Search (12 results, page 1 of 1)

0.0047219303 = product of:
  0.018887721 = sum of:
    0.018887721 = weight(_text_:information in 1880) [ClassicSimilarity], result of:
      0.018887721 = score(doc=1880,freq=14.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.3078936 = fieldWeight in 1880, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=1880)
  0.25 = coord(1/4)

Abstract

While the Web has become a worldwide platform for communication, terrorists share their ideology and communicate with members on the Dark Web - the reverse side of the Web used by terrorists. Currently, the problems of information overload and difficulty to obtain a comprehensive picture of terrorist activities hinder effective and efficient analysis of terrorist information on the Web. To improve understanding of terrorist activities, we have developed a novel methodology for collecting and analyzing Dark Web information. The methodology incorporates information collection, analysis, and visualization techniques, and exploits various Web information sources. We applied it to collecting and analyzing information of 39 Jihad Web sites and developed visualization of their site contents, relationships, and activity levels. An expert evaluation showed that the methodology is very useful and promising, having a high potential to assist in investigation and understanding of terrorist activities by producing results that could potentially help guide both policymaking and intelligence research.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.8, S.1347-1359

0.0039907596 = product of:
  0.015963038 = sum of:
    0.015963038 = weight(_text_:information in 4242) [ClassicSimilarity], result of:
      0.015963038 = score(doc=4242,freq=10.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2602176 = fieldWeight in 4242, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=4242)
  0.25 = coord(1/4)

Abstract

With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich knowledge base. The knowledge comes not only from the content of the pages themselves, but also from the unique characteristics of the Web, such as its hyperlink structure and its diversity of content and languages. Analysis of these characteristics often reveals interesting patterns and new knowledge. Such knowledge can be used to improve users' efficiency and effectiveness in searching for information an the Web, and also for applications unrelated to the Web, such as support for decision making or business management. The Web's size and its unstructured and dynamic content, as well as its multilingual nature, make the extraction of useful knowledge a challenging research problem. Furthermore, the Web generates a large amount of data in other formats that contain valuable information. For example, Web server logs' information about user access patterns can be used for information personalization or improving Web page design.

Source

Annual review of information science and technology. 38(2004), S.289-330

0.0035694437 = product of:
  0.014277775 = sum of:
    0.014277775 = weight(_text_:information in 492) [ClassicSimilarity], result of:
      0.014277775 = score(doc=492,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.23274569 = fieldWeight in 492, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.09375 = fieldNorm(doc=492)
  0.25 = coord(1/4)

Source

Proceedings of ACM SIGIR 23rd International Conference on Research and Development in Information Retrieval. Ed. by N.J. Belkin, P. Ingwersen u. M.K. Leong

0.0035694437 = product of:
  0.014277775 = sum of:
    0.014277775 = weight(_text_:information in 2733) [ClassicSimilarity], result of:
      0.014277775 = score(doc=2733,freq=8.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.23274569 = fieldWeight in 2733, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2733)
  0.25 = coord(1/4)

Abstract

While the Web has grown significantly in recent years, some portions of the Web remain largely underdeveloped, as shown in a lack of high-quality content and functionality. An example is the Arabic Web, in which a lack of well-structured Web directories limits users' ability to browse for Arabic resources. In this research, we proposed an approach to building Web directories for the underdeveloped Web and developed a proof-of-concept prototype called the Arabic Medical Web Directory (AMedDir) that supports browsing of over 5,000 Arabic medical Web sites and pages organized in a hierarchical structure. We conducted an experiment involving Arab participants and found that the AMedDir significantly outperformed two benchmark Arabic Web directories in terms of browsing effectiveness, efficiency, information quality, and user satisfaction. Participants expressed strong preference for the AMedDir and provided many positive comments. This research thus contributes to developing a useful Web directory for organizing the information in the Arabic medical domain and to a better understanding of how to support browsing on the underdeveloped Web.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.3, S.595-607

Theme

Information Gateway

0.0025760243 = product of:
  0.010304097 = sum of:
    0.010304097 = weight(_text_:information in 142) [ClassicSimilarity], result of:
      0.010304097 = score(doc=142,freq=6.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.16796975 = fieldWeight in 142, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=142)
  0.25 = coord(1/4)

Abstract

The World Wide Web presents significant opportunities for business intelligence analysis as it can provide information about a company's external environment and its stakeholders. Traditional business intelligence analysis on the Web has focused on simple keyword searching. Recently, it has been suggested that the incoming links, or backlinks, of a company's Web site (i.e., other Web pages that have a hyperlink pointing to the company of Interest) can provide important insights about the company's "online communities." Although analysis of these communities can provide useful signals for a company and information about its stakeholder groups, the manual analysis process can be very time-consuming for business analysts and consultants. In this article, we present a tool called Redips that automatically integrates backlink meta-searching and text-mining techniques to facilitate users in performing such business intelligence analysis on the Web. The architectural design and implementation of the tool are presented in the article. To evaluate the effectiveness, efficiency, and user satisfaction of Redips, an experiment was conducted to compare the tool with two popular business Intelligence analysis methods-using backlink search engines and manual browsing. The experiment results showed that Redips was statistically more effective than both benchmark methods (in terms of Recall and F-measure) but required more time in search tasks. In terms of user satisfaction, Redips scored statistically higher than backlink search engines in all five measures used, and also statistically higher than manual browsing in three measures.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.3, S.351-365

0.0025760243 = product of:
  0.010304097 = sum of:
    0.010304097 = weight(_text_:information in 3452) [ClassicSimilarity], result of:
      0.010304097 = score(doc=3452,freq=6.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.16796975 = fieldWeight in 3452, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3452)
  0.25 = coord(1/4)

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.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.891-906

0.0025760243 = product of:
  0.010304097 = sum of:
    0.010304097 = weight(_text_:information in 4980) [ClassicSimilarity], result of:
      0.010304097 = score(doc=4980,freq=6.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.16796975 = fieldWeight in 4980, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4980)
  0.25 = coord(1/4)

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.

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.256-269

0.0025239778 = product of:
  0.010095911 = sum of:
    0.010095911 = weight(_text_:information in 1872) [ClassicSimilarity], result of:
      0.010095911 = score(doc=1872,freq=4.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.16457605 = fieldWeight in 1872, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=1872)
  0.25 = coord(1/4)

Abstract

Despite the rapid growth of text-based computer-mediated communication (CMC), its limitations have rendered the media highly incoherent. This poses problems for content analysis of online discourse archives. Interactional coherence analysis (ICA) attempts to accurately identify and construct CMC interaction networks. In this study, we propose the Hybrid Interactional Coherence (HIC) algorithm for identification of web forum interaction. HIC utilizes a bevy of system and linguistic features, including message header information, quotations, direct address, and lexical relations. Furthermore, several similarity-based methods including a Lexical Match Algorithm (LMA) and a sliding window method are utilized to account for interactional idiosyncrasies. Experiments results on two web forums revealed that the proposed HIC algorithm significantly outperformed comparison techniques in terms of precision, recall, and F-measure at both the forum and thread levels. Additionally, an example was used to illustrate how the improved ICA results can facilitate enhanced social network and role analysis capabilities.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.8, S.1195-1209

0.0021033147 = product of:
  0.008413259 = sum of:
    0.008413259 = weight(_text_:information in 1527) [ClassicSimilarity], result of:
      0.008413259 = score(doc=1527,freq=4.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.13714671 = fieldWeight in 1527, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1527)
  0.25 = coord(1/4)

Abstract

As the Internet becomes ubiquitous, it has advanced to more closely represent aspects of the real world. Due to this trend, researchers in various disciplines have become interested in studying relationships between real-world phenomena and their virtual representations. One such area of emerging research seeks to study relationships between real-world and virtual activism of social movement organization (SMOs). In particular, SMOs holding extreme social perspectives are often studied due to their tendency to have robust virtual presences to circumvent real-world social barriers preventing information dissemination. However, many previous studies have been limited in scope because they utilize manual data-collection and analysis methods. They also often have failed to consider the real-world aspects of groups that partake in virtual activism. We utilize automated data-collection and analysis methods to identify significant relationships between aspects of SMO virtual communities and their respective real-world locations and ideological perspectives. Our results also demonstrate that the interconnectedness of SMO virtual communities is affected specifically by aspects of the real world. These observations provide insight into the behaviors of SMOs within virtual environments, suggesting that the virtual communities of SMOs are strongly affected by aspects of the real world.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.11, S.2210-2222

0.0017847219 = product of:
  0.0071388874 = sum of:
    0.0071388874 = weight(_text_:information in 2355) [ClassicSimilarity], result of:
      0.0071388874 = score(doc=2355,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.116372846 = fieldWeight in 2355, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2355)
  0.25 = coord(1/4)

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.11, S.1761-1775

0.0014872681 = product of:
  0.0059490725 = sum of:
    0.0059490725 = weight(_text_:information in 2753) [ClassicSimilarity], result of:
      0.0059490725 = score(doc=2753,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.09697737 = fieldWeight in 2753, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2753)
  0.25 = coord(1/4)

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.4, S.655-665

0.0014872681 = product of:
  0.0059490725 = sum of:
    0.0059490725 = weight(_text_:information in 3471) [ClassicSimilarity], result of:
      0.0059490725 = score(doc=3471,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.09697737 = fieldWeight in 3471, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3471)
  0.25 = coord(1/4)

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1213-1231