fact, it could not be a more inspiring environment in which to do research, to find connections in ideas, and to benefit from and generate new ideas. A few lessons from expert searchers, focused on critical concepts and search practices, can sharpen a student’s search saw and move the proficient student-researcher, desiring more relevant and comprehensive search results, into a trajectory toward search expertise. For the lessons involved in this journey, the focus is on two areas: first, the critical concepts— called threshold concepts (Meyer & Land, 2003)— found to be necessary for developing search expertise (Tucker et al., 2014); and, second, four strategic areas within search that can have significant and immediate impact on improving search results for research literature. The latter are grounded in the threshold concepts and positioned for application to literature reviews for graduate student studies

Search engine For Twitter sentiment analysis

textThe purpose of sentiment analysis is to determine the attitude of a writer or a speaker with respect to some topic or his feeling in a document. Thanks to the rise of social media, nowadays there are numerous data generated by users. Mining and categorizing these data will not only bring profits for companies, but also benefit the nation. Sentiment analysis not only enables business decision makers to better understand customers' behaviors, but also allows customers to know how the public feel about a product before purchasing. On the other hand, the aggregation of emotions will effectively measure the public response toward an event or news. For example, the level of distress and sadness will increase significantly after terror attacks or natural disaster. In our project, we are going to build a search engine that allows users to check the sentiment of his query. Some of previous researches on classifying sentiment of messages on micro-blogging services like Twitter have tried to solve this problem but they have ignored neutral tweets, which will result in problematic results (12). Our sentiment analysis will also be based on tweets collected from twitter, since twitter can offer sufficient and real-time corpora for analysis. We will preprocess each tweet in the training set and label it as positive, negative or neutral. As we use words in the tweet as the feature for our model, different features will be used. We will show that accuracy achieved by different machine learning algorithms (Naïve Bayes, Maximum Entropy) can be improved with a feature vector obtained by using bigrams (5). In our practice, we find that Naive Bayes has better performance than Maximum Entropy.Statistic

Data Mining in Electronic Commerce

Modern business is rushing toward e-commerce. If the transition is done properly, it enables better management, new services, lower transaction costs and better customer relations. Success depends on skilled information technologists, among whom are statisticians. This paper focuses on some of the contributions that statisticians are making to help change the business world, especially through the development and application of data mining methods. This is a very large area, and the topics we cover are chosen to avoid overlap with other papers in this special issue, as well as to respect the limitations of our expertise. Inevitably, electronic commerce has raised and is raising fresh research problems in a very wide range of statistical areas, and we try to emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Utilizing a 3D game engine to develop a virtual design review system

A design review process is where information is exchanged between the designers and design reviewers to resolve any potential design related issues, and to ensure that the interests and goals of the owner are met. The effective execution of design review will minimize potential errors or conflicts, reduce the time for review, shorten the project life-cycle, allow for earlier occupancy, and ultimately translate into significant total project savings to the owner. However, the current methods of design review are still heavily relying on 2D paper-based format, sequential and lack central and integrated information base for efficient exchange and flow of information. There is thus a need for the use of a new medium that allow for 3D visualization of designs, collaboration among designers and design reviewers, and early and easy access to design review information. This paper documents the innovative utilization of a 3D game engine, the Torque Game Engine as the underlying tool and enabling technology for a design review system, the Virtual Design Review System for architectural designs. Two major elements are incorporated; 1) a 3D game engine as the driving tool for the development and implementation of design review processes, and 2) a virtual environment as the medium for design review, where visualization of design and design review information is based on sound principles of GUI design. The development of the VDRS involves two major phases; firstly, the creation of the assets and the assembly of the virtual environment, and secondly, the modification of existing functions or introducing new functionality through programming of the 3D game engine in order to support design review in a virtual environment. The features that are included in the VDRS are support for database, real-time collaboration across network, viewing and navigation modes, 3D object manipulation, parametric input, GUI, and organization for 3D objects

Assessing the Effectiveness and Usability of Personalized Internet Search through a Longitudinal Evaluation

This paper discusses a longitudinal user evaluation of Prospector, a personalized Internet meta-search engine capable of personalized re-ranking of search results. Twenty-one participants used Prospector as their primary search engine for 12 days, agreed to have their interaction with the system logged, and completed three questionnaires. The data logs show that the personalization provided by Prospector is successful: participants preferred re-ranked results that appeared higher up. However, the questionnaire results indicated that people would prefer to use Google instead (their search engine of choice). Users would, nevertheless, consider employing a personalized search engine to perform searches with terms that require disambiguation and/or contextualization. We conclude the paper with a discussion on the merit of combining system- and user-centered evaluation for the case of personalized systems