Social Bookmarking Systems Research Articles

Logsonomy — A Search Engine Folksonomy

In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user's information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us.

Portraying Privacy Leakage of Public WiFi Systems for Users on Travel Spam Detection in Social Bookmarking System

In this paper, we depicts spam revelation, in perspective of the examination of posts, in social bookmarking districts. For consistent acknowledgment of spam posts, we propose a name estimation plot and a specific evaluation procedure for picking marks. The label estimation scores each tag. In the particular evaluation, the label scores in perspective of the utilization repeat and the degree of spammers are estimated and the thoughts of white tag and dim tag are introduced. Using these thoughts, names are proficiently arranged into the names demolishing the execution of spam revelation, the names pleasing in getting spammers, and the marks which should achieve a discipline. Finally, we propose semantic components to moreover upgrade the spam distinguishing proof

Density-based clustering of big probabilistic graphs

Clustering is a machine learning task to group similar objects in coherent sets. These groups exhibit similar behavior with-in their cluster. With the exponential increase in the data volume, robust approaches are required to process and extract clusters. In addition to large volumes, datasets may have uncertainties due to the heterogeneity of the data sources, resulting in the Big Data. Modern approaches and algorithms in machine learning widely use probability-theory in order to determine the data uncertainty. Such huge uncertain data can be transformed to a probabilistic graph-based representation. This work presents an approach for density-based clustering of big probabilistic graphs. The proposed approach deals with clustering of large probabilistic graphs using the graph’s density, where the clustering process is guided by the nodes’ degree and the neighborhood information. The proposed approach is evaluated using seven real-world benchmark datasets, namely protein-to-protein interaction, yahoo, movie-lens, core, last.fm, delicious social bookmarking system, and epinions. These datasets are first transformed to a graph-based representation before applying the proposed clustering algorithm. The obtained results are evaluated using three cluster validation indices, namely Davies–Bouldin index, Dunn index, and Silhouette coefficient. This proposal is also compared with four state-of-the-art approaches for clustering large probabilistic graphs. The results obtained using seven datasets and three cluster validity indices suggest better performance of the proposed approach.

Improving personalized recommendations using community membership information

While early recommender systems have mostly focused on numeric ratings to model their interests, recent research in this area has explored a range of other sources that can provide information about user interests, such as their bookmarks, tags, social links, or reviews. One source of information that has received little attention so far is users’ membership in online communities. Online communities frequently evolve around specific topics. Therefore, user membership in a community could be interpreted as a sign of user interests in the topics of a particular community, and furthermore, could apply to personalized recommendations as a source of information. This paper explores the feasibility and the value of using users’ community membership as a source of personalized recommendations for individual users. The first part of the paper focuses on feasibility. It attempts to assess to what extent the interests of users within the same community are truly similar. The second part focuses on the value of this information to personalized recommendations. It suggests several recommendation approaches that use community membership information. It also assesses the comparative quality of recommendations that are generated by these approaches. In particular, we substantiate our approach with one typical social bookmarking system, CiteULike. The results of our study demonstrate that the interests of members of the same communities are significantly closer than the interests of non-connected users. Moreover, we found that recommendation approaches based on community membership produce recommendations that are as accurate as those produced through a collaborative filtering approach, but with better efficiency. The recommendations are also more complete than those produced by a collaborative filtering approach. In addition, for cold-start users who have insufficient bookmarking information to reliably represent their interests, recommendations based on community membership are the most valuable.

Spam Detection in Social Bookmarking Systems Using Tag Scores and Selective Evaluation

In this paper we study spam detection in social bookmarking systems. We analyze posts and develop features to find spammers from users by focusing on analysis of tags. In calculating tag scores, we carefully select tags to give different weights depending on how much spammer and non-spammers like them. To further enhance performance, we propose features based on semantic similarity, tagging tendency, and quantitative tag information and combine them with the tag features. We evaluate the features using the well-known dataset and see they work in detecting spammers. In addition, we do experiments to see how our proposed features work in different environments, namely, different number of posts per user, different classifiers employed, and different levels of feature computation.

Tag clustering algorithm LMMSK: improved K-means algorithm based on latent semantic analysis

With the wide application of Web_2.0 and social software, there are more and more tag-related studies and applications. Because of the randomness and the personalization in users' tagging, tag research continues to encounter data space and semantics obstacles. With the min-max similarity (MMS) to establish the initial centroids, the traditional K-means clustering algorithm is firstly improved to the MMSK-means clustering algorithm, the superiority of which has been tested; based on MMSK-means and combined with latent semantic analysis (LSA), here secondly emerges a new tag clustering algorithm, LMMSK. Finally, three algorithms for tag clustering, MMSK-means, tag clustering based on LSA (LSA-based algorithm) and LMMSK, have been run on Matlab, using a real tag-resource dataset obtained from the Delicious Social Bookmarking System from 2004 to 2009. LMMSK's clustering result turns out to be the most effective and the most accurate. Thus, a better tag-clustering algorithm is found for greater application of social tags in personalized search, topic identification or knowledge community discovery. In addition, for a better comparison of the clustering results, the clustering corresponding results matrix (CCR matrix) is proposed, which is promisingly expected to be an effective tool to capture the evolutions of the social tagging system.

Posted, visited, exported: Altmetrics in the social tagging system BibSonomy

In social tagging systems, like Mendeley, CiteULike, and BibSonomy, users can post, tag, visit, or export scholarly publications. In this paper, we compare citations with metrics derived from users’ activities (altmetrics) in the popular social bookmarking system BibSonomy. Our analysis, using a corpus of more than 250,000 publications published before 2010, reveals that overall, citations and altmetrics in BibSonomy are mildly correlated. Furthermore, grouping publications by user-generated tags results in topic-homogeneous subsets that exhibit higher correlations with citations than the full corpus. We find that posts, exports, and visits of publications are correlated with citations and even bear predictive power over future impact. Machine learning classifiers predict whether the number of citations that a publication receives in a year exceeds the median number of citations in that year, based on the usage counts of the preceding year. In that setup, a Random Forest predictor outperforms the baseline on average by seven percentage points.

The Role of Cores in Recommender Benchmarking for Social Bookmarking Systems

Social bookmarking systems have established themselves as an important part in today’s Web. In such systems, tag recommender systems support users during the posting of a resource by suggesting suitable tags. Tag recommender algorithms have often been evaluated in offline benchmarking experiments. Yet, the particular setup of such experiments has rarely been analyzed. In particular, since the recommendation quality usually suffers from difficulties such as the sparsity of the data or the cold-start problem for new resources or users, datasets have often been pruned to so-called cores (specific subsets of the original datasets), without much consideration of the implications on the benchmarking results. In this article, we generalize the notion of a core by introducing the new notion of a set-core , which is independent of any graph structure, to overcome a structural drawback in the previous constructions of cores on tagging data. We show that problems caused by some types of cores can be eliminated using set-cores. Further, we present a thorough analysis of tag recommender benchmarking setups using cores. To that end, we conduct a large-scale experiment on four real-world datasets, in which we analyze the influence of different cores on the evaluation of recommendation algorithms. We can show that the results of the comparison of different recommendation approaches depends on the selection of core type and level. For the benchmarking of tag recommender algorithms, our results suggest that the evaluation must be set up more carefully and should not be based on one arbitrarily chosen core type and level.

Using social bookmarking for acquiring web resources for e-learning

As students increasingly acquire information from the internet for their formal or informal learning, the aim of this study is to analyse the use of social bookmarking to help students collaboratively access useful and creditable web resources, and hence developing their information literacy capability. We have developed an intelligent social bookmarking system, namely, (SE)^2, in this study. Control experiments on students' use of (SE)^2 to improve the quality of acquired web resources were conducted. The results show that (SE)^2 is an effective tool to facilitate the access of web resources and social bookmarking can be integrated into different e-learning activities to enhance students' learning.

Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system

In the last few years, social media systems have experienced a fast growth. The amount of content shared in these systems increases fast, leading users to face the well known “interaction overload” problem, i.e., they are overwhelmed by content, so it becomes difficult to come across interesting items. To overcome this problem, social recommender systems have been recently designed and developed in order to filter content and recommend to users only interesting items. This type of filtering is usually affected by the “over-specialization” problem, which is related to recommendations that are too similar to the items already considered by the users. This paper proposes a friend recommender system that operates in the social bookmarking application domain and is based on behavioral data mining, i.e., on the exploitation of the users activity in a social bookmarking system. Experimental results show how this type of mining is able to produce accurate friend recommendations, allowing users to get to know bookmarked resources that are both novel and serendipitous. Using this approach, the impact of the “interaction overload” and the “over-specialization” problems is strongly reduced.

What effects topological changes in dynamic graphs?

To describe the dynamics taking place in networks that structurally change over time, we propose an approach to search for vertex attributes whose value changes impact the topology of the graph. In several applications, it appears that the variations of a group of attributes are often followed by some structural changes in the graph that one may assume they generate. We formalize the triggering pattern discovery problem as a method jointly rooted in sequence mining and graph analysis. We apply our approach on three real-world dynamic graphs of different natures – a co-authoring network, an airline network, and a social bookmarking system – assessing the rele-vancy of the triggering pattern mining approach.

Personalizing Information Using Users’ Online Social Networks: A Case Study of CiteULike

This paper aims to assess the feasibility of a new and less-focused type of online sociability (the watching network) as a useful information source for personalized recommendations. In this paper, we recommend scientific articles of interests by using the shared interests between target users and their watching connections. Our recommendations are based on one typical social bookmarking system, CiteULike. The watching network-based recommendations, which use a much smaller size of user data, produces suggestions that are as good as the conventional Collaborative Filtering technique. The results demonstrate that the watching network is a useful information source and a feasible foundation for information personalization. Furthermore, the watching network is substitutable for anonymous peers of the Collaborative Filtering recommendations. This study shows the expandability of social network-based recommendations to the new type of online social networks.

Hybrid TRS-PSO Clustering Approach for Web2.0 Social Tagging System

Social tagging is one of the important characteristics of WEB2.0. The challenge of Web 2.0 is a huge amount of data generated over a short period. Tags are widely used to interpret and classify the web 2.0 resources. Tag clustering is the process of grouping the similar tags into clusters. The tag clustering is very useful for searching and organizing the web2.0 resources and also important for the success of Social Bookmarking systems. In this paper, the authors proposed a hybrid Tolerance Rough Set Based Particle Swarm optimization (TRS-PSO) clustering algorithm for clustering tags in social systems. Then the proposed method is compared to the benchmark algorithm K-Means clustering and Particle Swarm optimization (PSO) based Clustering technique. The experimental analysis illustrates the effectiveness of the proposed approach.

집단지성 기반 학습자료 북마킹 서비스 시스템

최근 IT 환경의 변화에 따라 웹 서비스를 기반으로 대규모 사용자 대상의 상호 참여적인 MOOC(Massive Open Online Courses)과 같은 온라인 교육 환경이 부상하고 있다. 그러나 온라인 교육 시스템은 원거리로 학습이 이루어짐에 따라 학습자의 자발적 동기를 꾸준히 유지하기 어려우며, 또한 학습자 간에 지식을 공유하고 공유한 지식을 활용하는 기능이 부족하다. 이러한 문제를 극복하기 위해 구성주의적 학습이론과 집단지성에 기반하여 학습자가 보유한 학습자료를 공유하고 개인화된 학습자료 추천을 받을 수 있는 학습자료 북마킹 서비스인 WeStudy를 구현하였다. 위키피디아(Wikipedia), 슬라이드쉐어 (SlideShare), 비디오렉쳐스 (VideoLectures) 등 현존하는 집단지성 기반 서비스들의 주요 기능으로부터 필요한 집단지성 기능들을 검토하였으며, 본 서비스의 주요 기능으로 1) 리스트 및 그래프 형태의 학습자료 리스트 시각화, 2) 개인화된 학습자료 추천, 3) 보다 상세한 학습자료 추천을 위한 관심 학습자 지정 등을 도출하여 시스템을 설계하였다. 이후, 웹 기반으로 구현된 세 가지 주요기능 별로 개량된 휴리스틱 사용성 평가 방법을 통해 개발된 시스템의 사용성 평가를 실시하였다. 10명의 HCI 분야 전공자 및 현업 종사자를 대상으로 정량적 및 정성적인 평가 결과, 세 가지의 주요 기능에서 전반적으로 사용성이 우수한 것으로 판정되었다. 주요 기능 별 정성적인 평가에서 도출된 여러 마이너 이슈들을 반영할 필요가 있으며, 향후 대규모 사용자를 대상으로 본 서비스를 보급하고 이용할 수 있도록 제공하여 자발적인 지식 공유 환경을 조성할 수 있을 것으로 전망된다. Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

Data Mining on Social Interaction Networks

Social media and social networks have already woven themselves into the very fabric of everyday life. This results in a dramatic increase of social data capturing various relations between the users and their associated artifacts, both in online networks and the real world using ubiquitous devices. In this work, we consider social interaction networks from a data mining perspective - also with a special focus on real-world face-to-face contact networks: We combine data mining and social network analysis techniques for examining the networks in order to improve our understanding of the data, the modeled behavior, and its underlying emergent processes. Furthermore, we adapt, extend and apply known predictive data mining algorithms on social interaction networks. Additionally, we present novel methods for descriptive data mining for uncovering and extracting relations and patterns for hypothesis generation and exploration, in order to provide characteristic information about the data and networks. The presented approaches and methods aim at extracting valuable knowledge for enhancing the understanding of the respective data, and for supporting the users of the respective systems. We consider data from several social systems, like the social bookmarking system BibSonomy, the social resource sharing system flickr, and ubiquitous social systems: Specifically, we focus on data from the social conference guidance system Conferator and the social group interaction system MyGroup. This work first gives a short introduction into social interaction networks, before we describe several analysis results in the context of online social networks and real-world face-to-face contact networks. Next, we present predictive data mining methods, i.e., for localization, recommendation and link prediction. After that, we present novel descriptive data mining methods for mining communities and patterns.

Elements of social representation theory incollaborative tagging systems

This article discusses the information representation process based on the Moscovici's Social Representation Theory and domain analysis in Information Science. The aim was to identify mechanisms and constituent dimensions of social representation in collaborative tagging systems/social bookmarking systems. Scientific knowledge was defined as the object/phenomenon of representation in these systems; and the tag as the shareable structure of meaning that connects participants and resources. The empirical research involved descriptive statistical techniques applied to a corpora of tags available in CiteULike, which is a social tagging system developed for the academic community. The data analysis, performed in a sample of groups derived from the dataset, showed that the users' reuse of their own tags resembles the anchorage mechanism. The reuse of tags by other participants - in the same group - reveals some evidence of the objectification mechanism. Some speculation arose about the cognitive effort made by the individual, under group influence, with regard to the tagging activity, user's choice of resources, and sharing styles. Further studies on social bookmarking systems depend both on a "gain scale" of users and items tagged, requiring techniques and procedures redesigned by Information Science, Statistics, Network Analysis, Linguistics/Sociolinguistics and Social Psychology.

Recommending Tags for New Resources in Social Bookmarking Systems

Recommending Tags for New Resources in Social Bookmarking Systems

Is There Online Collective Identity within Social Bookmarking Systems?

Social tagging systems attract researchers in information systems and social sciences because they offer an enormous quantity of user-generated annotations that reveal the interest of millions of people. In this paper, we develop two hypotheses around the idea of online collective identity. We present a model in which agents exchange practical and symbolic resources via opinion and frame networks. The results discussed in the paper come from the analysis of a sample of a sample of 3,668 users, 2,148 URLs and 4,776 tags from the Delicious classification system on the subject of globalization of agriculture. Our social network analysis has identified differences between networks that reflect different degrees of explicitness within the Delicious online community and it has provided an understanding of social bookmarking systems that was not previously available. This research is one of the first to apply information to the frame components in an attempt to establish the implicit links within these networks.

Academic social bookmarking: An empirical analysis of Connotea users

Three groups of social bookmarking users, namely experienced users, users with moderate level of experience, and novice users, were investigated, in terms of their reported tagging behaviors, perceived usefulness of social bookmarking in information discovery and management, and perceived usefulness of the bookmarking features provided. Based on the empirical analysis of the Connotea users, who are primarily in academia, the study shows that experienced academic users generally prefer to use social bookmarking while moderately experienced and novice users still prefer to use the traditional bookmarking methods, such as creating and using bookmarks on a dedicated computer. Experienced academic users were also found to create more tags per bookmark comparing to the other two groups. Most novice academic users, however, only created one tag per bookmark, which just met Connotea's minimum requirement. Surprisingly different from the collaborative design nature of the social bookmarking systems, the study finds that our participants, particularly experienced academic users, prefer to create and use their own bookmarks rather than sharing bookmarks created by others. In fact, experienced users have significantly higher frequency (once every two weeks) of creating bookmarks than the other two groups (once a month or less). In addition, website design features and functions, such as automatic collection of bibliographic information, are regarded by all participants as helpful for information discovery. The in-depth examination and discussion of the opinions of Connotea users may be useful for further improvement of the design features and usage applications, particularly for academic social bookmarking websites. The results may also have potential implications to the future development of social bookmarking services in general.

Genre Tag . . . Tag Genre

This paper examines genre-related tags through a social bookmarking system CiteULike. Three types of genre-related tags were identified: “is” tag indicating a genre,“for” tag for creating another genre, and “about” tag dealing with a genretopic. An analysis of the frequency and functions of genre-related tags will enable us better understand tagging as well as genre.