Social tagging systems have established themselves as an important part in today's web and have attracted the interest of our research community in a variety of investigations. This has led to several assumptions about tagging, such as that tagging systems exhibit a social component. In this work we overcome the previous absence of data for testing such an assumption. We thoroughly study social interaction, leveraging for the first time live log data gathered from the real-world public social tagging system BibSonomy. Our results indicate that sharing of resources constitutes an important and indeed social aspect of tagging.
The representation and retrieval of books on the Skoob and GoodReads platforms is explored. In order to investigate the procedures and criteria of social indexing in the book platforms of Skoob and GoodReads, exploratory research was carried out in two phases: bibliographical research on social indexing, and analysis of the attribution and retrieval of representative terms in the book platforms of Skoob and GoodReads. The research results showed that both platforms offer the same basic services: organizing the users' readings and enabling interaction between them regarding their readings. What sets them apart are small details: GoodReads does not allow social indexing, although users can assign representative terms to the books that make up their personal shelf; on Skoob, the users perform the social indexing. Both platforms do not have any type of controlled vocabulary, directly affecting the representation and retrieval of books.
Authors focus on Flickr and Last.fm, two social media systems in which we can relate the tagging activity of the users with an explicit representation of their social network
By collectiong a set of US partisan Republican and Democratic videos, it is investigated how TikTok users communicate with each other about political issues
Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment,...
We propose a novel attention network for document annotation with user-generated tags. The network is designed according to the human reading and annotation behaviour. Usually, users try to digest the title and obtain a rough idea about the topic first, and then read the content of the document. Present research shows that the title metadata could largely affect the social annotation. To better utilise this information, we design a framework that separates the title from the content of a document and apply a title-guided attention mechanism over each sentence in the content. We also propose two semanticbased loss regularisers that enforce the output of the network to conform to label semantics, i.e. similarity and subsumption. We analyse each part of the proposed system with two real-world open datasets on publication and question annotation. The integrated approach, Joint Multi-label Attention Network (JMAN), significantly outperformed the Bidirectional Gated Recurrent Unit (Bi-GRU) by around 13%-26% and the Hierarchical Attention Network (HAN) by around 4%-12% on both datasets, with around 10%-30% reduction of training time.
This paper reviews research into social tagging and
folksonomy (as reflected in about 180 sources published
through December 2007). Methods of researching the
contribution of social tagging and folksonomy are described,
and outstanding research questions are presented. This is a
new area of research, where theoretical perspectives and
relevant research methods are only now being defined. This
paper provides a framework for the study of folksonomy,
tagging and social tagging systems. Three broad approaches
are identified, focusing first, on the folksonomy itself (and the
role of user tags in indexing and retrieval); secondly, on
tagging (and the behaviour of users); and thirdly, on the
nature of social tagging systems (as socio-technical frameworks).
This specification describes the FOAF language, defined as a dictionary of named properties and classes using W3C's RDF technology.
FOAF is a project devoted to linking people and information using the Web. Regardless of whether information is in people's heads, in physical or digital documents, or in the form of factual data, it can be linked. FOAF integrates three kinds of network: social networks of human collaboration, friendship and association; representational networks that describe a simplified view of a cartoon universe in factual terms, and information networks that use Web-based linking to share independently published descriptions of this inter-connected world. FOAF does not compete with socially-oriented Web sites; rather it provides an approach in which different sites can tell different parts of the larger story, and by which users can retain some control over their information in a non-proprietary format.
Journal of Information Science. we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given url.
R. Jäschke, A. Hotho, F. Mitzlaff, и G. Stumme. Recommender Systems for the Social Web, том 32 из Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)