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This study investigates how user-generated tags that describe musicians align with the name authority attributes specified in the Resource Description and Access (RDA) cataloging standard, specifically within the realm of popular music. Findings reveal significant alignment between user-generated tags and RDA person attributes, showcasing the potential for user-generated tags to enhance authority records and resolve complex queries. The study highlights the importance of adapting cataloging practices to leverage user-generated data in name authority records work to mitigate biases, improve identification of people, and enhance information resource access. [ABSTRACT FROM AUTHOR]

The paper comparatively investigates the relation between controlled vocabularies assigned by the experts in Library of Congress and tags assigned by users in Library Thing database in three subjects, Economics, History and Sociology under Social Science domain. Based on Term matching (S= 14.80 %, E= 12.77 % and H= 8.06 %) and Jaccard similarity coefficient (E= 0.15, S= 0.15 and H= 0.11), we found little matching between both vocabularies. We also found that experts mostly use double-word and multi-word specific topical terms (S= 73.14 %, E= 72.89 % and H= 61.05 %), whereas social taggers mostly use single-word general non-topical terms (E= 54.88 %, H= 54.21 % and S= 48.55%) and little topical and few personal terms. While comparison with LCSH subfield, we found that experts prefer topical terms for all subjects, whereas, taggers only prefer it for Economics and geographic subdivision terms for History and Sociology, but they don’t prefer chronological terms for tagging. Even, experts prefer little title-based terms (H= 196 terms, S= 195 terms and E= 175 terms) but taggers mostly prefer title-based terms (H= 673 terms, S= 564 terms and E= 444 terms) in three subjects. However, the study concludes that both vocabularies are different, but libraries can exploit those uncontrolled vocabularies and can introduce ‘hybrid metadata ecology’ which combines controlled vocabularies, classification and folksonomies for better subject access and retrieval of social science documents. [ABSTRACT FROM AUTHOR]

The ARTigo dataset, generated from over 10 million annotations, is a product of a citizen science project developed by the Institute of Art History and the Institute of Computer Science at LMU Munich. The project leverages Games with a Purpose (GWAPs) to foster a playful environment for tagging artworks. In these GWAPs, two anonymous players are given an image to annotate with textual or visual descriptors within a limited time frame. The annotations serve to improve the accessibility of art-historical images and offer vast research potential well beyond their utility as training datasets for Computer Vision (CV) algorithms.

This research aims to identify some patterns of author (as user) generated tags to the papers of library and information science field in Academia.edu. The research method is typically based on text analysis and word frequency distribution. The population contains over 6000 papers tagged in Academia.edu, and their abstracts were extracted from 159 English journals of the library and information science (LIS) field in the Scopus database. The growth of different types of tags in terms of the number of their words (one-word, two-word, three-word, and four-word and more), as well as the total number of tags over time, appeared as a logistic curve. It was also found that two-word tags had the most matching (54.92%) and four-word tags or more the least matching (1.76%) with different sections of papers (title, abstract, and authors' keywords). The total tags matched 7.5% with the title, 76.61% with the abstract, and 15.89% with the authors' keywords. Regarding the reuse of tags, it was revealed that on the one hand, 38.8% of the tags had been reused; on the other hand, 16% of the tags were reused in the first year, and more than 50% of the tags were reused in the first three years. Finally, it can be said that the users' consensus on specific terms can identify the new patterns of users' tagging at least partially compatible with professional indexing concepts, and by focusing on the most widely used tags and their sustainable distribution, the weighting of indexing terms and even classification schemes may be achieved. [ABSTRACT FROM AUTHOR]

Copyright of Campus Virtuales is the property of Campus Virtuales and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research aims to verify the patterns of users' tagging to the articles of knowledge and information science field in academia.edu. The research method is quantitative and based on text mining and applicable typically. The population includes 6086 bibliographic articles and their abstracts extracted from 159 English journals of knowledge and information science field in Scopus database that are core journals in LISTA as well. In order to gather these data, 194337 articles were searched in academia.edu then every article that tagged was chosen. Examining the relationship between the growth of different types of tags (one-word, two-word, three-word, and four-word and more) and increasing of documents showed a linear correlation between them. Among the different groups of tags, the highest growth rate was related to two-word tags (.609%) and the lowest growth rate was related to four-word tags and more (.143%). The total growth rate of the tags (new and duplicate) was also 5.52 (i.e. 5.52 tags per document). It was also found that two-word tags had the most matching (54.92%) and four-word tags and the least matching (1.76%) with different sections of articles (title, abstract, and authors' keywords). The total tags were matched 7.5% with the title, 76.61% with the abstract, and 15.89% with the authors' keywords. Regarding the reuse of tags, it was revealed that in general, 38.8% of the tags have been reused. On the other hand, two-word tags with 57.59% had the most reuse and four-word tags and more with 7.54% had the least. Another point is that 16% of the tags were reused in the first year and more than 50% of the tags were reused in the first 3 years. Finally, it can be said that the existence of a significant user consensus on certain terms indicates that the new patterns of user tags are at least partially compatible with professional indexing concepts about document content, and by focusing on the most widely used tags and their sustainable distribution, weight formulation and even classification schemes may be achieved. Also, users' activities on social networks can be used to increase the quality of suggestions in collective tagging systems. Another point is that there is a connection between professional indexing and user tagging, and the two are not alien to each other. This connectivity can be the basis for a complementary subject access system that enriches professional indexing.

Humanities scholars can today engage in research inquiry using data from a range of varied collections which are often characterised by poor subject access, often resulting in systems that underperform and even effectively prevent access to data, information and knowledge. In spite of the availability of professional standards and guidelines to provide quality-controlled subject access through knowledge organisation systems (KOS), subject access in such collections is rarely based on KOS. At the same time, KOS themselves may come with problems such as being slow to update, being rigidly structured and not incorporating end-users' vocabulary. It may therefore be useful to consider methods for remediating these deficiencies in KOSs, such as collecting user-generated metadata via social tagging or complementing automated indexing techniques with manual ones. To help address the above problems, the paper discusses these challenges and points to possible solutions in different contexts. It does so by reflecting on an open educational resource (OER) devoted to this theme, titled Introduction to Knowledge Organisation Systems for Digital Humanities. It was developed as part of an EU project called DiMPAH (Digital Methods Platform for the Arts and Humanities), 2021–2023, creating seven OERs for inclusion in DARIAH Teach. [ABSTRACT FROM AUTHOR]

The type, quantity, and scale of social-tagging systems have grown constantly in recent years as users' interest increases. Tags have important reference value in the study of networked communities since they typically represent user preference. This paper aims to examine how a tagging community evolves and to check the impact of new tags on evolution. Therefore, we proposed an improved evolution model for tag communities where tags constantly accumulate without withdrawal. Based on the model, we conducted an evolution analysis on three different tag communities with the datasets generated from the Delicious bookmarking system, CiteULike, and Douban. The results from Delicious emphasized that new individuals have an enormous influence on the community evolution, for they dominate the Form event, lead the early Split event, indirectly have a hand in the Merge event, and affect existing tags' transfer when they flood into the system. Moreover, new tags are proved to be more influential in tagging relation data of CiteULike and Douban, where new tags dominate the Split event. The in-depth and detailed depiction of community evolution helps us understand the evolution process of tag communities and the crucial role of new tags. [ABSTRACT FROM AUTHOR]

With more than one billion active users, Instagram is one of the most widely utilized social media platforms. Although recent research has begun to analyze brand-related images, Instagram remains largely neglected within halal food research. In this study, we aim to fill this research gap by collecting, labeling, aggregating, clustering, analyzing, and mapping halal food images, text, and social tagging on Instagram. In total, approximately 95,000 photos related to #halalfood tag were extracted from Instagram along with data related to photo captions, social tags, and comments on the posted photos. Google's Cloud Vision Application Programming Interface (API) was employed for image labeling to represent context of the photos. The photos were categorized, based on their label, into food, place, advertisement, event, and unhealthy food. The captions and comments in each category were analyzed using the associate network and sentiment analysis approaches. The study found the most frequent tags in Instagram posts, besides the obvious halal food related tags, were #halalfoodexpo, #halalfoodkorea, #halalfoodfestival, and #burger. Furthermore, the most influential tags, besides the halal food related tags, were #halalfoodexpo, #chicken, #halalfoodkorea, #halaltourism, and #repost. In addition, it was found that most of the Instagram data contain positive sentiments towards halal food. [ABSTRACT FROM AUTHOR]

The Wikipedia category system was designed to enable browsing and navigation of Wikipedia. It is also a useful resource for knowledge organisation and document indexing, especially using automatic approaches. However, it has received little attention as a resource for manual indexing. In this article, a hierarchical taxonomy of three-level depth is extracted from the Wikipedia category system. The resulting taxonomy is explored as a lightweight alternative to expert-created knowledge organisation systems (e.g. library classification systems) for the manual labelling of open-domain text corpora. Combining quantitative and qualitative data from a crowd-based text labelling study, the validity of the taxonomy is tested and the results quantified in terms of interrater agreement. While the usefulness of the Wikipedia category system for automatic document indexing is documented in the pertinent literature, our results suggest that at least the taxonomy we derived from it is not a valid instrument for manual subject matter labelling of open-domain text corpora. [ABSTRACT FROM AUTHOR]

This research aims to verify the patterns of users’ tagging to the articles of knowledge and information science field in academia.edu. The research method is quantitative and based on text mining and applicable typically. The population includes 6086 bibliographic articles and their abstracts extracted from 159 English journals of knowledge and information science field in Scopus database that are core journals in LISTA as well. In order to gather these data, 194337 articles were searched in academia. edu then every tagged article was chosen. Examining the relationship between the growth of different types of tags (one-word, two-word, threeword, and four-word and more) and increasing of documents showed a linear correlation between them. Among the different groups of tags, the highest growth rate was related to two-word tags (0.609%) and the lowest growth rate was related to four-word tags and more (0.143%). The total growth rate of the tags (new and duplicate) was also 5.52 (i.e. 5.52 tags per document). It was also found that two-word tags had the most matching (54.92%) and four-word tags and the least matching (1.76%) with different sections of articles (title, abstract, and authors’ keywords). The total tags were matched 7.5% with the title, 76.61% with the abstract, and 15.89% with the authors’ keywords. Regarding the reuse of tags, it was revealed that in general, 38.8% of the tags have been reused. On the other hand, two-word tags with 57.59% had the most reuse and four-word tags and more with 7.54% had the least. Another point is that 16% of the tags were reused in the first year and more than 50% of the tags were reused in the first 3 years. Finally, it can be said that the existence of a significant user consensus on certain terms indicates that the new patterns of user tags are at least partially compatible with professional indexing concepts about document content, and by focusing on the most widely used tags and their sustainable distribution, weight formulation and even classification schemes may be achieved. Also, users’ activities on social networks can be used to increase the quality of suggestions in collective tagging systems. Another point is that there is a connection between professional indexing and user tagging, and the two are not alien to each other. This connectivity can be the basis for a complementary subject access system that enriches professional indexing. [ABSTRACT FROM AUTHOR]

Access to information in multisource environments is facing many problems. One of them is the source selection problem. As more and more sources become available on the internet, how to select the relevant sources that meet the user needs is a big challenge. In this paper, we propose a multi-dimensional source selection approach based on topic modelling, which integrates both the social dimension and the intelligent dimension in order to optimize the source selection according to different user interests. Social tagging data is analyzed to discover relevant topics of user interests and latent relationships between users and sources based on topic modelling. By intelligently exploring a large search space of possible solutions, an (optimal) selection of sources is found using an intelligent method (a genetic algorithm). The proposed approach is evaluated on real data sources. The experimental results demonstrate that the proposed approach outperforms state-of-the-art source selection algorithms. [ABSTRACT FROM AUTHOR]

Recommendation Systems have been well established to reduce the problem of information overload and have become one of the most valuable tools applicable to different domains like computer science, mathematics, psychology etc. Despite its popularity and successful deployment in different commercial environments, this area is still exploratory due to the rapid development of social media which has accelerated the development of social recommendation systems. This paper addresses the key motivation for social media sites to apply recommendation techniques, unique properties of social recommendation systems, classification of social recommendation systems on the basis of basic models, comparison with existing traditional recommender systems, key findings from positive and negative experiences in applying social recommendation systems. Consequently, the aim of this paper is to provide research directions to improve the capability of social recommendation systems including the heterogeneous nature of social networks, understanding the role of negative relations, coldstart problems, integrating the cross-domain data and its applicability to a broader range of applications. This study will help the researchers and academicians in planning future social recommendation studies for designing a unified and coherent social recommendation system. [ABSTRACT FROM AUTHOR]

Ontology and social tagging, despite the differences they have with each other, are the new ways of organizing, representing and sharing knowledge in the electronic environment and can help promote each other. The aim of this research is to describe the relationships and applications of these two in conjunction with each other. The present study is written with a library and conceptual approach. Necessary information was gathered using the study of printed and electronic information resources available in libraries, the Internet, and Persian and English databases. Although the position of tags is in Web 2.0 and the ontology position is in Semantic Web 2.0, but we can use tags in the semantic web and ontologies in Web 2.0. The use of ontology in Web 2.0 causes creation of semantic structure and fixes its defects and weaknesses in organizing and retrieving that resulting from difficulties related to users, vocabulary as well as system weaknesses in the tagging in Web 2.0. The use of tags in ontology, also, causes updating, making them more functional and accepting them in the user community. The lack or deficiency of these is due to the lack of attention paid to end users by ontology and also the literature and resources used to create ontologies are not up to date

Abstract User tags in social network are valuable information for many applications such as Web search, recommender systems and online advertising. Thus, extracting high quality tags to capture user interest has attracted many researchers’ study in recent years. Most previous studies inferred users’ interest based on text posted in social network. In some cases, ordinary users usually only publish a small number of text posts and text information is not related to their interest very much. Compared with famous user, it is more challenging to find non-famous (ordinary) user’s interest. In this paper, we propose a probabilistic topic model, Bi-Labeled LDA, to automatically find interest tags for non-famous users in social network such as Twitter. Instead of extracting tags from text posts, tags of non-famous users are inferred from interest topics of famous users. With the proposed model, the formulation of social relationship between non-famous users and famous user is simulated and interest tags of famous users are exploited to supervise the training of the model and to make use of latent relation among famous users. Furthermore, the influence of popularity of famous user and popular tags are considered, and tags of non-famous users are ranked based on random walk model. Experiments were conducted on Twitter real datasets. Comparison with state-of-the-art methods shows that our method is more superior in terms of both ranking and quality of the tagging results.

Objective: Findability is a critical factor of usability and visibility in information architecture. One of the preparations developed to support the findability of social networks is the use of social tagging. Social tagging is an effective way for users to organize, manage, share and search various types of resources. This research done with aim of investigate information tagging behavior and the degree to which the content themes were matched in the educational and successful pages in Instagram visibility, to reach a model for social tagging that can be found to increase the information findability on Instagram that leads to success in information visibility on this social network site. Methodology: This was a qualitative and exploratory study, carried out through thematic analysis. Thematic analysis is a way to see the text, the good perception and understanding of apparently unrelated information, qualitative information analysis, systematic observation of person, group, position, organization or culture; the transformation of quantitative data into quantitative data. In this regard, more than 2,800 labels (hashtags), from the Instagram theme and educational pages, were selected as a targeted sample based on a competitive bench-marking approach, and analyzed. While classifying hashtags, they were matched to the theme of the contents. Theses hashtags were surveyed from shared posts in 30 selected pages that picked by purposed sampling. Findings: The findings showed that most content with more than 42% of the posts surveyed was related to the photo and the lowest content was allocated to the film with more than 26%. 2844 hashtags dedicated to 300 Instagram posts were categorized into thematic, critical, exclusive, common, descriptive, emphasis, location and event categories. The subject, common, and exclusive tags and hashtags were assigned more to content and used less to emphatic and critical hashtags. Having a well-defined and policy in using of hashtags, the assignment of subject and related hashtags to content, the use of exclusive hashtags for customizing and branding were the behaviors that were showed in these pages in regard of using social tagging. The findings also indicated that 76 percent of the investigated hashtags matched to the theme of the shared content and were related to the main topic and theme of these pages. This can also cause networking among pages in same theme. The co-occurrence of the theme codes and subject hashtags suggests that the subject code has a co-occurrence with a large number of theme codes, which is due to the fact that many pages have focused on subject hashtags among a variety of hashtags. Conclusion: Investigations showed that the selected pages in the posts surveyed used a specific pattern for hashtaging. Most hashtags were in Persian, and in many cases hashtags contributed to the understanding of visual content because they were sharing the main theme of the content. Successful pages in visibility have more applied subject hashtags in the same time they used less and more the other categories. The type of tags assigned to the content depended on the pages' context. Tagging behavior may reflect information sharing motivations and the hashtags assigned in educational pages can be used as a pattern for the pictures organization, according to their relevance to content theme.

Information retrieval is the challenge of the Web 2.0 world. The experiment of knowledge organisation in the context of abundant information available from various sources proves a major hurdle in obtaining information retrieval with greater precision and recall. The fast-changing landscape of information organisation through social networking sites at a personal level creates a world of opportunities for data scientists and also library professionals to assimilate the social data with expert created data. Thus, folksonomies or social tags play a vital role in information organisation and retrieval. The comparison of these user-created tags with expert-created index terms, author keywords and title words, will throw light on the differentiation between these sets of data. Such comparative studies show revelation of a new set of terms to enhance subject access and reflect the extent of similarity between user-generated tags and other set of terms. The CiteULike tags extracted from 5,150 scholarly journal articles in marine science were compared with corresponding Aquatic Science and Fisheries Abstracts descriptors, author keywords, and title terms. The Jaccard similarity coefficient method was employed to compare the social tags with the above mentioned wordsets, and results proved the presence of user-generated keywords in Aquatic Science and Fisheries Abstracts descriptors, author keywords, and title words. While using information retrieval techniques like stemmer and lemmatization, the results were found to enhance keywords to subject access.

This research determines an identification system for the types of Beiguan music -- a historical, nonclassical music genre -- by combining artificial neural network (ANN), social tagging, and music information retrieval (MIR). Based on the strategy of social tagging, the procedure of this research includes: evaluating the qualifying features of 48 Beiguan music recordings, quantifying 11 music indexes representing tempo and instrumental features, feeding these sets of quantized data into a three-layered ANN, and executing three rounds of testing, with each round containing 30 times of identification. The result of ANN testing reaches a satisfying correctness (97% overall) on classifying three types of Beiguan music. The purpose of this research is to provide a general attesting method, which can identify diversities within the selected non-classical music genre, Beiguan. The research also quantifies significant musical indexes, which can be effectively identified. The advantages of this method include improving data processing efficiency, fast MIR, and evoking possible musical connections from the high-relation result of statistical analyses. [ABSTRACT FROM AUTHOR]

Copyright of Campus Virtuales is the property of Campus Virtuales and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of this paper is to define a utilization model of meaningful users' tags in subject indexing work in libraries. The research work was originally performed with a quantitative method; a large number of relations (tag–bibliographic record) were examined and analyzed, resulting in a definition of the classes of the model. This model was attempted to be verified by a survey addressed to cataloguers in Greek libraries. This paper is based on the principle that the users' collaboration and their vocabulary provide useful feedback for the enhancement of the subject description of the documents. [ABSTRACT FROM AUTHOR]

The study attempts to compare user-generated social tags with expert-generated LCSH descriptors of one thousand sociology books. The objective is to examine if social tags can be used to enhance the accessibility of library collections. The study found that both datasets do not follow the same vocabulary. Though, the Spearmans' rank correlation (0.89) indicates a good association between common terms in both vocabularies. The Jaccard similarity coefficient (J = 0.13, 0.14, 0.17, 0.15 and 0.16) in different word clusters proves that top frequent social tags and top frequent LCSH descriptors used by users and experts are different. The comparison with each book also reveals that 555 books (55.5%) have 50 to 100 percent matching between both vocabularies. LCSH descriptor vocabulary contains more subject terms (24) than social tag vocabulary (12) out of the top thirty frequent terms. The comparison of social tags with MARC subfields ($a, $x, $y, $z, $v) reveals that users use more or less all the subfield terms as tags but either they do not use chronological terms ($y) for tags or use different terms other than experts for chronological information. Further, comparison with each book title reveals that social tags alongside LCSH descriptors can enhance the title-based search of libraries. Moreover, the study suggests that usage of social tags will not only enhance the accessibilities of library resources under sociology but also complement to controlled vocabularies by supplementing a variety of terms other than experts. [ABSTRACT FROM AUTHOR]

Network representation learning has attracted much attention as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network information. In this paper, in the light of user co-tag network and social network, we introduced network representation learning techniques into the learning of user preference, to encode user social relations into a continuous vector space. First, we proposed a hybrid network representation learning approach to effectively utilize users' tagging and social relationships, and then we took it for service recommendation. The experimental results show that, compared with four baselines on two public data sets, the improvement ratio over the baselines is up to 50% in terms of Recall@10 and Precision@10 and the improvement is even more than 90% in terms of NDGG@10 and MRR@10.

The purpose is review the literature to identify the epistemological and theoretical approach to the folksonomies and compare them with the theoretical foundations of controlled vocabularies. This paper is a library research. A review of the literature, identify and review theoretical approaches related to the folksonomies includes Critical social theory, Social constructivism, Relativism, Derrida’s deconstruction and hospitality and compared them with the theoretical foundations of controlled vocabulary . In theoretical approaches emphasized that people and users are different and have their different experience and knowledge, So they formed different concepts in mind and used different words to express their concepts that are different from one user to another user. These diversity concepts is represented in folksonomies. But in controlled vocabularies, concepts that only exist in the minds of catalogers is represented which may different with concepts that are in the minds of users. On the other hand, relativity can create the lack of uniformity and it is considered a disadvantage for folksonomies. While, uniformity is considered in controlled vocabularies and is an strength for them. In conclusion reviewed theoretical approaches provide theoretical basis for judging folksonomies capabilities, advantages and disadvantages and comparing them to controlled vocabularies, but must consider findings in future researches.

Tags, when properly assigned to limited access papers, help users to estimate their relevance. This paper introduces a new approach for the selection of relevant tags as well as a recommendation for scientific papers tagging. The approach defines the relatedness between the tags attributed by users and the concepts extracted from the available sections of scientific papers based on statistical, structural and semantic aspects. Two different term-based graphs ( R 1 -graph and R 2 -graph) were generated whose vertices indicate the terms and the edges represent the relatedness score between these terms. In addition, two algorithms were implemented to select and recommend the relevant tags: the neighbor-algorithm and the best-path-algorithm. The results of the experiments performed on a CiteULike collection of tagged papers show significant improvements only for the tagging of abstracted scientific articles. The approach was evaluated by referring to the full text of the papers with expert evaluation and comparing the tags generated by CiteULike users. Using the neighbor-algorithm, 80% of the top 10 recommended tags based on R 2 -graph and 76% of the top 10 recommended tags based on the R 1 -graph were relevant. While only 62% of those recommended by CiteULike users were relevant. The best-path-algorithm gave the best results in the top 20 and top 30 recommended tags and this in comparison with the tags recommended by the neighbor-algorithm and the tags assigned by CiteULike users. [ABSTRACT FROM AUTHOR]

Social tagging allows users to assign any free-form keywords as tags to any digital resources through a decentralised way. Many information scientists find that there are similarities through their studies between usergenerated social tags and the librarian-generated subject headings for the libraries. The present study was conducted to identify the similarity and dissimilarity between user-generated social tags and librarian-generated subject terms of 1000 books in the domain of History. The study also conducted to identify whether social tags can replace controlled vocabularies. The study finds that only a small portion of terms overlaps with each other (3.54% of social tags & 56.07% of SLSH terms) and Spearman's rank correlation proves that there is a good association between overlapping terms. Jaccard similarity coefficient highlights that users and the librarian use different terminologies (as J = 0.13, 0.12 & 0.11). Individual title wise comparison also defines that 90 per cent (88.4%) of all book titles where users and the librarian use at least one common term. Users use the least subject & non-subject terms but use some personal tags for personal benefit whereas the librarian use only subject & non-subject terms. Matching with each book title clarifies that for describing resources users mostly use title based keywords (696) whereas the librarian use very little title based keywords (113). The study clearly defines that social tags can enhance the experience of library users. If it can be exploited properly it can complement to controlled vocabularies but can not replace the controlled vocabularies used for libraries a long time. Overall the study explicitly identifies the viability regarding the adoption of social tags into the library databases where the resources in the field of history will be accessed. [ABSTRACT FROM AUTHOR]

User tags in social network are valuable information for many applications such as Web search, recommender systems and online advertising. Thus, extracting high quality tags to capture user interest has attracted many researchers' study in recent years. Most previous studies inferred users' interest based on text posted in social network. In some cases, ordinary users usually only publish a small number of text posts and text information is not related to their interest very much. Compared with famous user, it is more challenging to find non-famous (ordinary) user's interest. In this paper, we propose a probabilistic topic model, Bi-Labeled LDA, to automatically find interest tags for non-famous users in social network such as Twitter. Instead of extracting tags from text posts, tags of non-famous users are inferred from interest topics of famous users. With the proposed model, the formulation of social relationship between non-famous users and famous user is simulated and interest tags of famous users are exploited to supervise the training of the model and to make use of latent relation among famous users. Furthermore, the influence of popularity of famous user and popular tags are considered, and tags of non-famous users are ranked based on random walk model. Experiments were conducted on Twitter real datasets. Comparison with state-of-the-art methods shows that our method is more superior in terms of both ranking and quality of the tagging results. [ABSTRACT FROM AUTHOR]

Die Fotografie hat sich seit ihrer Entstehung zum Massenmedium entwickelt. Die technische Weiterentwicklung führte im 20. und 21. Jahrhundert zur Explosion der fotografischen Überlieferungen. Mit dem Wandel zur Informations- und Mediengesellschaft und der steigenden Bedeutung der Fotografie als wissenschaftliche Quelle, steigen auch die Anforderungen an Archive, diese Quellengattung angemessen zu erschließen und der Forschung zugänglich zu machen. Für die Archive bedeutet die Erschließung von Fotobeständen eine hohe Herausforderung. Einerseits ist die große Masse kaum zu bewältigen, andererseits fehlt es den Mitarbeiterinnen und Mitarbeitern häufig an Wissen über den historischen Kontext. In den Archivwissenschaften werden derzeit neue Erschließungsmethoden diskutiert und erprobt, um durch sogenanntes Crowdsourcing Nutzerinnen und Nutzer an der Erschließung zu beteiligen. Der Artikel beschreibt verschiedene Crowdsourcing-Projekte für die Erschließung genealogischer und fotografischer Archivalien und stellt Chancen und Risiken gegenüber. Photography has developed into a mass medium since its creation. In the 20th and 21st century, the technical development has led to the explosion of photographic collections and fonds. With the change to an information and media society and the growing importance of photography as a scientific source, the requirements placed on archives have also increased, the aim being to adequately tap into this kind of source and make it accessible to research. For the archives, the content description of photos is a great challenge. On the one hand, the great mass is hard to deal with, and on the other hand, employees often lack knowledge of the historical context. In archival sciences, new development methods are currently being discussed and tested in order to involve users in the description and recording process through so-called crowdsourcing. The article describes various crowdsourcing projects for the content description of genealogical and photographic sources and presents opportunities and risks. [ABSTRACT FROM AUTHOR]