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61
Evaluating Similarity Measures for Emergent Semantics of Social Tagging
"... Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, ..."
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Cited by 71 (8 self)
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Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. We also investigate the issue of scalability. We find that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.
Folks in Folksonomies: Social Link Prediction from Shared Metadata
"... Web 2.0 applications have attracted a considerable amount of attention because their open-ended nature allows users to create lightweight semantic scaffolding to organize and share content. To date, the interplay of the social and semantic components of social media has been only partially explored. ..."
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Cited by 53 (5 self)
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Web 2.0 applications have attracted a considerable amount of attention because their open-ended nature allows users to create lightweight semantic scaffolding to organize and share content. To date, the interplay of the social and semantic components of social media has been only partially explored. Here we focus on Flickr and Last.fm, two social media systems in which we can relate the tagging activity of the users with an explicit representation of their social network. We show that a substantial level of local lexical and topical alignment is observable among users who lie close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local alignment between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. This analysis suggests that users with
Stop Thinking, Start Tagging: Tag Semantics Emerge from Collaborative Verbosity
"... Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the ..."
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Cited by 31 (11 self)
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Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with
Social spam detection
- In Proc. of AIRWeb
, 2009
"... The popularity of social bookmarking sites has made them prime targets for spammers. Many of these systems require an administrator’s time and energy to manually filter or remove spam. Here we discuss the motivations of social spam, and present a study of automatic detection of spammers in a social ..."
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Cited by 25 (0 self)
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The popularity of social bookmarking sites has made them prime targets for spammers. Many of these systems require an administrator’s time and energy to manually filter or remove spam. Here we discuss the motivations of social spam, and present a study of automatic detection of spammers in a social tagging system. We identify and analyze six distinct features that address various properties of social spam, finding that each of these features provides for a helpful signal to discriminate spammers from legitimate users. These features are then used in various machine learning algorithms for classification, achieving over 98 % accuracy in detecting social spammers with 2 % false positives. These promising results provide a new baseline for future efforts on social spam. We make our dataset publicly available to the research community.
Friendship prediction and homophily in social media
- ACM Transactions on the Web
"... Web 2.0 applications have attracted considerable attention because their open-ended nature allows users to create lightweight semantic scaffolding to organize and share content. To date, the interplay of the social and topical components of social media has been only partially explored. Here we stud ..."
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Cited by 18 (4 self)
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Web 2.0 applications have attracted considerable attention because their open-ended nature allows users to create lightweight semantic scaffolding to organize and share content. To date, the interplay of the social and topical components of social media has been only partially explored. Here we study the presence of homophily in three systems that combine tagging of social media with online social networks. We find a substantial level of topical similarity among users who lie close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local similarity between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. This analysis suggests that users with similar interests are more likely to be friends, and therefore topical similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this hypothesis on several datasets, confirming that social networks constructed from topical similarity capture actual friendship accurately.
Pragmatic Evaluation of Folksonomies
"... Recently, a number of algorithms have been proposed to obtain hierarchical structures — so-called folksonomies — from social tagging data. Work on these algorithms is in part driven by a belief that folksonomies are useful for tasks such as: (a) Navigating social tagging systems and (b) Acquiring se ..."
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Cited by 17 (11 self)
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Recently, a number of algorithms have been proposed to obtain hierarchical structures — so-called folksonomies — from social tagging data. Work on these algorithms is in part driven by a belief that folksonomies are useful for tasks such as: (a) Navigating social tagging systems and (b) Acquiring semantic relationships between tags. While the promises and pitfalls of the latter have been studied to some extent, we know very little about the extent to which folksonomies are pragmatically useful for navigating social tagging systems. This paper sets out to address this gap by presenting and applying a pragmatic framework for evaluating folksonomies. We model exploratory navigation of a tagging system as decentralized search on a network of tags. Evaluation is based on the fact that the performance of a decentralized search algorithm depends on the quality of the background knowledge used. The key idea of
Making sense of Twitter
- Volume 6496 of LNCS
, 2010
"... Abstract. Twitter enjoys enormous popularity as a micro-blogging ser-vice largely due to its simplicity. On the downside, there is little organi-zation to the Twitterverse and making sense of the stream of messages passing through the system has become a significant challenge for every-one involved. ..."
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Cited by 17 (0 self)
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Abstract. Twitter enjoys enormous popularity as a micro-blogging ser-vice largely due to its simplicity. On the downside, there is little organi-zation to the Twitterverse and making sense of the stream of messages passing through the system has become a significant challenge for every-one involved. As a solution, Twitter users have adopted the convention of adding a hash at the beginning of a word to turn it into a hashtag. Hash-tags have become the means in Twitter to create threads of conversation and to build communities around particular interests. In this paper, we take a first look at whether hashtags behave as strong identifiers, and thus whether they could serve as identifiers for the Se-mantic Web. We introduce some metrics that can help identify hashtags that show the desirable characteristics of strong identifiers. We look at the various ways in which hashtags are used, and show through evalu-ation that our metrics can be applied to detect hashtags that represent real world entities. 1
A personalized tag-based recommendation in social web systems,” in Proc. of the Workshop on Adaptation and Personalization for Web 2.0, in connection with the first and
- Seventeenth International Conference User Modeling, Adaptation, and Personalization, 2009
, 2008
"... Abstract. Tagging activity has been recently identified as a potential source of knowledge about personal interests, preferences, goals, and other attributes known from user models. Tags themselves can be therefore used for finding personalized recommendations of items. In this paper, we present a t ..."
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Cited by 10 (1 self)
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Abstract. Tagging activity has been recently identified as a potential source of knowledge about personal interests, preferences, goals, and other attributes known from user models. Tags themselves can be therefore used for finding personalized recommendations of items. In this paper, we present a tag-based recommender system which suggests similar Web pages based on the similarity of their tags from a Web 2.0 tagging application. The proposed approach extends the basic similarity calculus with external factors such as tag popularity, tag representativeness and the affinity between user and tag. In order to study and evaluate the recommender system, we have conducted an experiment involving 38 people from 12 countries using data from Del.icio.us, a social bookmarking web system on which users can share their personal bookmarks.
Evaluation of folksonomy induction algorithms. under
, 2012
"... Algorithms for constructing hierarchical structures from user-generated metadata have caught the interest of the academic community in recent years. In social tagging systems, the output of these algorithms is usually referred to as folksonomies (from folk-generated taxonomies). Evaluation of folkso ..."
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Cited by 10 (5 self)
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Algorithms for constructing hierarchical structures from user-generated metadata have caught the interest of the academic community in recent years. In social tagging systems, the output of these algorithms is usually referred to as folksonomies (from folk-generated taxonomies). Evaluation of folksonomies and folksonomy induction algorithms is a challenging issue complicated by the lack of golden standards, lack of comprehensive methods and tools as well as a lack of research and empirical/simulation studies applying these methods. In this paper, we report results from a broad comparative study of state-of-the-art folksonomy induction algorithms that we have applied and evaluated in the context of five social tagging systems. In addition to adopting semantic evaluation techniques, we present and adopt a new technique that can be used to evaluate the usefulness of folksonomies for navigation. Our work sheds new light on the properties and characteristics of state-of-the-art folksonomy induction algorithms and introduces a new pragmatic approach to folksonomy evaluation, while at the same time identifying some important limitations and challenges of folksonomy evaluation. Our results show that folksonomy induction algorithms specifically developed to capture intuitions of social tagging systems outperform traditional hierarchical clustering techniques. To the best of our knowledge, this work represents the largest and most comprehensive evaluation study of
