Search (44 results, page 1 of 3)

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Abstract

This paper introduces a project to develop a reliable, cost-effective method for classifying Internet texts into register categories, and apply that approach to the analysis of a large corpus of web documents. To date, the project has proceeded in 2 key phases. First, we developed a bottom-up method for web register classification, asking end users of the web to utilize a decision-tree survey to code relevant situational characteristics of web documents, resulting in a bottom-up identification of register and subregister categories. We present details regarding the development and testing of this method through a series of 10 pilot studies. Then, in the second phase of our project we applied this procedure to a corpus of 53,000 web documents. An analysis of the results demonstrates the effectiveness of these methods for web register classification and provides a preliminary description of the types and distribution of registers on the web.

Date

4. 8.2015 19:22:04

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Abstract

S R Ranganathan's ideas have influenced library classification since the inception of his Colon Classification in 1933. His address at Elsinore, "Library Classification Through a Century", was his grand vision of the century of progress in classification from 1876 to 1975, and looked to the future of faceted classification as the means to provide a cohesive system to organize the world's information. Fifty years later, the internet and its achievements, social ecology, and consequences present a far more complicated picture, with the library as he knew it as a very small part and the problems that he confronted now greatly exacerbated. The systematic nature of Ranganathan's canons, principles, postulates, and devices suggest that modern semantic algorithms could guide automatic subject tagging. The vision presented here is one of internet-wide faceted classification and retrieval, implemented as open, distributed facets providing unified faceted searching across all web sites.

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Content

Vortrag im Rahmen der Session 93. Cataloguing der WORLD LIBRARY AND INFORMATION CONGRESS: 76TH IFLA GENERAL CONFERENCE AND ASSEMBLY, 10-15 August 2010, Gothenburg, Sweden - 149. Information Technology, Cataloguing, Classification and Indexing with Knowledge Management

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Abstract

Online discussion forums have become a popular medium for users to discuss with and seek information from other users having similar interests. A typical discussion thread consists of a sequence of posts posted by multiple users. Each post in a thread serves a different purpose providing different types of information and, thus, may not be equally useful for all applications. Identifying the purpose and nature of each post in a discussion thread is thus an interesting research problem as it can help in improving information extraction and intelligent assistance techniques. We study the problem of classifying a given post as per its purpose in the discussion thread and employ features based on the post's content, structure of the thread, behavior of the participating users, and sentiment analysis of the post's content. We evaluate our approach on two forum data sets belonging to different genres and achieve strong classification performance. We also analyze the relative importance of different features used for the post classification task. Next, as a use case, we describe how the post class information can help in thread retrieval by incorporating this information in a state-of-the-art thread retrieval model.

Date

22. 1.2016 11:50:46

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Abstract

The behavior of modern web robots varies widely when they crawl for different purposes. In this article, we present a framework to classify these web robots from two orthogonal perspectives, namely, their functionality and the types of resources they consume. Applying the classification framework to a year-long access log from the UConn SoE web server, we present trends that point to significant differences in their crawling behavior.

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Abstract

With the emergence of Web 2.0, sharing personal content, communicating ideas, and interacting with other online users in Web 2.0 communities have become daily routines for online users. User-generated data from Web 2.0 sites provide rich personal information (e.g., personal preferences and interests) and can be utilized to obtain insight about cyber communities and their social networks. Many studies have focused on leveraging user-generated information to analyze blogs and forums, but few studies have applied this approach to video-sharing Web sites. In this study, we propose a text-based framework for video content classification of online-video sharing Web sites. Different types of user-generated data (e.g., titles, descriptions, and comments) were used as proxies for online videos, and three types of text features (lexical, syntactic, and content-specific features) were extracted. Three feature-based classification techniques (C4.5, Naïve Bayes, and Support Vector Machine) were used to classify videos. To evaluate the proposed framework, user-generated data from candidate videos, which were identified by searching user-given keywords on YouTube, were first collected. Then, a subset of the collected data was randomly selected and manually tagged by users as our experiment data. The experimental results showed that the proposed approach was able to classify online videos based on users' interests with accuracy rates up to 87.2%, and all three types of text features contributed to discriminating videos. Support Vector Machine outperformed C4.5 and Naïve Bayes techniques in our experiments. In addition, our case study further demonstrated that accurate video-classification results are very useful for identifying implicit cyber communities on video-sharing Web sites.

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Abstract

All text is ephemeral. Some texts are more ephemeral than others. The web has proved to be among the most ephemeral and changing of information vehicles. The research note revisits Koehler's original data set after about 20 years since it was first collected. By late 2013, the number of URLs responding to a query had fallen to 1.6% of the original sample. A query of the 6 remaining URLs in February 2015 showed only 2 still responding.

Date

22. 1.2016 14:37:14

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Abstract

Since its foundation in 2006, Twitter has enjoyed a meteoric rise in popularity, currently boasting over 500 million users. Its short text nature means that the service is open to a variety of different usage patterns, which have evolved rapidly in terms of user base and utilization. Prior work has categorized Twitter users, as well as studied the use of lists and re-tweets and how these can be used to infer user profiles and interests. The focus of this article is on studying why and how Twitter users mark tweets as "favorites"-a functionality with currently poorly understood usage, but strong relevance for personalization and information access applications. Firstly, manual analysis and classification are carried out on a randomly chosen set of favorited tweets, which reveal different approaches to using this functionality (i.e., bookmarks, thanks, like, conversational, and self-promotion). Secondly, an automatic favorites classification approach is proposed, based on the categories established in the previous step. Our machine learning experiments demonstrate a high degree of success in matching human judgments in classifying favorites according to usage type. In conclusion, we discuss the purposes to which these data could be put, in the context of identifying users' patterns of interests.

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Content

Vortrag im Rahmen der Session 93. Cataloguing der WORLD LIBRARY AND INFORMATION CONGRESS: 76TH IFLA GENERAL CONFERENCE AND ASSEMBLY, 10-15 August 2010, Gothenburg, Sweden - 149. Information Technology, Cataloguing, Classification and Indexing with Knowledge Management

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Footnote

Pressestimmen - In diesem faszinierenden Buch machen Eric Schmidt und Jared Cohen von ihrer einzigartigen Sachkenntnis Gebrauch, um uns eine Zukunft auszumalen, in der die Einkommen steigen, die Partizipation zunimmt und ein echter Sinn für Gemeinschaft entsteht - vorausgesetzt, wir treffen heute die richtigen Entscheidungen. (Bill Clinton) - Dieses Buch erklärt sowohl, was die neue Welt ausmacht, die das Internet schafft, als auch die Herausforderungen, die sie mit sich bringt. Niemand könnte das besser als Eric Schmidt und Jared Cohen. (Tony Blair) - Selbst wer nicht alle Schlussfolgerungen teilen mag, wird viel von diesem anregenden Buch lernen. (Henry A. Kissinger) - Auf dieses Buch habe ich gewartet: Eine prägnante und überzeugende Darstellung der Auswirkungen, die Technologie auf Krieg und Frieden, Freiheit und Diplomatie hat ... - Eine unverzichtbare Lektüre. (Madeleine Albright) -Dies ist das wichtigste - und faszinierendste - Buch, das bislang über die Auswirkungen des Digitalzeitalters auf unsere Welt geschrieben wurde. (Walter Isaacson) - «Die Vernetzung der Welt» verbindet auf faszinierende Weise Konzepte und Einblicke darüber, wie die sich die virtuelle Welt und die internationale Staatenordnung durchkreuzen. (Robert B. Zoellick) - Kaum jemand auf der Welt beschäftigt sich mehr damit, sich das neue Digitalzeitalter auszumalen - und es zu gestalten - als Eric Schmidt und Jared Cohen. Mit diesem Buch werfen sie einen Blick in ihre Kristallkugel und laden uns ein, ihnen dabei über die Schulter zu schauen. (Michael Bloomberg) - Dieses Buch ist die aufschlussreichste Erkundung unserer Zukunft, die ich je gelesen habe. Ich konnte es gar nicht mehr weglegen. (Sir Richard Branson) - «Die Vernetzung der Welt» ist Pflichtlektüre für alle, die das Ausmaß der digitalen Revolution wirklich verstehen wollen. (General Michael Hayden - ehemaliger Direktor der CIA) - Trotz der Herkunft der Autoren verbreitet «Die Vernetzung der Welt» keine Silicon-Valley-Propaganda ... Und was noch wichtiger ist: Es hebt die Debatte über Technologie auf ein höheres Niveau - weg vom banalen Streit über den Nutzen von Dating-Apps, hin zu allgemeineren Frage nach der gegenseitigen Beeinflussung von Technologie und Macht. (The Economist) - Dieses Buch ist deutlich mehr als nur Science Fiction. Es diskutiert hellsichtig und offen die entscheidenden Fragen, denen wir uns schon jetzt stellen müssen. Wer die Welt der Zukunft verstehen will, sollte es daher unbedingt lesen. (NDR Kultur)

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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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Abstract

Social media is frequently used as a platform for the exchange of information and opinions as well as propaganda dissemination. But online content can be misused for the distribution of illicit information, such as violent postings in web forums. Illicit content is highly distributed in social media, while non-illicit content is unspecific and topically diverse. It is costly and time consuming to label a large amount of illicit content (positive examples) and non-illicit content (negative examples) to train classification systems. Nevertheless, it is relatively easy to obtain large volumes of unlabeled content in social media. In this article, an artificial immune system-based technique is presented to address the difficulties in the illicit content identification in social media. Inspired by the positive selection principle in the immune system, we designed a novel labeling heuristic based on partially supervised learning to extract high-quality positive and negative examples from unlabeled datasets. The empirical evaluation results from two large hate group web forums suggest that our proposed approach generally outperforms the benchmark techniques and exhibits more stable performance.

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Abstract

Managing the constant flow of incoming messages is a daily challenge faced by knowledge workers who use technologies such as e-mail and other digital communication tools. This study focuses on the most ubiquitous of these technologies, e-mail, and unobtrusively explores the ongoing inbox-management activities of thousands of users worldwide over a period of 8 months. The study describes the dynamics of these inboxes throughout the day and the week as users strive to handle incoming messages, read them, classify them, respond to them in a timely manner, and archive them for future reference, all while carrying out the daily tasks of knowledge workers. It then tests several hypotheses about the influence of specific inbox-management behaviors in mitigating the causes of e-mail overload, and proposes a continuous index that quantifies one of these inbox-management behaviors. This inbox clearing index (ICI) expands on the widely cited trichotomous classification of users into frequent filers, spring cleaners, and no filers, as suggested by Whittaker and Sidner (1996). We propose that the ICI allows shifting the focus, from classifying users to characterizing a diversity of user behaviors and measuring the relationships between these behaviors and desired outcomes.

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Abstract

Controversy is a complex concept that has been attracting attention of scholars from diverse fields. In the era of Internet and social media, detecting controversy and controversial concepts by the means of automatic methods is especially important. Web searchers could be alerted when the contents they consume are controversial or when they attempt to acquire information on disputed topics. Presenting users with the indications and explanations of the controversy should offer them chance to see the "wider picture" rather than letting them obtain one-sided views. In this work we first introduce a formal model of controversy as the basis of computational approaches to detecting controversial concepts. Then we propose a classification based method for automatic detection of controversial articles and categories in Wikipedia. Next, we demonstrate how to use the obtained results for the estimation of the controversy level of search queries. The proposed method can be incorporated into search engines as a component responsible for detection of queries related to controversial topics. The method is independent of the search engine's retrieval and search results recommendation algorithms, and is therefore unaffected by a possible filter bubble. Our approach can be also applied in Wikipedia or other knowledge bases for supporting the detection of controversy and content maintenance. Finally, we believe that our results could be useful for social science researchers for understanding the complex nature of controversy and in fostering their studies.

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Abstract

Given the task of finding influencers (opinion makers) for a given domain in a social network, we investigate (a) what is the relative importance of domain and authority signals, (b) what is the most effective way of combining signals (voting, classification, learning to rank, etc.) and how best to model the vocabulary signal, and (c) how large is the gap between supervised and unsupervised methods and what are the practical consequences. Our best results on the RepLab dataset (which improves the state of the art) uses language models to learn the domain-specific vocabulary used by influencers and combines domain and authority models using a Learning to Rank algorithm. Our experiments show that (a) both authority and domain evidence can be trained from the vocabulary of influencers; (b) once the language of influencers is modeled as a likelihood signal, further supervised learning and additional network-based signals only provide marginal improvements; and (c) the availability of training data sets is crucial to obtain competitive results in the task. Our most remarkable finding is that influencers do use a distinctive vocabulary, which is a more reliable signal than nontextual network indicators such as the number of followers, retweets, and so on.

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Abstract

Purpose - This paper is the final one in a series which has tried to give an overview of so-called transformational areas of digital library technology. The aim has been to assess how much real transformation these applications can bring about, in terms of creating genuine user benefit and also changing everyday library practice. Design/methodology/approach - The paper provides a summary of some of the legal and ethical issues associated with web 2.0 applications in libraries, associated with a brief retrospective view of some relevant literature. Findings - Although web 2.0 innovations have had a massive impact on the larger World Wide Web, the practical impact on library service delivery has been limited to date. What probably can be termed transformational in the effect of web 2.0 developments on library and information work is their effect on some underlying principles of professional practice. Research limitations/implications - The legal and ethical challenges of incorporating web 2.0 platforms into mainstream institutional service delivery need to be subject to further research, so that the risks associated with these innovations are better understood at the strategic and policy-making level. Practical implications - This paper makes some recommendations about new principles of library and information practice which will help practitioners make better sense of these innovations in their overall information environment. Social implications - The paper puts in context some of the more problematic social impacts of web 2.0 innovations, without denying the undeniable positive contribution of social networking to the sphere of human interactivity. Originality/value - This paper raises some cautionary points about web 2.0 applications without adopting a precautionary approach of total prohibition. However, none of the suggestions or analysis in this piece should be considered to constitute legal advice. If such advice is required, the reader should consult appropriate legal professionals.

Date

22. 1.2011 17:54:04

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Abstract

Spam and wildly varying documents make searching in Twitter challenging. Most Twitter search systems generally treat a Tweet as a plain text when modeling relevance. However, a series of conventions allows users to Tweet in structural ways using a combination of different blocks of texts. These blocks include plain texts, hashtags, links, mentions, etc. Each block encodes a variety of communicative intent and the sequence of these blocks captures changing discourse. Previous work shows that exploiting the structural information can improve the structured documents (e.g., web pages) retrieval. In this study we utilize the structure of Tweets, induced by these blocks, for Twitter retrieval and Twitter opinion retrieval. For Twitter retrieval, a set of features, derived from the blocks of text and their combinations, is used into a learning-to-rank scenario. We show that structuring Tweets can achieve state-of-the-art performance. Our approach does not rely on social media features, but when we do add this additional information, performance improves significantly. For Twitter opinion retrieval, we explore the question of whether structural information derived from the body of Tweets and opinionatedness ratings of Tweets can improve performance. Experimental results show that retrieval using a novel unsupervised opinionatedness feature based on structuring Tweets achieves comparable performance with a supervised method using manually tagged Tweets. Topic-related specific structured Tweet sets are shown to help with query-dependent opinion retrieval.

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Abstract

We discuss the needs of a knowledge organization scheme for supporting Web-based software applications. We show how it differs from traditional knowledge organization schemes due to the virtual, dynamic, ad-hoc, userspecific and application-specific nature of Web-based knowledge. The sheer size of Web resources also adds to the complexity of organizing knowledge on the Web. As such, a standard, global scheme such as a single ontology for classifying and organizing all Web-based content is unrealistic. There is nevertheless a strong and immediate need for effective knowledge organization schemes to improve the efficiency and effectiveness of Web-based applications. In this context, we propose a novel knowledge organization scheme wherein concepts in the ontology of a domain are semantically interlinked with specific pieces of Web-based content using a rich hyper-linking structure known as Superlinks with well-defined semantic roles. We illustrate how such a knowledge organization scheme improves the efficiency and effectiveness of a Web-based e-commerce retail store.