Search (2 results, page 1 of 1)

0.022996342 = product of:
  0.09198537 = sum of:
    0.09198537 = weight(_text_:web in 2931) [ClassicSimilarity], result of:
      0.09198537 = score(doc=2931,freq=20.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.5701118 = fieldWeight in 2931, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2931)
  0.25 = coord(1/4)

Content

The Web of User Contribution - Foundations and Principles of the Social Web - Social Tagging - Rating and Filtering of Digital Resources Empirical Analysisof User Contributions - The Functional and Linguistic Structure of Tags - A Comparative Analysis of Tags for Different Digital Resource Types - Exploring Relevance Assessments in Social IR Systems - Exploring User Contribution Within a Higher Education Scenario - Summary of Empirical Results and Implications for Designing Social Information Systems User Contribution for a Participative Information System - Social Information Architecture for an Online Help System

Object

Web 2.0

RSWK

World Wide Web 2.0 / Benutzer / Online-Publizieren / Information Retrieval / Soziale Software / Hilfesystem
Social Tagging / Filter / Web log / World Wide Web 2.0

Subject

World Wide Web 2.0 / Benutzer / Online-Publizieren / Information Retrieval / Soziale Software / Hilfesystem
Social Tagging / Filter / Web log / World Wide Web 2.0

0.01924012 = product of:
  0.07696048 = sum of:
    0.07696048 = weight(_text_:web in 3452) [ClassicSimilarity], result of:
      0.07696048 = score(doc=3452,freq=14.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.47698978 = fieldWeight in 3452, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.2635105 = idf(docFreq=4597, 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.

Object

Web 2.0