Search (16 results, page 1 of 1)

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Abstract

Social tagging is one of the major phenomena transforming the World Wide Web from a static platform into an actively shared information space. This paper addresses various aspects of social tagging, including different views on the nature of social tagging, how to make use of social tags, and how to bridge social tagging with other Web functionalities; it discusses the use of facets to facilitate browsing and searching of tagging data; and it presents an analogy between bibliometrics and tagometrics, arguing that established bibliometric methodologies can be applied to analyze tagging behavior on the Web. Based on the Upper Tag Ontology (UTO), a Web crawler was built to harvest tag data from Delicious, Flickr, and YouTube in September 2007. In total, 1.8 million objects, including bookmarks, photos, and videos, 3.1 million taggers, and 12.1 million tags were collected and analyzed. Some tagging patterns and variations are identified and discussed.

Theme

Social tagging

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Abstract

Data integration and mediation have become central concerns of information technology over the past few decades. With the advent of the Web and the rapid increases in the amount of data and the number of Web documents and users, researchers have focused on enhancing the interoperability of data through the development of metadata schemes. Other researchers have looked to the wealth of metadata generated by bookmarking sites on the Social Web. While several existing ontologies have capitalized on the semantics of metadata created by tagging activities, the Upper Tag Ontology (UTO) emphasizes the structure of tagging activities to facilitate modeling of tagging data and the integration of data from different bookmarking sites as well as the alignment of tagging ontologies. UTO is described and its utility in modeling, harvesting, integrating, searching, and analyzing data is demonstrated with metadata harvested from three major social tagging systems (Delicious, Flickr, and YouTube).

Theme

Social tagging

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Abstract

With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods.

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Abstract

Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain some of the irrational behaviors associated with financial decisions to help explain some of the fluctuations. In this study we argue that social media users who demonstrate an interest in finance can offer insights into ways in which irrational behaviors may affect a stock market. To test this, we analyzed all the data collected over a 3-month period in 2011 from Tencent Weibo (one of the largest microblogging websites in China). We designed a social influence (SI)-based Tencent finance-related moods model to simulate investors' irrational behaviors, and designed a Tencent Moods-based Stock Trend Analysis (TM_STA) model to detect correlations between Tencent moods and the Hushen-300 index (one of the most important financial indexes in China). Experimental results show that the proposed method can help explain the data fluctuation. The findings support the existing behavioral financial theory, and can help to understand short-term rises and falls in a stock market. We use behavioral financial theory to further explain our findings, and to propose a trading model to verify the proposed model.

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Abstract

The presence of social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using social tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems.

Theme

Social tagging

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Abstract

This article investigates the dynamic features of social tagging vocabularies in Delicious, Flickr, and YouTube from 2003 to 2008. Three algorithms are designed to study the macro- and micro-tag growth as well as the dynamics of taggers' activities, respectively. Moreover, we propose a Tagger Tag Resource Latent Dirichlet Allocation (TTR-LDA) model to explore the evolution of topics emerging from those social vocabularies. Our results show that (a) at the macro level, tag growth in all the three tagging systems obeys power law distribution with exponents lower than 1; at the micro level, the tag growth of popular resources in all three tagging systems follows a similar power law distribution; (b) the exponents of tag growth vary in different evolving stages of resources; (c) the growth of number of taggers associated with different popular resources presents a feature of convergence over time; (d) the active level of taggers has a positive correlation with the macro-tag growth of different tagging systems; and (e) some topics evolve into several subtopics over time while others experience relatively stable stages in which their contents do not change much, and certain groups of taggers continue their interests in them.

Theme

Social tagging

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Abstract

Reports results of a cocitation analysis study from the international retrieval research field from 1987 to 1997. Data was taken from Social SciSearch, via Dialog, and the top 40 authors were submitted to author cocitation analysis to yield the intellectual structure of information retrieval. The resulting multidimensional scaling map revealed: identifiable author groups for information retrieval; location of these groups with respect to each other; extend of centrality and peripherality of authors within groups, proximities of authors within groups and across group boundaries; and the meaning of the axes of the map. Factor analysis was used to reveal the extent of the authors' research areas and statistical routines included: ALSCAL; clustering analysis and factor analysis

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Abstract

This study explores the similarity among six types of scholarly networks aggregated at the institution level, including bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks. Cosine distance is chosen to measure the similarities among the six networks. The authors found that topical networks and coauthorship networks have the lowest similarity; cocitation networks and citation networks have high similarity; bibliographic coupling networks and cocitation networks have high similarity; and coword networks and topical networks have high similarity. In addition, through multidimensional scaling, two dimensions can be identified among the six networks: Dimension 1 can be interpreted as citation-based versus noncitation-based, and Dimension 2 can be interpreted as social versus cognitive. The authors recommend the use of hybrid or heterogeneous networks to study research interaction and scholarly communications.

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Abstract

This study proposes a new framework for citation content analysis (CCA), for syntactic and semantic analysis of citation content that can be used to better analyze the rich sociocultural context of research behavior. This framework could be considered the next generation of citation analysis. The authors briefly review the history and features of content analysis in traditional social sciences and its previous application in library and information science (LIS). Based on critical discussion of the theoretical necessity of a new method as well as the limits of citation analysis, the nature and purposes of CCA are discussed, and potential procedures to conduct CCA, including principles to identify the reference scope, a two-dimensional (citing and cited) and two-module (syntactic and semantic) codebook, are provided and described. Future work and implications are also suggested.

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Abstract

Innovations transform our research traditions and become the driving force to advance individual, group, and social creativity. Meanwhile, interdisciplinary research is increasingly being promoted as a route to advance the complex challenges we face as a society. In this paper, we use Latent Dirichlet Allocation (LDA) citation as a proxy context for the diffusion of an innovation. With an analysis of topic evolution, we divide the diffusion process into five stages: testing and evaluation, implementation, improvement, extending, and fading. Through a correlation analysis of topic and subject, we show the application of LDA in different subjects. We also reveal the cross-boundary diffusion between different subjects based on the analysis of the interdisciplinary studies. The results show that as LDA is transferred into different areas, the adoption of each subject is relatively adjacent to those with similar research interests. Our findings further support researchers' understanding of the impact formation of innovation.

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Abstract

Text mining has been widely used in multiple types of user-generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic-level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recall and f1-measure. Finally, we demonstrate some useful applications of TOIM in analyzing users' behaviors in Tencent Weibo.

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Abstract

Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision-making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.

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Abstract

This study comprises a suite of analyses of words in article titles in order to reveal the cognitive structure of Library and Information Science (LIS). The use of title words to elucidate the cognitive structure of LIS has been relatively neglected. The present study addresses this gap by performing (a) co-word analysis and hierarchical clustering, (b) multidimensional scaling, and (c) determination of trends in usage of terms. The study is based on 10,344 articles published between 1988 and 2007 in 16 LIS journals. Methodologically, novel aspects of this study are: (a) its large scale, (b) removal of non-specific title words based on the "word concentration" measure (c) identification of the most frequent terms that include both single words and phrases, and (d) presentation of the relative frequencies of terms using "heatmaps". Conceptually, our analysis reveals that LIS consists of three main branches: the traditionally recognized library-related and information-related branches, plus an equally distinct bibliometrics/scientometrics branch. The three branches focus on: libraries, information, and science, respectively. In addition, our study identifies substructures within each branch. We also tentatively identify "information seeking behavior" as a branch that is establishing itself separate from the three main branches. Furthermore, we find that cognitive concepts in LIS evolve continuously, with no stasis since 1992. The most rapid development occurred between 1998 and 2001, influenced by the increased focus on the Internet. The change in the cognitive landscape is found to be driven by the emergence of new information technologies, and the retirement of old ones.

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Date

22. 1.2011 13:02:21

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Abstract

Support for evolving ontologies is required in almost all situations where ontologies are used in real-world applications. In those cases, ontologies are often developed by several persons and will continue to evolve over time, because of changes in the real world, adaptations to different tasks, or alignments to other ontologies. To prevent that such changes will invalidate existing usage, a change management methodology is needed. This involves advanced versioning methods for the development and the maintenance of ontologies, but also configuration management, that takes care of the identification, relations and interpretation of ontology versions. All these aspects come together in integrated ontology library systems. When the number of different ontologies is increasing, the task of storing, maintaining and re-organizing them to secure the successful re-use of ontologies is challenging. Ontology library systems can help in the grouping and reorganizing ontologies for further re-use, integration, maintenance, mapping and versioning. Basically, a library system offers various functions for managing, adapting and standardizing groups of ontologies. Such integrated systems are a requirement for the Semantic Web to grow further and scale up. In this chapter, we describe a number of results with respect to the above mentioned areas. We start with a description of the alignment task and show a meta-ontology that is developed to specify the mappings. Then, we discuss the problems that are caused by evolving ontologies and describe two important elements of a change management methodology. Finally, in Section 4.4 we survey existing library systems and formulate a wish-list of features of an ontology library system.

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Date

22. 8.2014 16:52:04