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The purpose of this paper is to explore different tagging behaviours between Chinese and Americans by analysing the movie tags, and explore the feasibility of applying cultural differences to tag recommendations.

This paper introduced hypotheses based on several well-established psychological theories and tested them with social tags for the same movies generated by Chinese and Americans. And to prove the utility of the cultural factor consideration, this paper conducted a cross-cultural tag recommendation experiment.

The results show that compared with Americans, Chinese users tend to add more tags about movies’ background information (e.g. release year) and global contextual characteristics (e.g. genre); they also prefer to add tags about production countries and factual tags, and cultural factors can be applied for recommending more accurate tags.

Other reasons for tagging differences beyond cultural factors have not be explored. Tags for some sample movies in MovieLens might be unstable, as they had been tagged by a small scale of users; as a result, the tags’ type distribution might be influenced.

The results and conclusion of this study will be beneficial for the cross-cultural applications of social tags and mining users’ interests based on tags.

This paper provided a deeper investigation of the cross-cultural effect in people’s social tagging behaviours from cognitive perspective, and an empirical analysis has been performed to explore proper approaches of incorporating cultural differences for tag recommendation.

This article describes the third part of a three-stage study investigating the information behaviour of fans and fan communities, the first stage of which is described in the study by Price and Robinson (2017).

Using tag analysis as a method, a comparative case study was undertaken to explore three aspects of fan information behaviour: information gatekeeping; classifying and tagging and entrepreneurship and economic activity. The case studies took place on three sites used by fans–Tumblr, Archive of Our Own (AO3) and Etsy. Supplementary semi-structured interviews with site users were used to augment the findings with qualitative data.

These showed that fans used tags in a variety of ways quite apart from classification purposes. These included tags being used on Tumblr as meta-commentary and a means of dialogue between users, as well as expressors of emotion and affect towards posts. On AO3 in particular, fans had developed a practice called “tag wrangling” to mitigate the inherent “messiness” of tagging. Evidence was also found of a “hybrid market economy” on Etsy fan stores. From the study findings, a taxonomy of fan-related tags was developed.

Findings are limited to the tagging practices on only three sites used by fans during Spring 2016, and further research on other similar sites are recommended. Longitudinal studies of these sites would be beneficial in understanding how or whether tagging practices change over time. Testing of the fan-tag taxonomy developed in this paper is also recommended.

This research develops a method for using tag analysis to describe information behaviour. It also develops a fan-tag taxonomy, which may be used in future research on the tagging practices of fans, which heretofore have been a little-studied section of serious leisure information users.

Suggested tag was considered as one of the critical factors affecting a user’s tagging behaviour. However, compared to the findings on the suggested tags for the monolingual environment, it still lacks focused studies on the tag suggestions for cross-language information. Therefore, this paper aims to concern with annotation behaviour and psychological cognition in the cross-language environment when suggested tags are provided.

A cross-language tagging experiment was conducted to explore the impact of suggested tags on the tagging results and process. The descriptive statistics of tags, the sources and semantic relations of tags, as well as the user’s psychological cognition were all measured in the test.

The experimental results demonstrated that the multilingual suggested tags could bring some costs to a user’s tagging perception. Furthermore, the language factor of suggested tags led to different paths of tagging imitation (reflected by longer semantic mapping and imitation at the visual level) and different cognitive processes (topic extraction and inference process).

To the best of the authors’ knowledge, this study is one of the first to emphasize the effect of suggested tags during multilingual tagging. The findings will enrich the theories of user-information interaction in the cross-language environment and, in turn, provide practical implications for tag-based information system design.

In the era of social media, users all over the world annotate books with social tags to express their preferences and interests. The purpose of this paper is to explore different tagging behaviours by analysing the book tags in different languages.

This investigation collected nearly 56,000 tags of 1,200 books from one Chinese and two English online bookmarking systems; it combined content analysis and machine-processing methods to evaluate the similarities and differences between different tagging systems from a cross-lingual perspective. Jaccard’s coefficient was adopted to evaluate the similarity level.

The results show that the similarity between mono-lingual tags of the same books is higher than that of cross-lingual tags in different systems and the similarity between tags of books written for specialties is higher than that of books written for the general public.

Those who have more in common annotate books with more similar tags. The similarity between users in tagging systems determines the similarity of the tag sets.

The results and conclusion of this study will benefit users’ cross-lingual information retrieval and cross-lingual book recommendation for online bookmarking systems.

This study may be one of the first to compare cross-lingual tags. Its methodology can be applied to tag comparison between any two languages. The insights of this study will help develop cross-lingual tagging systems and improve information retrieval.

Social library systems are Web 2.0 sites where users discover interesting books, movies, and music, etc., collect these resources to their personal libraries, and share their collections with others. The purpose of this study is to identify the information seeking modes adopted by users in this context as well as to reveal the characteristics of the users who are dominated by each mode.

An online survey was conducted to capture the background and behavior data of regular users from Douban, the most influential Chinese‐language social library system. The “friend‐of‐a‐friend” recruitment technique resulted in a total of 129 responses, 112 of which were valid and analyzed to generate both descriptive and inferential statistics.

Searching, browsing, encountering, and monitoring are the four major information seeking modes adopted by social library system users. The majority of the users tend to combine two or more modes, but each user has a dominating one that helps define him/her as a searcher, browser, encounterer, or monitor. While searching is the most widely adopted mode, browsers are the most prevalent type of information seekers. Different information seekers do not demonstrate significantly different characteristics by and large, however with some exceptions.

This study is one of the first to investigate how users look for resources in social library systems, a problem neglected by previous studies mostly focusing on how users organize and tag resources. The research findings enrich our understanding of social library systems as diverse and dynamic information seeking environments. This in turn will provide useful implications for their interface design to more effectively address the needs and expectations of special types of information seekers.

Visual Distinctive Language (VDL)-based iconic tags are structured visual information annotation. They explicate the content and organization of tagged information by graphical and symbolic features in order to improve the vocabulary problems of textual tags. The purpose of this paper is to investigate how these special icons help in tagged-based user information searching.

A two-stage experiment was designed and conducted so as to follow and quantify the searching process in specific searching target case and no specific searching target case when using VDL-based iconic tags.

The experimental results manifested that VDL-based iconic tags enhanced the role of tag in information searching. They could make user better understand tag clusters, which, in turn, provide global structure of involved topics. Also, VDL-based iconic tags helped user to find out searching target more quickly with higher accuracy by taking advantages of visual representation of tag categories and symbolic signification of tag content.

This study is one of the first to verify how structured icons work in information searching and how user’s graphical cognition impacts on tag-based information searching process. The research findings are dedicated to the theory of VDL-based iconic tags, as well as to a new visualization method for search user interface design.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

The purpose of this research is to investigate some general features of folksonomies and user‐generated content with copyright issues, and to present semantic representation for folksonomies using a tag ontology that can be used to represent tagging data at a semantic level using Semantic Web technologies.

An exploratory study is described that features current social tagging methods and copyright metadata. In particular, a tag ontology is extended for representing copyright metadata across different platforms.

The main finding is that Social Semantic Cloud of Tags can improve the expressive knowledge representation of folksonomies and that this ontology can aid in describing copyright metadata using some extended properties.

The paper gives a valuable insight into representing folksonomies with Semantic Web technologies that enable the representation, exchange, and reuse of tagging data, and provides a way to reduce the risk of copyright infringements in the process of tag sharing in folksonomies.

Through a theoretical review of the literature, this chapter assesses the potential of different knowledge organisation systems (KOS) to support corporate knowledge management systems (KMS), namely digital libraries (DL) in companies and other institutions. Questions are framed through which the chapter discusses how classical KOS, such as nomenclatures, classification systems, thesauri and ontologies, are able to reflect explicit knowledge in sense of the Semantic Web and also introduces persons as documents along with folksonomies as a means for externalising implicit knowledge in sense of the Web 2.0.

The purpose of this paper is to investigate tag use in a metadata ecosystem that supports a fan work repository to identify functions of tags and explore the system as a co-constructed communicative context.

Using modified techniques from grounded theory (Charmaz, 2007), this paper integrates humanistic and social science methods to identify kinds of tag use in a rich setting.

Three primary roles of tags emerge out of detailed study of the metadata ecosystem: tags can identify elements in the fan work, tags can reflect on how those elements are used or adapted in the fan work, and finally, tags can express the fan author’s sense of her role in the discursive context of the fan work repository. Attending to each of the tag roles shifts focus away from just what tags say to include how they say it.

Instead of building metadata systems designed solely for retrieval or description, this research suggests that it may be fruitful to build systems that recognize various metadata functions and allow for expressivity. This research also suggests that attending to metadata previously considered unusable in systems may reflect the participants’ sense of the system and their role within it.

In addition to accommodating a wider range of tag functions, this research implies consideration of metadata ecosystems, where different kinds of tags do different things and work together to create a multifaceted artifact.