Social Propagation: Boosting Social Annotations for Web Mining

Bao, Shenghua; Yang, Bo; Fei, Ben; Xu, Shun; Su, Zhong; Yu, Yong

doi:10.1007/s11280-009-0068-2

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Social bookmarking enables users to store, organize, search and last but not least share with other users bookmarks of web pages [65]. Some popular sites serving as social bookmarking are: del.icio.us [3,49], Furl, Google Bookmarks, Diigo.…”

Section: Social Bookmarking and Cataloguingmentioning

confidence: 99%

Social networks on the Internet

2012

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The rapid development and expansion of the Internet and the social-based services comprised by the common Web 2.0 idea provokes the creation of the new area of research interests, i.e. social networks on the Internet called also virtual or online communities. Social networks can be either maintained and presented by social networking sites like MySpace, LinkedIn or indirectly extracted from the data about user interaction, activities or achievements such as emails, chats, blogs, homepages connected by hyperlinks, commented photos in multimedia sharing system, etc. A social network is the set of human beings or rather their digital representations that refer to the registered users who are linked by relationships extracted from the data about their activities, common communication or direct links gathered in the internetbased systems. Both digital representations named in the paper internet identities as well as their relationships can be characterized in many different ways. Such diversity yields for building a comprehensive and coherent view onto the concept of internetbased social networks. This survey provides in-depth analysis and classification of social networks existing on the Internet together with studies on selected examples of different virtual communities.

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Section: Social Bookmarking and Cataloguingmentioning

confidence: 99%

Social networks on the Internet

2012

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“…A topic model method is used to analyze tagging actions in [18]. Bao et al explored the application of tag propagation processing for later web mining in [3]. In [7,10,20], latent feature based or user learning methods were used to discover latent concept hierarchy in the tag space.…”

Section: Related Workmentioning

confidence: 99%

Bursty event detection from collaborative tags

et al. 2011

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Collaborative tagging have emerged as a ubiquitous way to annotate and organize online resources. As a kind of descriptive keyword, large amount of tags are created and associated to multiple types of resources, e.g., web pages, photos, videos and tweets. Users' tagging actions over time reflect their changing interests. Monitoring and analyzing the temporal patterns of tags can provide important insights to trace hot topics on the web. Existing work focuses on deriving temporal patterns for individual tags. However, there exist remarkable correlations among tags assigned to online resources. In this paper, we propose a new approach to detect bursty tagging event, which captures the relations among a group of correlated tags where the tags are either bursty or associated with bursty tag co-occurrence. This kind of bursty tagging event generally corresponds to a real life event. It profiles the events with more representative and comprehensible clues. The proposed approach is divided into three stages. We exploit the sliding time intervals to extract bursty features as the first step, and then adopt graph clustering techniques to group bursty features into meaningful bursty events. We discuss the choice of similarity and granularity for event detection. After that, we further utilize an automatically generated tag taxonomy to organize bursty events to facilitate the burst oriented 172 World Wide Web (2012) 15:171-195 navigation and analysis. The experimental study on a large real data set demonstrates the superiority of our new approach.

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“…Heckner et al [26] have analyzed a survey that Flickr and Youtube users perceive tags, and they have used the survey results to improve the performance of the IR process. Bao et al [27] have found that the social tagging (or annotations) can benefit the web search in two aspects. First, the tags are usually good summaries of corresponding web pages.…”

Section: Searching By Tag Matchingmentioning

confidence: 99%

Discovering Community of Lingual Practice for Matching Multilingual Tags from Folksonomies

Jung

2011

The Computer Journal

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Many existing studies have investigated to discover a variety of co-occurrence patterns between entities (e.g. users, tags and resources) from a folksonomy system. The common purposes among them are (i) to understand collective behaviors between online users and (ii) to provide online services (e.g. tag recommendation and information searching) to users. However, most of the existing studies assume that all tags in the folksonomy should be written in an identical language. In this paper, we focus on analyzing a multilingual folksonomy generated by various lingual practices of online users, and discovering meaningful relationships between multilingual tags (e.g. between 'Seoul' in English and 'Corée'in French) co-occurred in the folksonomy. Thereby, we propose novel methods for (i) identifying lingual practices from user tagging patterns to build a community of lingual practice and (ii) exploiting the tag matchings to extend simple term-based queries. Thus, additional resources tagged by other languages can be retrieved. To evaluate the proposed multilingual tag matching method, we have collected real tagging datasets from several well-known social tagging websites (e.g. Del.icio.us), and applied to translating queries to other languages without any external dictionaries.

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Social Propagation: Boosting Social Annotations for Web Mining

Cited by 6 publications

References 28 publications

Social networks on the Internet

Social networks on the Internet

Bursty event detection from collaborative tags

Discovering Community of Lingual Practice for Matching Multilingual Tags from Folksonomies

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