Search (159 results, page 8 of 8)

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Abstract

Purpose - The purpose of this paper is to examine the question of whether modern digital information technologies damage their users' cognitive capacities in some way, and to speculate on how librarians should adapt their services as a consequence of the controversy surrounding this question. Design/methodology/approach - The paper reviews some recent literature on this subject, combined with an examination of the role played by technology, librarians and government in determining the nature of our society's response to problematic aspects of the use of digital, internet-based applications in education. Findings - The paper finds that highly differentiated and highly polemical attitudes to this subject mean that librarians have to acknowledge the existence of important challenges to the apparent consensus about the way information technologies should be used in education in Western societies. This has important consequences for the approach to collection building (the balance between digital versus print provision), for library building design, and for the value which should be placed on systematic information literacy teaching. The existence of such an important debate should also embolden information professionals to make their own insights into these issues more widely known. Research limitations/implications - Some of the findings in this paper are amenable to further development through practitioner-oriented research; however, the bulk of the content used for this paper is derived from the literature on this topic, so there is no original research data presented to back up the assertions made by the author. It is simply an account of a debate which has to be acknowledged by librarians. Practical implications - The implications of the issues under discussion in the paper are presented in clear practical terms, and the consequences for library management made explicit. Social implications - The clash between two different theories of learning and information provision is debated and the links with issues of government policy are explored. The social connections between education and wealth generation are brought into this debate. Originality/value - The paper provides a useful, up-to-date briefing on recent controversial issues in education, information management and socio-economic policy making.

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Abstract

Information extraction systems discover structured information in natural language text. Having information in structured form enables much richer querying and data mining than possible over the natural language text. However, information extraction is a computationally expensive task, and hence improving the efficiency of the extraction process over large text collections is of critical interest. In this paper, we focus on an especially valuable family of text collections, namely, the so-called deep-web text collections, whose contents are not crawlable and are only available via querying. Important steps for efficient information extraction over deep-web text collections (e.g., selecting the collections on which to focus the extraction effort, based on their contents; or learning which documents within these collections-and in which order-to process, based on their words and phrases) require having a representative document sample from each collection. These document samples have to be collected by querying the deep-web text collections, an expensive process that renders impractical the existing sampling approaches developed for other data scenarios. In this paper, we systematically study the space of query-based document sampling techniques for information extraction over the deep web. Specifically, we consider (i) alternative query execution schedules, which vary on how they account for the query effectiveness, and (ii) alternative document retrieval and processing schedules, which vary on how they distribute the extraction effort over documents. We report the results of the first large-scale experimental evaluation of sampling techniques for information extraction over the deep web. Our results show the merits and limitations of the alternative query execution and document retrieval and processing strategies, and provide a roadmap for addressing this critically important building block for efficient, scalable information extraction.

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Abstract

In this article, we describe the results of an experiment designed to understand the effects of background information and social interaction on image tagging. The participants in the experiment were asked to tag 12 preselected images of Jewish cultural heritage. The users were partitioned into three groups: the first group saw only the images with no additional information whatsoever, the second group saw the images plus a short, descriptive title, and the third group saw the images, the titles, and the URL of the page in which the image appeared. In the first stage of the experiment, each user tagged the images without seeing the tags provided by the other users. In the second stage, the users saw the tags assigned by others and were encouraged to interact. Results show that after the social interaction phase, the tag sets converged and the popular tags became even more popular. Although in all cases the total number of assigned tags increased after the social interaction phase, the number of distinct tags decreased in most cases. When viewing the image only, in some cases the users were not able to correctly identify what they saw in some of the pictures, but they overcame the initial difficulties after interaction. We conclude from this experiment that social interaction may lead to convergence in tagging and that the wisdom of the crowds helps overcome the difficulties due to the lack of information.

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Abstract

Understanding the behavior and characteristics of web users is valuable when improving information dissemination, designing recommendation systems, and so on. In this work, we explore various methods of predicting the ratio of male viewers to female viewers on YouTube. First, we propose and examine two hypotheses relating to audience consistency and topic consistency. The former means that videos made by the same authors tend to have similar male-to-female audience ratios, whereas the latter means that videos with similar topics tend to have similar audience gender ratios. To predict the audience gender ratio before video publication, two features based on these two hypotheses and other features are used in multiple linear regression (MLR) and support vector regression (SVR). We find that these two features are the key indicators of audience gender, whereas other features, such as gender of the user and duration of the video, have limited relationships. Second, another method is explored to predict the audience gender ratio. Specifically, we use the early comments collected after video publication to predict the ratio via simple linear regression (SLR). The experiments indicate that this model can achieve better performance by using a few early comments. We also observe that the correlation between the number of early comments (cost) and the predictive accuracy (gain) follows the law of diminishing marginal utility. We build the functions of these elements via curve fitting to find the appropriate number of early comments (approximately 250) that can achieve maximum gain at minimum cost.

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Abstract

Social networking sites (SNSs) can encourage interaction among users. Existing research mainly focuses on the ways in which SNSs are used in libraries and on librarians' or users' attitudes towards these SNSs. This study focused on the flow of information via SNS interactions between librarians and users on library Facebook, Twitter, and Chinese Weibo sites, and developed an SNS user interaction type model based on these information flows. A mixed-method approach was employed combining quantitative data generated from the analysis of 1,753 posts sampled from 40 library SNSs and qualitative data from interviews with 10 librarians. Four types of interactions were identified: information/knowledge sharing, information dissemination, communication, and information gathering. The study found that SNSs were used primarily as channels for disseminating news and announcements about things currently happening in the library. Communication allowed open-ended questions and produced more replies. In Facebook posts, Chinese Facebook users generated less "likes" than English-speaking users. The comparison of data between Facebook-like and Twitter-like SNSs in different library settings suggested that libraries need to coordinate different types of SNSs, and take library settings and sociocultural environments into consideration in order to enhance and encourage user engagement and interaction.

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Abstract

"The revolution will be Twittered!" declared journalist Andrew Sullivan after protests erupted in Iran. But as journalist and social commentator Evgeny Morozov argues in "The Net Delusion," the Internet is a tool that both revolutionaries and authoritarian governments can use. For all of the talk in the West about the power of the Internet to democratize societies, regimes in Iran and China are as stable and repressive as ever. Social media sites have been used there to entrench dictators and threaten dissidents, making it harder--not easier--to promote democracy. In this spirited book, journalist and social commentator Evgeny Morozov shows that by falling for the supposedly democratizing nature of the Internet, Western do-gooders may have missed how it also entrenches dictators, threatens dissidents, and makes it harder-not easier-to promote democracy. Buzzwords like "21st-century statecraft" sound good in PowerPoint presentations, but the reality is that "digital diplomacy" requires just as much oversight and consideration as any other kind of diplomacy. Marshalling a compelling set of case studies, " The Net Delusion" shows why the cyber-utopian stance that the Internet is inherently liberating is wrong, and how ambitious and seemingly noble initiatives like the promotion of "Internet freedom" are misguided and, on occasion, harmful.

Content

The Google doctrine -- Texting like it's 1989 -- Orwell's favorite lolcat -- Censors and sensibilities -- Hugo Chavez would like to welcome you to the spinternet -- Why the KGB wants you to join Facebook -- Why Kierkegaard hates slacktivism -- Open networks, narrow minds : cultural contradictions of internet freedom -- Internet freedoms and their consequences -- Making history (more than a browser menu) -- The wicked fix.

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Abstract

The degree of structure of Web content is the determining factor for the types of functionality that search engines can provide. The more well structured the Web content is, the easier it is for search engines to understand Web content and provide advanced functionality, such as faceted filtering or the aggregation of content from multiple Web sites, based on this understanding. Today, most Web sites are generated from structured data that is stored in relational databases. Thus, it does not require too much extra effort for Web sites to publish this structured data directly on the Web in addition to HTML pages, and thus help search engines to understand Web content and provide improved functionality. An early approach to realize this idea and help search engines to understand Web content is Microformats, a technique for markingup structured data about specific types on entities-such as tags, blog posts, people, or reviews-within HTML pages. As Microformats are focused on a few entity types, the World Wide Web Consortium (W3C) started in 2004 to standardize RDFa as an alternative, more generic language for embedding any type of data into HTML pages. Today, major search engines such as Google, Yahoo, and Bing extract Microformat and RDFa data describing products, reviews, persons, events, and recipes from Web pages and use the extracted data to improve the user's search experience. The search engines have started to aggregate structured data from different Web sites and augment their search results with these aggregated information units in the form of rich snippets which combine, for instance, data This chapter gives an overview of the topology of the Web of Data that has been created by publishing data on the Web using the microformats RDFa, Microdata and Linked Data publishing techniques.

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Abstract

Aimed at information and communication professionals, scholars and students, Google and the Digital Divide: The Biases of Online Knowledge provides invaluable insight into the significant role that search engines play in growing the digital divide between individuals, organizations, and states. With a specific focus on Google, author Elad Segev explains the concept of the digital divide and the effects that today's online environment has on knowledge bias, power, and control. Using innovative methods and research approaches, Segev compares the popular search queries in Google and Yahoo in the United States and other countries and analyzes the various biases in Google News and Google Earth. Google and the Digital Divide shows the many ways in which users manipulate Google's information across different countries, as well as dataset and classification systems, economic and political value indexes, specific search indexes, locality of use indexes, and much more. Segev presents important new social and political perspectives to illustrate the challenges brought about by search engines, and explains the resultant political, communicative, commercial, and international implications.

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