The dynamic features of Delicious, Flickr, and YouTube

Lin, Nan; Li, Daifeng; Ding, Ying; He, Bing; Qin, Zheng; Tang, Jie; Li, Juanzi; Dong, Tianxi

doi:10.1002/asi.21628

Cited by 15 publications

(10 citation statements)

References 37 publications

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“…While previous work mainly neglected the impact of individual's tagging pragmatics, our previous work showed that not all users contribute equally to the emergence of tag semantics and that "describer" are more useful than "categorizer" [19]. Similar to our work [21] also investigates tag distribution on a macro level (i.e., per system) and on a micro level (i.e., per resource). However, unlike in our work they use distribution fitting to explore the stabilization process.…”

Section: Related Workmentioning

confidence: 76%

Semantic stability in social tagging streams

Wagner

Singer

Strohmaier

et al. 2014

Proceedings of the 23rd International Conference on World Wide Web

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One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources may become semantically stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the semantic stability of social tagging systems in a robust and methodical way? (ii) Does semantic stabilization of tags vary across different social tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization processes in a wide range of social tagging systems with distinct domains and properties and (iii) detecting potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams which are generated via imitation dynamics or natural language phenomena alone.

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Section: Related Workmentioning

confidence: 76%

Semantic stability in social tagging streams

Wagner

Singer

Strohmaier

et al. 2014

Proceedings of the 23rd International Conference on World Wide Web

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“…We quote in this type of annotation system the websites CritLink (Yee, 2002), Delicious (Lin et al, 2012) and A.nnotate (Anagnostopoulou and Howell, 2009). …”

Section: Annotation Systems On the Webmentioning

confidence: 99%

A new annotation system based on a semantic analysis of a learner's annotative activity to invoke web services

Kalboussi

Mazhoud

Omheni

et al. 2014

IJMSO

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Many annotation systems proposed in the literature suffer from an under-exploitation of the annotation's semantic at the level of the assistance presented to the learner during annotative activity. Thus, these tools offer the same classic features to its users, such as management, search, sharing and storage of annotations. In this paper, we propose a new annotation system which differs in its novel features. The system tries to assist the learner via web services during learning activities. Therefore, from a user's annotation, our system is able to interpret a goal implicitly expressed and tries to discover and invoke a web service that can meet this annotation's objective. This system is based on an annotation model composed of an ontology and pattern annotation. This enrichment will provide advantages for both the provider, who will find his service within the consumer's reach, and the learner, who will have at his disposal an intelligent annotation tool.

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“…Lin et al. () used the perplexity method to choose the number of topics automatically. In our work, the LDA topic model will be used on the citing sentences and citing abstracts.…”

Section: Related Workmentioning

confidence: 99%

The differences between latent topics in abstracts and citation contexts of citing papers

Liu

Chen

2013

J Am Soc Inf Sci Tec

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Although it is commonly expected that the citation context of a reference is likely to provide more detailed and direct information about the nature of a citation, few studies in the literature have specifically addressed the extent to which the information in different parts of a scientific publication differs. Do abstracts tend to use conceptually broader terms than sentences in a citation context in the body of a publication? In this article, we propose a method to analyze and compare latent topics in scientific publications, in particular, from abstracts of papers that cited a target reference and from sentences that cited the target reference. We conducted an experiment and applied topical modeling techniques to fulltext papers in eight biomedicine journals. Topics derived from the two sources are compared in terms of their similarities and broad-narrow relationships defined based on information entropy. The results show that abstracts and citation contexts are characterized by distinct sets of topics with moderate overlaps. Furthermore, the results confirm that topics from abstracts of citing papers have broader terms than topics from citation contexts formed by citing sentences. The method and the findings could be used to enhance and extend the current methodologies for research evaluation and citation evaluation. Abstract topic 1 Abstract topic 2 Abstract topic 3 Abstract topic 4 Abstract topic 5 Abstract topic 6 Abstract topic 7 Abstract topic 8 Abstract topic 9 Abstract topic 0TABLE 7. The similarity matrix between two group topics. Topics Abstract topic 0 Abstract topic 1 Abstract topic 2 Abstract topic 3 Abstract topic 4 Abstract topic 5 Abstract topic 6 Abstract topic 7 Abstract topic 8 Abstract topic 9

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The dynamic features of Delicious, Flickr, and YouTube

Cited by 15 publications

References 37 publications

Semantic stability in social tagging streams

Semantic stability in social tagging streams

A new annotation system based on a semantic analysis of a learner's annotative activity to invoke web services

The differences between latent topics in abstracts and citation contexts of citing papers

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