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Why do Users Tag? Detecting Users' Motivation for Tagging in Social Tagging Systems.
... Although partially successful, these approaches often suffer on the missing motivation of the user for annotations [FSCG07,LZMT15]. However, recent studies on this topic show that user's motivation to annotate resources increases if this provides a navigational aid to the resources [SKK10]. Ricci et al [RN07], for instance, present a recommender system to help the user with searching for travel products. ...
... The process of annotating can be considered as an encoding process where the annotations encode the information (facts, features etc.) about the items [SKK10]. However, it depends on the encoding quality of the used annotation type (tags, titles, Q&As) how good a recommender performs. ...
... The obtained results finally provide insight into how effective the tags are at encoding documents. Strohmaier et al. [SKK10] use conditional entropy and orphan ratio for measuring and detecting the tacit nature of tagging motivation by analyzing the tag sets produces by 8 different tagging systems regarding to their encoding and descriptive power. The results of their study show that (i) tagging motivation of individuals varies within and across tagging systems and (ii) user's motivation for tagging has an influence on produced tags and folksonomies. ...
... Although partially successful, these approaches often su er on the missing motivation of the user for annotations [5,13]. However, recent studies on this topic show that user's motivation to annotate resources increases if this provides a navigational aid to the resources [25]. Ricci et al [22], for instance, present a recommender system to help user with searching for travel products. ...
... The process of annotating can be considered as an encoding process where the annotations encode the information (facts, features etc.) about the items [25]. However, it depends on the encoding quality of the used annotation type (tags, titles, Q&As) how good a recommender performs. ...
... The obtained results nally provide insight into how e ective the tags are at encoding documents. Strohmaier et al. [25] use conditional entropy and orphan ratio for measuring and detecting the tacit nature of tagging motivation by analyzing the tag sets produces by 8 di erent tagging systems regarding to their encoding and descriptive power. The results of their study show that (i) tagging motivation of individuals varies within and across tagging systems, and (ii) user's motivation for tagging has an in uence on produced tags and folksonomies. ...
In today's digital age with an increasing number of websites, so-cial/learning platforms, and diierent computer-mediated communication systems, nding valuable information is a challenging and tedious task, regardless from which discipline a person is. However , visualizations have shown to be eeective in dealing with huge datasets: because they are grounded on visual cognition, people understand them and can naturally perform visual operations such as clustering, ltering and comparing quantities. But, creating appropriate visual representations of data is also challenging: it requires domain knowledge, understanding of the data, and knowledge about task and user preferences. To tackle this issue, we have developed a recommender system that generates visualiza-tions based on (i) a set of visual cognition rules/guidelines, and (ii) lters a subset considering user preferences. A user places interests on several aspects of a visualization, the task or problem it helps to solve, the operations it permits, or the features of the dataset it represents. This paper concentrates on characterizing user preferences, in particular: i) the sources of information used to describe the visualizations, the content descriptors respectively, and ii) the methods to produce the most suitable recommendations thereby. We consider three sources corresponding to diierent aspects of interest: a title that describes the chart, a question that can be answered with the chart (and the answer), and a collection of tags describing features of the chart. We investigate user-provided input based on these sources collected with a crowd-sourced study. Firstly, information-theoretic measures are applied to each source to determine the eeciency of the input in describing user preferences and visualization contents (user and item models). Secondly, the practicability of each input is evaluated with content-based recom-mender system. The overall methodology and results contribute methods for design and analysis of visual recommender systems. The ndings in this paper highlight the inputs which can (i) effectively encode the content of the visualizations and user's visual preferences/interest, and (ii) are more valuable for recommending personalized visualizations.
... Representative examples include social bookmarking platforms (e.g., Delicious, Tumblr, reddit) and content curation platforms (e.g., Pinterest, Last.fm). In these platforms, users build and manage a collection of content and search, discover, and share content using social tags (e.g., Ames and Naaman 2007;Gilbert et al. 2013;Strohmaier, Körner, and Kern 2010). Thus, the tags associated with brands/products in these platforms provide insights into (1) how online users interpret and perceive content associated with brands/products, (2) how a brand is grouped together with competing brands, and (3) how potential customers construct the consideration set. ...
... In addition, geotags Harvesting Brand Information / 3 that reveal geolocation information of posts on social media platforms provide geographical details of user experiences related to a brand. From a user perspective, the key motivations for social tagging fall into two categories: content classification and content description (see also Strohmaier, Körner, and Kern 2010). Researchers have found that people create different types of keywords depending on their motivations for tagging. ...
... Researchers have found that people create different types of keywords depending on their motivations for tagging. For instance, when people intend to categorize content, they are more likely to use high-level attributes as tags; yet when people intend to describe content, they are more likely to use contextual attributes as tags (Strohmaier, Körner, and Kern 2010). In addition, each type of motivation could be driven by self-oriented needs (e.g., organization of content for one's own reference), social communication (e.g., information sharing and opinion expression regarding the content with other users), or a combination of both (see also Ames and Naaman 2007). ...
Social tags are user-defined keywords associated with online content and reflect consumers' perceptions of various objects, including products and brands. This research presents a new approach for harvesting rich, qualitative information on brands from user-generated social tags. We first compare and contrast our proposed approach with conventional techniques such as brand concept maps and textmining. We highlight the added value of our approach specifically due to the unconstrained, open-ended and synoptic nature of consumer generated content contained within social tags. We then apply existing textmining and data reduction methods to analyze disaggregate-level social tagging data for marketing research, and demonstrate how marketers can utilize the information in social tags by extracting key representative topics, monitoring common dynamic trends, and understanding heterogeneous perceptions of a brand.
... Representative examples include social bookmarking platforms (e.g., Delicious, Tumblr, reddit) and content curation platforms (e.g., Pinterest, Last.fm). In these platforms, users build and manage a collection of content and search, discover, and share content using social tags (e.g., Ames and Naaman 2007;Gilbert et al. 2013;Strohmaier, Körner, and Kern 2010). Thus, the tags associated with brands/products in these platforms provide insights into (1) how online users interpret and perceive content associated with brands/products, (2) how a brand is grouped together with competing brands, and (3) how potential customers construct the consideration set. ...
... In addition, geotags Harvesting Brand Information / 3 that reveal geolocation information of posts on social media platforms provide geographical details of user experiences related to a brand. From a user perspective, the key motivations for social tagging fall into two categories: content classification and content description (see also Strohmaier, Körner, and Kern 2010). Researchers have found that people create different types of keywords depending on their motivations for tagging. ...
... Researchers have found that people create different types of keywords depending on their motivations for tagging. For instance, when people intend to categorize content, they are more likely to use high-level attributes as tags; yet when people intend to describe content, they are more likely to use contextual attributes as tags (Strohmaier, Körner, and Kern 2010). In addition, each type of motivation could be driven by self-oriented needs (e.g., organization of content for one's own reference), social communication (e.g., information sharing and opinion expression regarding the content with other users), or a combination of both (see also Ames and Naaman 2007). ...
This research presents an approach to harvest rich, qualitative information in user-generated social tags. Social tags are user-defined keywords being associated with any content generated online by consumers and reflect their perceptions of various objects, including brands and products. Previous research has shown that social tags contain significant informational value in understanding competitive market structure and predicting firm performance. We build on this work to showcase the unique characteristics of social tags compared to other forms of user generated content, and show how tags can be used to construct perceptual maps. We discuss similarities and complementarities of our approach vis-a-vis conventional techniques such as metaphor elicitation and brand concept maps; as well as more recent text mining techniques. The added value of our approach arises specifically from the unconstrained, open-ended and synoptic nature of consumer generated content contained within social tags. We illustrate how marketers can monitor the information in perceptual brand maps by extracting common dynamic trends, visualize the relative competitive position of their brand, and understand heterogeneous perceptions about a brand by discovering distinct clusters of perceptual maps.
... Lots of different motivations for assigning tags exist. For the sake of the scope of this paper we will only distinguish between two main motivations introduced by Körner et al. [8] [9] and Strohmaier et al. [14]: categorization and description. This paper examines existing heuristics to distinguish the user's motivation behind assigning tags for artworks and evaluates the art-folksonomy collected with the explorAR-Torium. ...
... The design of a tagging interface could only improve the total amount of assigned tags, but it was not possible to change the motivation of the users. This is in accordance with Strohmaier et al., [14] who compared different tagging platforms and argue that the tagging behaviour of users varies within the same platform (and therefore the same tagging environment). As described in Section 3, the users have been able to rate artworks on a scale of zero to five, with zero being the lowest rating and five being the best rating. ...
... The works of Körner et al. [8] [9] and Stromaier et al. [14] distinguish two different kinds of users according to their motivation to assign tags: categorizers who categorize resources and describers who describe them. In this paper we use existing heuristics that distinguish the user's motivation, to evaluate our folksonomy. ...
The perception of art is a subjective affair - being influenced by our feelings, education and cultural background. Contrary, the study of art history uses formal methods to classify artworks. This discrepancy often poses a risk of being insurmountable -- especially for users without prior knowledge of art history. The concept of social tagging provides the possibility to merge art historical information with the subjective perception of users. For our art Web platform explorARTorium, social tags augment exiting art historical information. In order to better understand how social tagging is best applied, it is necessary to examine the user's motivation to assign tags. We adopt the differentiation between users who are motivated by categorizing, and users who are motivated by describing resources. By evaluating our folksonomy according to this paradigm, we show that the preference for certain artworks has an effect on the user's tagging motivation, whereas the presentation of an artwork does not. While measures exist that are able to identify the user's motivation for annotating artworks, we propose an heuristic that aims to classify categorizing, respectively descriptive, tags. After evaluating this proposed heuristic, we show that it is indeed possible to identify categorizing and descriptive tags, even though the results are somewhat biased by the content of the resources and the individual tagging behaviour of the users.
... Koutrika et al. 6 Leone et al. 26 26 Koh et al. 34 To evaluate content representation Torres-Parejo et al. 40 Orbit shape Kerr 65 Others Skoutas and Alrifai 41 Evaluations about design Morville and Rosenfeld 29 Morik et al. 42 Halvey and Keane 28 Hearst and Rosner 25 Bateman et al. 35 Schrammel et al. 32 Lohmann et al. 33 Methods for tag generation Provost 43 Software Greene et al. 20 Astrain et al. 44 Jin 22 Knautz et al. 45 Breedvelt 23 Skoutas and Alrifai 41 Tag utility Strohmaier et al. 46 Cloud computing in big data Sookhak et al. 21 ...
... Without knowing the motivation of users when tagging a resource, it is difficult to predict the usefulness of the tag employed, 46 since the utility of the tag will be different if you want to categorize a resource (to find it later) or describe it (useful in extracting knowledge from folksonomies), so it is useful to be able to distinguish between categorization and description. ...
Tag clouds are tools that have been widely used on the Internet since their conception. The main applications of these textual visualizations are information retrieval, content representation and browsing of the original text from which the tags are generated. Despite the extensive use of tag clouds, their enormous popularity and the amount of research related to different aspects of them, few studies have summarized their most important features when they work as tools for information retrieval and content representation. In this paper we present a summary of the main characteristics of tag clouds found in the literature, such as their different functions, designs and negative aspects. We also present a summary of the most popular metrics used to capture the structural properties of a tag cloud generated from the query results, as well as other measures for evaluating the goodness of the tag cloud when it works as a tool for content representation. The different methods for tagging and the semantic association processes in tag clouds are also considered. Finally we give a list of alternative for visual interfaces, which makes this study a useful first help for researchers who want to study the content representation and information retrieval interfaces in greater depth.
... Many studies have focused on tagging behaviours (Chen and Ke, 2013;De Meo et al., 2013;Farooq et al., 2007;Golbeck et al., 2011;Lin and Chen, 2012;Ruiz and Chin, 2010;Santos-Neto et al., 2009;Vuorikari et al., 2007;Wan et al., 2013) and the motivation behind them (Ames and Naaman, 2007;Elhussein and Nakata, 2012;Gupta et al., 2010;Strohmaier et al., 2010). Numerous tagging behaviour studies examined the general tagging distribution and tagging vocabularies and used the results to describe tagging behaviour. ...
... Several scholars have studied tagging motivation. Strohmaier et al. (2010) measured the ESP game data set (www.cs.cmu.edu/ϳbiglou/resources/) and found that users show different motivations within a tagging system, as well as across different ones, and users agree more with tags describing resources than those categorizing resources. Ames and Naaman (2007) built a taxonomy of tagging motivations for Flickr and ZoneTag in two dimensions -function (organization and communication) and sociality (self, friends/families and public). ...
Purpose
In the era of social media, users all over the world annotate books with social tags to express their preferences and interests. The purpose of this paper is to explore different tagging behaviours by analysing the book tags in different languages.
Design/methodology/approach
This investigation collected nearly 56,000 tags of 1,200 books from one Chinese and two English online bookmarking systems; it combined content analysis and machine-processing methods to evaluate the similarities and differences between different tagging systems from a cross-lingual perspective. Jaccard’s coefficient was adopted to evaluate the similarity level.
Findings
The results show that the similarity between mono-lingual tags of the same books is higher than that of cross-lingual tags in different systems and the similarity between tags of books written for specialties is higher than that of books written for the general public.
Research limitations/implications
Those who have more in common annotate books with more similar tags. The similarity between users in tagging systems determines the similarity of the tag sets.
Practical implications
The results and conclusion of this study will benefit users’ cross-lingual information retrieval and cross-lingual book recommendation for online bookmarking systems.
Originality/value
This study may be one of the first to compare cross-lingual tags. Its methodology can be applied to tag comparison between any two languages. The insights of this study will help develop cross-lingual tagging systems and improve information retrieval.
... Consequently it creates associations with the structure 'user -tag -item'. Users tag in order to categorize and describe resources to maintain navigational aid to resources for later browsing or retrieval (Strohmaier, et al., 2010). ...
... Both groups add mainly descriptive tags. While Schmidt and Stock (2009) discovered more attitudinal tags and Strohmaier, et al. (2010) more selfreferencing tags, these two types of tags were virtually non-existent in our experiment. Hypothesis 3 cannot be rejected: laymen use different words to experts, but not entirely different. ...
This paper focuses on the use of online social tagging and storytelling to enrich digital collections of cultural heritage. Together with several Dutch museums, we examined the question of whether and how social tagging could benefit these museums in disclosing specific digital collections. This led to the development of a social tagging tool (www.ikweetwatditis.nl) as a means of researching behaviour when tagging cultural objects. The results show that tagging and storytelling can help museums enrich their collections and involve their audiences.
... Underlying the questions about folksonomy creation is the fundamental issue of motivation -why do users contribute to social tagging systems? A substantial literature has explored this topic in terms of why users tag in one manner rather than another (Nov and Ye, 2010;Ames and Naaman, 2007;Strohmaier et al., 2010), but there is little work addressing what motivates some users to tag so much more than others. By comparing the tagging patterns of supertaggers to other users, we contribute to an understanding of what differentiates heavy contributors from their low-tagging counterparts in social tagging and what motivational factors may distinguish these two groups. ...
... In contrast, describers do not constrain their vocabulary; instead, they freely use a variety of informative keywords to describe items, facilitating later keywordbased search. Strohmaier et al. (2010) and Körner et al. (2010a) present several metrics with which to classify users according to this dichotomy, discussed in Section 6. Other researchers have developed taxonomies of tagging motivation that can be broadly mapped onto dimensions of sociality (are tags self-or socially-directed?) and function (are tags used for organization or communication?) (Ames and Naaman, 2007;Heckner et al., 2009). ...
A folksonomy is ostensibly an information structure built up by the "wisdom of the crowd", but is the "crowd" really doing the work? Tagging is in fact a sharply skewed process in which a small minority of "supertagger" users generate an overwhelming majority of the annotations. Using data from three large-scale social tagging platforms, we explore (a) how to best quantify the imbalance in tagging behavior and formally define a supertagger, (b) how supertaggers differ from other users in their tagging patterns, and (c) if effects of motivation and expertise inform our understanding of what makes a supertagger. Our results indicate that such prolific users not only tag more than their counterparts, but in quantifiably different ways. These findings suggest that we should question the extent to which folkosonomies achieve crowdsourced classification via the "wisdom of the crowd", especially for broad
folksonomies like Last.fm as opposed to narrow folksonomies like Flickr.
... Underlying questions about folksonomy creation is the fundamental issue of motivation -why do users contribute to social tagging systems? A substantial literature has explored this topic in terms of why users tag in one manner rather than another [16,1,19], but there is little work addressing the question of why users choose to participate in the tagging process to begin with. By comparing the tagging patterns of the minority of prolific taggers to the majority of non-prolific taggers, here we contribute to an understanding of what differentiates the heavy contributors from their lowtagging counterparts in social tagging, what motivational factors distinguish these two groups, and whether their tags reflect different underlying folksonomies. ...
... In contrast, describers do not constrain their vocabulary; instead they freely choose a variety of informative keywords to describe items. Strohmaier et al. [19] and Körner et al. [9] present several metrics with which to categorize users according to this dichotomy, discussed in Section 6.2. ...
A folksonomy is ostensibly an information structure built up by the "wisdom of the crowds", but is the "crowd" re-ally doing the work? Tagging is in fact a sharply skewed process in which a small minority of users generate an over-whelming majority of the annotations. Using data from the social music site Last.fm as a case study, this paper explores the implications of this tagging imbalance. Partitioning the folksonomy into two halves — one created by the prolific minority and the other by the non-prolific majority of tag-gers — we examine the large-scale differences in these two sub-folksonomies and the users generating them, and then explore several possible accounts of what might be driving these differences. We find that prolific taggers preferentially annotate content in the long-tail of less popular items, use tags with higher information content, and show greater tag-ging expertise. These results indicate that "supertaggers" not only tag more than their counterparts, but in quantifi-ably different ways.
... Este comportamento se assemelha ao relatado no trabalho de Strohmaier et. al [SKK10], onde é descrita a existência de motivações diferentes entre os usuários para o modo como utilizam as tags. Foram observados dois tipos de usuários, chamados categorizadores e descritores. ...
... Dentre as tags únicas encontradas, observa-se problemas relacionados a erros de tipografia, tags compostas, erros de inclusão e também à utilização de tags com motivo navegacional mencionadas em [SKK10]. Por exemplo, erros de digitação como "minneasota" (minnesota) e "manhatten" (manhattan), podem ser facilmente contornados com a implementação de uma verificação de palavras similares como já mencionado no capítulo anterior. ...
... Social tagging systems such as Delicious, BibSonomy or Flickr have attracted the interest of our research community for almost a decade [18,12]. Significant advances have been made with regard to our understanding about the emergent, individual and collective processes that can be observed in such systems [26]. Useful algorithms for retrieval [14] and classification [31] have been developed that exploit the rich fabric of links between users, resources, and tags in social tagging systems for facilitating information organization, search and navigation. ...
... Using the post data of tagging systems, several studies analyzed aspects of posting behavior, e.g., the distributions of users, resources, and tags in posts [7], or the identification of different types of users -categorizers and describersregarding their choice of tags [26]. However, these studies did not use log data for their analysis to explore the actual retrieval behavior. ...
Social tagging systems have established themselves as an important part in
today's web and have attracted the interest from our research community in a
variety of investigations. The overall vision of our community is that simply
through interactions with the system, i.e., through tagging and sharing of
resources, users would contribute to building useful semantic structures as
well as resource indexes using uncontrolled vocabulary not only due to the
easy-to-use mechanics. Henceforth, a variety of assumptions about social
tagging systems have emerged, yet testing them has been difficult due to the
absence of suitable data. In this work we thoroughly investigate three
available assumptions - e.g., is a tagging system really social? - by examining
live log data gathered from the real-world public social tagging system
BibSonomy. Our empirical results indicate that while some of these assumptions
hold to a certain extent, other assumptions need to be reflected and viewed in
a very critical light. Our observations have implications for the design of
future search and other algorithms to better reflect the actual user behavior.
... The user population of social annotation systems and the behavior we can observe in such systems varies broadly. For example, in previous work we found that different types of tagging systems lend themselves naturally to different kinds of tagging motivation [15] . In the following we present an overview of various types of measures for detecting and characterizing different kinds of tagging pragmatics, i. e., different types of users and user behavior in social annotation systems. ...
... The notion of categorizers and describers was initially presented by Strohmaier et al. in [15] and further elaborated in [16] by introducing and evaluating different measures for tagging motivation. In this previous work, we found that a useful and valid measure for distinguishing between these two types of users is the tag/resource ratio. ...
The presence of emergent semantics in social annotation systems has been reported in numerous studies. Two important problems in this context are the induction of semantic relations among tags and the discovery of different senses of a given tag. While a number of approaches for discovering tag senses exist, little is known about which factors influence the discovery process. In this paper, we analyze the influence of user pragmatic factors. We divide taggers into different pragmatic distinctions. Based on these distinctions, we identify subsets of users whose annotations allow for a more precise and complete discovery of tag senses. Our results provide evidence for a link between tagging pragmatics and semantics and provide another argument for including pragmatic factors in semantic extraction methods. Our work is relevant for improving search, retrieval and browsing in social annotation systems, as well as for optimizing ontology learning algorithms based on tagging data.
... Stattdessen ist der Anteil an deskriptiven Tags in einer Folksonomy stark von der Motivation der Nutzenden abhängig, d. h. ob sie eher describers oder categorizers sind (Strohmaier et al. 2010 (Monnin et al. 2010). Solche Tag-Variationen verdeutlichen den starken Zusammenhang zwischen Tag-Nutzung und Funktionalitäten des Tagging-Systems: Weil die Tagging-Systeme nur bestimmte Funktionalitäten anbieten, die Nutzerschaft aber weitere spezielle kommunikative Bedürfnisse hat, werden bestehende Funktionalitäten entsprechend umgedeutet und (Hash-) Tags zum Füllen der Lücke kreativ genutzt. ...
... The first group focuses on empirical studies of tagging content and tagging behaviors. For instance, research has been conducted to find out the motivation for social tagging Strohmaier et al., 2010), tagging roles (Thom-Santelli et al., 2008), and the dynamics and consensus of collaborative tagging (Halpin et al., 2007;Robu et al., 2009). The second group investigates the possible usage and functions of social tags. ...
Tagging is a defining characteristic of Web 2.0. It allows users of social computing systems (e.g., question and answering (Q&A) sites) to use free terms to annotate content. However, is tagging really a free action? Existing work has shown that users can develop implicit consensus about what tags best describe the content in an online community. However, there has been no work studying the regularities in how users order tags during tagging. In this paper, we focus on the natural ordering of tags in domain-specific Q&A sites. We study tag sequences of millions of questions in four Q&A sites, i.e., CodeProject, SegmentFault, Biostars, and CareerCup. Our results show that users of these Q&A sites can develop implicit consensus about in which order they should assign tags to questions. We study the relationships between tags that can explain the emergence of natural ordering of tags. Our study opens the path to improve existing tag recommendation and Q&A site navigation by leveraging the natural ordering of tags.
... For further tag history, tagging process, and accessing information with tags, interested readers may refer to Gupta's overview article [18] and Bullock et al.'s chapter [19] in the newly published social information access book [20]. While scholars have devoted efforts on investigating the social tagging process [21] and tagging motivation [22], [23], using tags to organize content has also caught scholars' attention from various aspects, such as tag ranking and selection [5], [24]- [26], tag recommendation [27]- [29], tag cloud construction [26], [30]- [33], tag visualization [34], [35] and tag applications to improve search and navigation [5], [15], [36]- [38]. Tag clouds have become one of the most common approaches to describe content, organize given resources and further enhance resource search, navigation, and recommendations [5], [39]. ...
Tag clouds have been utilized as a “social” way to find and visualize information, providing both one-click access and a snapshot of the “aboutness” of a tagged collection. While many research projects have explored and compared various tag artifacts using information theory and simulations, fewer studies have been conducted to compare the effectiveness of different tag-based browsing interfaces from the user’s point of view. This research aims to investigate how users utilize tags in image search context and to what extent different organizations of tag browsing interfaces are useful for image search. We conducted two experiments to explore user behavior and performance with three interfaces: two tag-enabled interfaces (the regular and faceted tag-clouds) and a baseline (search-only) interface. Our results demonstrate the value of tags in the image search context, the role of tags in the exploratory search, and the strengths of two kinds of tag organization explored in this paper.
... On social tag platforms, consumers describe, organize, and categorize the content of the website (such as text, pictures, audio, or video). They may categorize content abstractly with high-level attributive words or describe content semantically with more contextual attributive words [36]. As illustrated, people may categorize motor-relative content as "car" or "motor" and may also describe the web content of their interest by "interesting". ...
With the explosion of social media, consumers’ minds have become important assets in brand competitions. Determining a brand’s competitive structure based on consumers’ desires is particularly important to effectively establish a brand and maintain sustainable competitiveness. The traditional methods of determining brand competitiveness are costly and time-consuming. In this study, we propose an efficient, systematical, highly automated, and real-time method to determine brand competitiveness based on consumers’ brand associations with the brand’s social tags. Using a set of 45 brands in the automobile industry and around 50,000 social tags, we compared our brand competitiveness determination method with data provided by Interbrand and directly elicited survey data, finding a significant correlation and a better predictive power in consumers’ perceived brand competitiveness than the traditional method. Our proposed method enables managers to create and maintain sustainable brand advantages in consumers’ minds.
... They are used to categorize and describe any possible object (e.g., photos, comments, and bookmarks). Not only does this enable a user to find the object later, but it also allows every other user to find objects in which he or she is interested (Strohmaier, Körner, & Kern, 2010). Accordingly, users can label content about a brand (e.g., Adidas) by using the brand name as a tag (e.g., #adidas). ...
The increasing use of social media services has led to an enormous amount of content being shared every day. Brand-related user-generated content offers huge opportunities for learning what consumers currently think and feel about brands. Against this back-ground, this paper presents an automatic approach for collecting, aggregating, and visu-alizing brand-related user-generated content. Using data from the social network Insta-gram, brand perceptions are visualized in the form of associative networks. To the best of our knowledge, this is the first approach combining textual and tagging data, as well as network and sentiment analysis of user-generated content, from Instagram. We demonstrate the usefulness of our approach by deriving meaningful insights for brand managers from two brand networks. The approach enables easy and quickly accessible real-time monitoring of brands and, therefore, provides new possibilities for brand management and research.
... Yet, tagging is not just a matter of preference, it is also a task performed by users that involves the language (Stiller et al. 2011) chosen for tagging and the content (Klavans et al. 2014) being tagged. Even though tags were created to help the content description/classification task, users have distinct motivation (Strohmaier et al. 2010) for tagging and, consequently, new types of tags have emerged (Manish et al. 2010). Some researchers have reported users' distinct motivations for tagging as self-presentation, opinion expression, social signaling, among others. ...
Tagging has the potential to improve information retrieval. Although its primary purpose is description and categorization of content, the tagging task has evolved and now users express more than the explicit content presented on the resources being tagged. However, tagging is a repetitive work and, due to this fact, tag recommender systems were developed to support this task by suggesting tags based on similarity. Although these types of systems improve the quality and homogeneity of tags, users have distinct criteria, motivation and style for tagging. In this work we conducted two studies to investigate how users express themselves using tags on images. Results show that tags’ language and structure are a matter of personal style and users want to express the context that an image represents through tags. Despite the fact that users want to express themselves by using personal tags, when recommendation is involved they change their tagging style and assume the collective tag expression.
... People tag their images with various motivations while using social media [2,13,15]. It not only allows them to organize images but also makes it possible for other users to locate images by communicating contextual information [2]. ...
Instagram, a popular global mobile photo-sharing platform, involves various user interactions centered on posting images accompanied by hashtags. Participatory hashtagging, one of these diverse tagging practices, has great potential to be a communication channel for various organizations and corporations that would like to interact with users on social media. In this paper, we aim to characterize participatory hashtagging behaviors on Instagram by conducting a case study of its representative hashtagging practice, the Weekend Hashtag Project, or #WHP. By conducting a user study using both quantitative and qualitative methods, we analyzed the way Instagram users respond to participation calls and identified factors that motivate users to take part in the project. Based on these findings, we provide design strategies for any interested parties to interact with users on social media.
... Authors concluded that all the measures are not equally helpful but tag/resource ratio appears to give good results in capturing human judgement. Strohmaier et al. (2009) worked in the same direction, experimentally evaluated eight data sets and found out that tag agreement between users who are motivated by categorizing resources is less as compared with users who are motivated by describing resources. ...
Folksonomy gives liberty to its users to freely assign chosen keywords as tags, and this is the main reason behind its popularity. Apart from freedom, this system also reflects the collective intelligence of the crowd. However, this freedom and liberty can degrade quality of the folksonomy. It is required that quality of the folksonomy must remain consistently excellent and does not degrade with the passage of time. This is a survey paper, in which we present a brief survey of the research efforts intended to maintain a quality-protected folksonomy. We have organized our paper by looking at the problem from four aspects namely selection of quality tags, tag management features provided by folksonomy applications, folksonomy cleaning and interoperability of tags across platforms. We conclude our review with some of the interesting research topics, which need to be explored further. Our conclusion will be relevant and beneficial for engineers and designers who aim to design and maintain a quality-protected folksonomy.
... There has been substantial analysis as to why people provide tags. Some researchers, (e.g., Strohmaier, Körner, and Kern 2010) have suggested there are two general reasons why people tag: to either categorize or describe resources. However, other researchers (e.g., Ames and Naaman 2007) appear to suggest that people tag for a range of reasons, some based on the particular domain. ...
This paper examines the use of crowdsourcing informal and discretionary tags in accounting and finance. Crowd-provided tags employ short amounts of text to capture some characteristic(s) of documents, messages, financial information, and other objects. Increasingly, tagging is being used in systems for knowledge management, including facilitating search and categorization. The paper reviews previous research on tagging, summarizes accounting applications of crowdsourced tags, investigates problems associated with using crowdsourced tags in accounting, and generates a number of potential research issues. Empirical analysis of Delicious and Twitter data are used to illustrate some of the concepts associated with the emerging technology of crowdsourced tags in accounting and finance. Empirical analysis finds that users provide different tags for the same concept, potentially making it difficult to use tags for search purposes. In addition, users appear to generate redundant tags, with many tags simply capturing object title information.
... For archiving motivations, the tags are more biased on personal opinions and personal terms. On the other hand, taggers who are aware of sharing will have more popular tags which are well known by other people (Strohmaier, M., Korner, C. and Kern, R., 2010). There are 2 factors which influence on taggers: personal tendency and community influence: ...
... Users employ a tagging system to organize the resource (e.g., articles, photos, images). When users intend to categorize content, they more commonly use high-level attributes as tags; yet when users intend to describe content, they use more semantic, contextual attributes as tags (Strohmaier, Körner, and Kern 2010). It has been shown that tags created for describing content might be more useful for understanding rich interpretations of a document or article than those for categorizing content. ...
Social tagging is a new way to share and categorize online content that enables users to express their thoughts, perceptions, and feelings with respect to diverse concepts. In social tagging, content is connected through user-generated keywords-"tags"-and is readily searchable through these tags. The rich associative information that social tagging provides marketers new opportunities to infer brand associative networks. This article investigates how the information contained in social tags can act as a proxy measure for brand performance and can predict the financial valuation of a firm. Using data collected from a social tagging and bookmarking website, Delicious, the authors examine social tagging data for 44 firms across 14 markets. After controlling for accounting metrics, media citations, and other user-generated content, they find that social tag-based brand management metrics capturing brand familiarity, favorability of associations, and competitive overlaps of brand associations can explain unanticipated stock returns. In addition, they find that in managing brand equity, it is more important for strong brands to enhance category dominance, whereas it is more critical for weak brands to enhance connectedness. These findings suggest a new way for practitioners to track, measure, and manage intangible brand equity; proactively improve brand performance; and influence a firm's financial performance.
... In our own previous work, we have studied the relationship between pragmatics and semantics in the context of social tagging systems. We have found that, for example, the pragmatics of tagging (users' behavior and motivation in social tagging systems [11,6,4]) exert an influence on the usefulness of emergent semantic structures [7]. In social awareness streams, we have shown that different types of Twitter stream aggregations can significantly influence the result of semantic analysis of tweets [12]. ...
Online social media such as wikis, blogs or message boards enable large groups of users to generate and socialize around content. With increasing adoption of such media, the number of users interacting with user-generated content grows and as a result also the amount of pragmatic metadata -i.e. data about the usage of content -grows. The aim of this work is to compare different methods for learning topical user profiles from Social Web data and to explore if and how pragmatic metadata has an effect on the quality of semantic user models. Since accurate topical user profiles are required by many applications such as recommender systems or expert search engines, learning such models by observing content and activities around content is an appealing idea. To the best of our knowledge, this is the first work that demonstrates an effect between pragmatic metadata on one hand, and the quality of semantic user models based on user-generated content on the other. Our results suggest that not all types of pragmatic metadata are equally useful for acquiring accurate semantic user models, and some types of pragmatic metadata can even have detrimental effects.
... In recent years, social tagging systems have emerged as an alternative to traditional forms of organizing information. Instead of enforcing rigid taxonomies with controlled vocabulary, social tagging systems allow users to freely choose so-called tags to annotate resources Strohmaier et al. 2010]. In related research, it has been suggested that social tagging systems can be used to acquire latent hierarchical structures that are rooted in the language and dynamics of the underlying user population Heymann and Garcia-Molina 2006;Cattuto et al. 2008]. ...
... However, the Tweetonomy model presents a more complex and dynamic structure than folksonomies. Strohmaier et al [ 18] and Körner et al [13], study quantitative measures for tagging motivation. In their study they found empirical evidence that the emerging semantics of tags in folksonomies are influenced by individual user tagging practices. ...
Social activity streams provide information both about the user's in-terests and about the way in which they engage with real world entities. Recent research has provided evidence of the presence of emergent semantics in such streams. In this work, we explore whether the online discourse of user's social activities can convey meaningful contextual information. We introduce a user-centric methodology based on tensor analysis for deriving personal vocabularies given an entity-based context. By extracting entities (e.g. location, organisation, people) from the user's stream content, we explore the data structures that emerge from the user's interrelationship with these entities. Our experimental results re-vealed that the simultaneous correlation of entities leads to the identification of concepts which are relevant to the user given a specific context. This methodol-ogy is relevant for mobile application designers (1) in fostering user entity-based ontologies for merging user context in pervasive environments, (2) for personal-ising entity-based recommendations.
... Recent research on social tagging systems has been motivated by a vision that the tagging data produced by Web users can be used for social classification, i.e., the collective classification of resources into a commonly agreed structure [15]. According to Strohmaier et al. [9], users' tagging behaviors can be grouped into two main motivations: Categorizers where users use tags to categorize objects and Describers where users use tags to describe objects. These tags reflect, explicitly or implicitly, the semantics of web objects from users' point of view and reveal the information about an object such as which category it belongs to or what it looks like. ...
This paper studies web object classification problem with the novel exploration of social tags. More and more web objects are increasingly annotated with human interpretable labels (i.e., tags), which can be considered as an auxiliary attribute to assist the object classification. Automatically classifying web objects into manageable semantic categories has long been a fundamental pre-process for indexing, browsing, searching, and mining heterogeneous web objects. However, such heterogeneous web objects often suffer from a lack of easy-extractable and uniform descriptive features. In this paper, we propose a discriminative tag-centric model for web object classification by jointly modeling the objects category labels and their corresponding social tags and un-coding the relevance among social tags. Our approach is based on recent techniques for learning large-scale discriminative models. We conduct experiments to validate our approach using real-life data. The results show the feasibility and good performance of our approach.
... Because they fit well with the social Web's general principle of sharing and participating, crowdsourced tags quickly established themselves as one of the major forces for converting the static Web into a participatory information space (Ding, Jacob, Yan, George, & Guo, 2009). Consequently, there is a considerable amount of work in the literature focusing on various aspects of tags and tagging behaviors, which include tagging motivation (Ames & Naaman, 2007;Nov & Ye, 2010;Strohmaier, Körner, & Kern, 2010), navigation (Chi & Mytkowicz, 2007;Helic, Trattner, Strohmaier, & Andrews, 2010), and search (Bischoff, Firan, Nejdl, & Paiu, 2008;Heymann, Koutrika, & Garcia-Molina, 2008;Trattner, Lin, Parra, & Brusilovsky, 2012). Readers who want to know more about this topic should read Gupta's overview article (Gupta, Li, Yin, & Han, 2011) and Smith's book (Smith, 2007). ...
Crowdsourcing has been emerging to harvest social wisdom from thousands of volunteers to perform series of tasks online. However, little research has been devoted to exploring the impact of various factors such as the content of a resource or crowdsourcing interface design to user tagging behavior. While images’ titles and descriptions are frequently available in image digital libraries, it is not clear whether they should be displayed to crowdworkers engaged in tagging. This paper focuses on offering an insight to the curators of digital image libraries who face this dilemma by examining (i) how descriptions influence the user in his/her tagging behavior and (ii) how this relates to the (a) nature of the tags, (b) the emergent folksonomy, and (c) the findability of the images in the tagging system. We compared two different methods for collecting image tags from Amazon’s Mechanical Turk’s crowdworkers – with and without image descriptions. Several properties of generated tags were examined from different perspectives: diversity, specificity, reusability, quality, similarity, descriptiveness, etc. In addition, the study was carried out to examine the impact of image description on supporting users’ information seeking with a tag cloud interface. The results showed that the properties of tags are affected by the crowdsourcing approach. Tags from the “with description” condition are more diverse and more specific than tags from the “without description” condition, while the latter has a higher tag re-use rate. A user study also revealed that different tag sets provided different support for search. Tags produced “with description” shortened the path to the target results, while tags produced without description increased user success in the search task.
... For further analysis of social tagging, Trant (2009a) and Strohmaier, Körner, and Kern (2010) offer excellent overviews of related literature. ...
In recent years, cultural heritage institutions have increasingly used social tagging. To better understand the nature of these tags, we analyzed tags assigned to a collection of 100 images of art (provided by the steve.museum project) using subject matter categorization. Our results show that the majority of tags describe the people and objects in the image and are generic in nature. This contradicts prior subject matter analyses of queries, tags, and index terms of other image collections, suggesting that the nature of social tags largely depends on the type of collection and on user needs. This insight may help cultural heritage institutions improve their management and use of tags.
... Strohmaier, Korner and Kern [10] described about the reasons of tags assigned by the users. They also explained about the user's inspiration in tagging. ...
Indeed e-learning provides gateway to better educational system for learners. In order to enhance socialization and sharing of knowledge among existing e-learning applications, Web 2.0 based tools can be integrated and utilized in these applications. This research paper is also related to evaluate the significance and compatibility of Web 2.0 tools in education.
... Models of user navigation have been successfully used in a range of related domains. For example, in the domain of tagging systems, navigational models [8] as well as behavioral and psychological theories are exploited to evaluate taxonomic structures [7], to assess the motivation for tagging [25], or to improve the quality of emergent semantics [14] and social classification tasks [29]. While navigational models have been applied to improve or evaluate (unstructured) semantics in these domains, they have not been extensively applied to structured knowledge bases. ...
Knowledge Engineering is a costly, tedious and often time-consuming task, for which light-weight processes are desperately needed. In this paper, we present a new paradigm - Navigation-induced Knowledge Engineering by Example (NKE) - to address this problem by producing structured knowledge as a result of users navigating through an information system. Thereby, NKE aims to reduce the costs associated with knowledge engineering by framing it as navigation. We introduce and define the NKE paradigm and demonstrate it with a proof-of-concept prototype which creates OWL class expressions based on users navigating in a collection of resources. The overall contribution of this paper is twofold: (i) it introduces a novel paradigm for knowledge engineering and (ii) it provides evidence for its technical feasibility.
... There are user-centered techniques that may improve the discovery experience from the user perspective. A recent study (Strohmaier, et al., 2010) differentiates between users who use tags for categorization and those who use tags for description purposes. The first group of users is motivated toward tagging because they want to construct and maintain a navigational aid for the resources being tagged. ...
Presently, solutions for geo-information sharing are mainly based on Web technologies, implementing service-oriented frameworks, and applying open (international or community) standards and interoperability arrangements. Such frameworks take the name of Spatial Data Infrastructures (SDIs). The recent evolution of the World Wide Web (WWW), with the introduction of the Semantic Web and the Web 2.0 concepts and solutions, made available new applications, architectures, and technologies for sharing and linking resources published on the Web. Such new technologies can be conveniently applied to enhance capabilities of present SDIs-in particular, discovery functionality. Different strategies can be adopted in order to enable new ways of searching geospatial resources, leveraging the Semantic Web and Web 2.0 technologies. The authors propose a Discovery Augmentation Methodology which is essentially driven by the idea of enriching the searchable information that is associated with geospatial resources. They describe and discuss three different high-level approaches for discovery augmentation: Provider-based, User-based, and Third-party based. From the analysis of these approaches, the authors suggest that, due to their flexibility and extensibility, the user-based and the third-party based approaches result more appropriate for heterogeneous and changing environments such as the SDI one. For the user-based approach, they describe a conceptual architecture and the main components centered on the integration of user-generated content in SDIs. For the third-party approach, the authors describe an architecture enabling semantics-based searches in SDIs.
... For example, it is not obvious or self-evident how an automatic distinction between motivations such as self-expression [22], enjoyment [17] or social-recognition [11] can be made. For these reasons, this paper focuses on a particularly promising distinction between categorizers and describers, inspired by Coates [12] and Heckner [9] and further refined and discussed in [23]. Several intuitions about this distinction (such as the kinds of tagging styles different types of users would adopt) make it a promising candidate for future investigations. ...
While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users' motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems.
... This is either done for semantic reasons (e.g. to enrich information items with metadata), conversational (e.g. for social signaling) [Ames and Naaman 2007] or for organizational reasons (e.g. to categorize information items) [Körner et al. 2010]. Independent of "why people tag" [Strohmaier et al. 2010b, Strohmaier 2008, tags can be visualized in socalled "tag clouds" (cf. [Ames and Naaman 2007]). ...
... While the paper at hand focuses on network-theoretic aspects, cognitive aspects of navigation have been studied previously using, for example, SNIF-ACT [34] and social information foraging theory [35]. Other work has studied the motivations of users for tagging [36], [37], and how they influence emergent semantic (as opposed to navigational) structures. The navigational utility of single tags has been investigated [38] with somewhat disappointing results. ...
It is a widely held belief among designers of social tagging systems that tag clouds represent a useful tool for navigation. This is evident in, for example, the increasing number of tagging systems offering tag clouds for navigational purposes, which hints towards an implicit assumption that tag clouds support efficient navigation. In this paper, we examine and test this assumption from a network-theoretic perspective, and show that in many cases it does not hold. We first model navigation in tagging systems as a bipartite graph of tags and resources and then simulate the navigation process in such a graph. We use network-theoretic properties to analyse the navigability of three tagging datasets with regard to different user interface restrictions imposed by tag clouds. Our results confirm that tag-resource networks have efficient navigation properties in theory, but they also show that popular user interface decisions (such as "pagination" combined with reverse-chronological listing of resources) significantly impair the potential of tag clouds as a useful tool for navigation. Based on our findings, we identify a number of avenues for further research and the design of novel tag cloud construction algorithms. We also argue that any future algorithm needs to take into account the trade-off between navigational and semantic properties of the generated tag-resource networks. In particular, we introduce a simple method for estimating a so-called semantic penalty induced by a given tag-cloud construction algorithm. Our work is relevant for researchers interested in navigability of emergent hypertext structures, and for engineers seeking to improve the navigability of social tagging systems.
Presently, solutions for geo-information sharing are mainly based on Web technologies, implementing service-oriented frameworks, and applying open (international or community) standards and interoperability arrangements. Such frameworks take the name of Spatial Data Infrastructures (SDIs). The recent evolution of the World Wide Web (WWW), with the introduction of the Semantic Web and the Web 2.0 concepts and solutions, made available new applications, architectures, and technologies for sharing and linking resources published on the Web. Such new technologies can be conveniently applied to enhance capabilities of present SDIs—in particular, discovery functionality.Different strategies can be adopted in order to enable new ways of searching geospatial resources, leveraging the Semantic Web and Web 2.0 technologies. The authors propose a Discovery Augmentation Methodology which is essentially driven by the idea of enriching the searchable information that is associated with geospatial resources. They describe and discuss three different high-level approaches for discovery augmentation: Provider-based, User-based, and Third-party based. From the analysis of these approaches, the authors suggest that, due to their flexibility and extensibility, the user-based and the third-party based approaches result more appropriate for heterogeneous and changing environments such as the SDI one. For the user-based approach, they describe a conceptual architecture and the main components centered on the integration of user-generated content in SDIs. For the third-party approach, the authors describe an architecture enabling semantics-based searches in SDIs.
One of the greatest challenges of Web 2.0 is to find the most relevant information to users’ needs with minimum effort and time. Though search en-gines have improved greatly in the recent years, search results heavily depend on the textual search query, which does not fully reflect users’ search context. In particular, teachers find it difficult to retrieve suitable materials to be used in their specific learning/teaching scenario. Tags, namely free-form user annota-tions, are found to be beneficial for improving search and retrieval but there has been no in-depth understanding as to which type of tags are useful and should be encouraged when designing a resource-sharing system for teachers. In this paper, we present an empirical study on the types of contextual meta-data that play an important role when teachers search for new learning/teaching materi-als. Based on results we collected, we propose a prototypical interface that fa-cilitates the searching of shared resources among a community of teachers.
Everyday, millions of users save content items for future use on sites like Pinterest, by ''pinning'' them onto carefully categorised personal pinboards, thereby creating personal taxonomies of the Web. This paper seeks to understand Pinterest as a distributed human computation that categorises images from around the Web. We show that despite being categorised onto personal pinboards by individual actions, there is a generally a global agreement in implicitly assigning images into a coarse-grained global taxonomy of 32 categories, and furthermore, users tend to specialise in a handful of categories. By exploiting these characteristics, and augmenting with image-related features drawn from a state-of-the-art deep convolutional neural network, we develop a cascade of predictors that together automate a large fraction of Pinterest actions. Our end-to-end model is able to both predict whether a user will repin an image onto her own pinboard, and also which pinboard she might choose, with an accuracy of 0.69 (Accuracy@5 of 0.75).
Social tagging systems have established themselves as an important part in today's Web and have attracted the interest of our research community in a variety of investigations. Henceforth, several aspects of social tagging systems have been discussed and assumptions have emerged on which our community builds their work. Yet, testing such assumptions has been difficult due to the absence of suitable usage data in the past. In this work, we thoroughly investigate and evaluate four aspects about tagging systems, covering social interaction, retrieval of posted resources, the importance of the three different types of entities, users, resources, and tags, as well as connections between these entities' popularity in posted and in requested content. For that purpose, we examine live server log data gathered from the real-world, public social tagging system BibSonomy. Our empirical results paint a mixed picture about the four aspects. Although typical assumptions hold to a certain extent for some, other aspects need to be reflected in a very critical light. Our observations have implications for the understanding of social tagging systems and the way they are used on the Web. We make the dataset used in this work available to other researchers.
Because of a huge variety of biological studies focused on different targets, i.e., from molecules to ecosystem, data produced and used in each field is also managed independently so that it is difficult to know the relationship among them. We aim to build a data hub with LOD to connect data in different biological fields to enhance search and use of data across the fields. We build a prototype data hub on taxonomic information on species, which is a key to retrieve data and link to databases in different fields. We also demonstrate how the data hub can be used with an application to assist search on other database.
In the existing folksonomy system, users can be allowed to add any social tags to the resources, but tags are fuzzy and redundancy in semantic, which make it hard to obtain the required information for users. An optimized tag recommender algorithm is proposed to solve the problem in this paper. First, based on the motivation theory, the recommender system uses the model given to calculate the user retrieval motivation before searching information. Second, we use the results in first step to distinguish the user’s type and then cluster the resources tagged according to users who have the similar retrieval motivation with k-means++ algorithm and recommend the most relevant resources to users. The experimental results show that our proposed algorithm with user retrieval motivation can have higher accuracy and stability than traditional retrieval algorithms in folksonomy system.
Purpose
– The paper aims to explore the reading behavior among LibraryThing users through the linked book-recommendations of 15 popular books.
Design/methodology/approach
– The research was approached by using the normality test as an index regarding the current reading situation that expresses the intensity of reading preferences upon specific or various topics. The selected variable was the number of book members that is automatically referred by LT statistics. First, 15 popular book titles were included in the research. Second, the combined book recommendations associated with the books under study and appearing in LibraryThing were selected, and their members' data was analyzed.
Findings
– In order to evaluate the normality, data were plotted and they showed that the combined recommended books do not follow the curve of normal distribution. The lack of normality shape in all the books studied indicates that 25 percent of reading preferences are based on books with a specific theme, and are closely related to the book under study, while 75 percent are related to various subjects.
Originality/value
– Approaches to reading preferences of LT users are partially unexplored, and this research examines one of its aspects.
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 article, 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 state-of-the-art folksonomy induction algorithms to date.
Social tagging has become popular around the Internet as well as in research. The main idea behind tagging is to allow users to provide metadata to the web content from their perspective to facilitate categorization and retrieval. There are many factors that influence users' tag choice. Many studies have been conducted to reveal these factors by analysing tagging data. This paper uses two theories to identify these factors, namely the semiotics theory and activity theory. The former treats tags as signs and the latter treats tagging as an activity. The theoretical analysis produced a framework that was used to identify a number of factors. These factors can be considered as categories that can be consulted to influence user tagging choice in order to support particular tagging behaviour, such as cross-lingual tagging.
There are enormous amount of pages on web. We consider that there are many web pages periodically viewed by users. For example, we can imagine that pages about the illustration of New Year's cards are generally viewed near the end of each year. In this paper, we investigate the property of such periodically viewed web pages using social book marking systems. Specifically, we will confirm that how many pages are actually book marked with a specific period and why they are book marked periodically. We focus on the frequency analysis with a discrete Fourier transform as a concrete tool to analyze the temporal periodicity of bookmarks, and we use the Hatena Bookmark dataset as a concrete dataset of social bookmarks. As a result, we detected 1385 periodically book marked pages which accounts for approximately 1% of the pages stored in the dataset.
Ontologies have been a hot research topic for the recent decade and have been used for many applications such as information integration, semantic search, knowledge management, etc. Manual engineering of ontologies is a costly process and automatic ontology engineering lacks in precision. Folksonomies have recently emerged as another hot research topic and several research efforts have been made to extract (lightweight) ontologies automatically from folksonomy data.Due to the high cost of manual ontology engineering and the lack of precision in automatic ontology engineering it is important that we are able to evaluate the structure of the ontology. Detection of problems with the suggested ontology at an early stage can, especially for manually engineered ontologies, be cost saving.In this paper we present an approach to evaluate the quality of hierarchical relations in ontologies and folksonomy based structures. The approach is based on constructing shallow semantic representations of the ontology concepts and folksonomy tags. We specify four hypotheses regarding the semantic representations and different quality aspects of the hierarchical relations and perform an evaluation on two different data sets. The results of the evaluation confirm our hypotheses.
Recently social collaboration projects such as Wikipedia and Flickr have been gaining popularity, and more and more social tag information is being accumulated. In this study, we demonstrate how to effectively use social tags created by humans to find similar items. We create a query-by-example interface for finding similar items through offering examples as a query. Our work aims to measure the similarity between a query, expressed as a group of items, and another item through utilizing the tag information. We show that using human-generated tags to find similar items has at least two major challenges: popularity bias and the missing tag effect. We propose several approaches to overcome the challenges. We build a prototype website allowing users to search over all entries in Wikipedia based on tag information, and then collect 600 valid questionnaires from 69 students to create a benchmark for evaluating our algorithms based on user satisfaction. Our results show that the presented techniques are promising and surpass the leading commercial product, Google Sets, in terms of user satisfaction.
Social tagging systems allow users to upload and assign keywords to digital resources. Thus a body of user annotated resources gradually evolves: Users can share resources, re-find their own resources or use the systems as search engines for items added by the whole user population. In this paper we want to contribute towards a better understanding of usage patterns within social tagging systems by presenting results from a survey of 142 users of the systems Flickr, Youtube, Delicious and Connotea. Data was gathered partly by using the Mechanical Turk service, and partly via an announcement on the Connotea blog. Our study reveals differences of user motivation and tag usage between systems. While (resource) sharing emerges as an all-embracing intra-system motivation, users differ with respect to social spheres of sharing. Based on our results which we integrated with earlier research from Cool and Belkin (2002), we propose a model of information behaviour in social tagging systems.
Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.
Tagging is the process of annotating resources with keywords or terms in order to enable better organization, retrieval and sharing of the resource. Today, a large number of diverse applications exist that allow users to tag their documents. Del.icio.us, for example, enables users to organize their bookmarks and tag them accordingly. Flickr allows users to tag pictures and YouTube enables content creators to assign tags to videos in order to facilitate retrieval. While these systems have increasingly become an object of research for web scientists, little is known about these systems, for example how and why they are used, how behavior on a micro level propagates to phenomenon on the macro level, or we can simulate the dynamics of tagging system's evolution. My research interest lies in understanding and modeling the motivation of users, in particular why and how they are using these systems. A related topic of interest is the modeling of the user behavior with simulators. Modeling and evaluating the habits of users opens doors for deeper empirical studies, such as simulation. This way it might also be possible to study relations and interactions between the behavior of single users and the behavior of larger tagging communities.
At the time of writing, all bookmarks posted to Connotea are visible to all registered users and visitors. This takes the concept of sharing to a new level, but also brings new opportunities. We recognise that some users will want to keep some of what they are reading personal, so private bookmarks will soon be added as a feature, though the default will be to make the bookmark public. The main benefits of openness come not just from the ease with which it allows explicit sharing with friends and colleagues, but from many users storing their bookmarks in the same place. This allows Connotea to automatically discover and present connections between users. For example, if someone else has bookmarked the same things as you, that person's library will be a good candidate for a place to find interesting new content. In addition, shared lists allow more sophisticated collaborative filtering algorithms to make recommendations of the form "people who bookmarked this also bookmarked...".
Figure 1 also illustrates the second key feature of Connotea. When a URL is added to Connotea, it is first analysed in order to determine whether it belongs to the set of URLs that Connotea recognises. This can trigger a special behaviour for those web pages that represent academic articles or books bibliographic data for the reference material is collected and added to Connotea. For example, for scholarly articles, Connotea stores the publication name, volume and issue numbers, publication date and the list of authors for the article. At the time of writing, Connotea supports four different article archives and websites [n2], and the Amazon websites for books.
Once the URL has been sent to Connotea and the bibliographic import process has been completed, the user can add personalised information. The most essential information is the list of tags to associate with the article. Tags are the means by which references are organised in Connotea. Suitable tags should therefore be meaningful in the context of that particular article and that user. For this reason, Connotea allows tags to be almost anything (including both single words and phrases). As discussed above, tags can be thought of as a list of categories for the article, or as folder names, albeit without the potential inconvenience of hierarchy and with the bonus of being able to store the article simultaneously in multiple folders. Figure 3 shows a Connotea window for adding an article. The article in question has been identified, and a few suitable tags have been entered. There is also an option to add a personal description of the resource being bookmarked.
As alluded to in our overview of Connotea, we plan to add the ability to keep certain bookmarks private viewable only by the user who posted them. While this removes certain benefits to others of bookmark sharing, we hope that it will also encourage some users to participate who might not otherwise use the system at all, and that those users will make at least some of their bookmarks public. Such users can, of course, still benefit from other users' public bookmarks.
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 a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that "verbose" taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most 'verbose' taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing "semantic noise", and (iii) in learning ontologies.
Users of social computing websites are both producers and consumers of the information found on the site. This creates a novel problem for web-based software applications: how can website designers induce users to produce information that is useful for others? We study this question by interviewing users of the social bookmarking website del.icio.us. We find that for the users in our sample, metadata reflecting who bookmarked a webpage better supports information seeking than free-form keyword metadata (tags). We explain this finding by describing differences in the way that the design of del.icio.us motivates users to contribute by providing personal benefits for bookmarking and tagging. Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/61317/1/1450440240_ftp.pdf
Coates, onomies org/archives/2005/06/two cultures of fauxonomies collide/. Last access: May 8:2008 Usage patterns of col-laborative tagging systems
- S Golder
- B Huberman
2005. collide. Coates, onomies org/archives/2005/06/two cultures of fauxonomies collide/. Last access: May 8:2008. Golder, S., and Huberman, B. 2006. Usage patterns of col-laborative tagging systems. Journal of Information Science 32(2):198. Hammond, T.; Hannay, T.; Lund, B.; and Scott, J