Search (2 results, page 1 of 1)

0.0050945682 = product of:
  0.015283704 = sum of:
    0.015283704 = weight(_text_:information in 3452) [ClassicSimilarity], result of:
      0.015283704 = score(doc=3452,freq=6.0), product of:
        0.09099081 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0518325 = 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)
  0.33333334 = coord(1/3)

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

0.0050945682 = product of:
  0.015283704 = sum of:
    0.015283704 = weight(_text_:information in 5781) [ClassicSimilarity], result of:
      0.015283704 = score(doc=5781,freq=6.0), product of:
        0.09099081 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0518325 = queryNorm
        0.16796975 = fieldWeight in 5781, 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=5781)
  0.33333334 = coord(1/3)

Abstract

The goal of ResearchGate (RG) is to help users exchange scholarly information around the world. This study drew on adaptive structuration theory (AST) to investigate the social structure of RG, which had been largely overlooked by prior research. Data were crawled from RG and results were presented based on content analysis. For the social structure embedded in RG, the most frequent updates of structural features and spirit occurred in the first two years. Six representative updates for information exchange were analyzed and the newly embedded social structures were presented. For the social structure emerging in using RG, users were more willing to answer questions than ask questions, which countered intuition. Three categories were elicited to present the purpose and expectation of questions. Users were more willing to publish publications than publish projects. Compared with reading publications and projects published by others, users seldom commented on them. For the comparison between the two social structures, this paper analyzed and compared the two social structures in terms of three types of information exchange, finding that the social structure emerging in using RG differed from that embedded in RG. We suggest that this paper could potentially help the two social structures of RG promote the optimization of each other.