Study about the different use of explicit and implicit tags in social bookmarking

This is the accepted version of the following article: Arolas, E. E., & Ladrón-de-Guevar, F. G. (2012). Uses of explicit and implicit tags in social bookmarking. Journal of the American Society for Information Science and Technology, 63(2), 313-322. doi:10.1002/asi.21663, which has been published in final form at http://dx.doi.org/10.1002/asi.21663Although Web 2.0 contains many tools with different functionalities, they all share a common social nature. One tool in particular, social bookmarking systems (SBSs), allows users to store and share links to different types of resources, i.e., websites, videos, images. To identify and classify these resources so that they can be retrieved and shared, fragments of text are used. These fragments of text, usually words, are called tags. A tag that is found on the inside of a resource text is referred to as an obvious or explicit tag. There are also nonobvious or implicit tags, which don't appear in the resource text. The purpose of this article is to describe the present situation of the SBSs tool and then to also determine the principal features of and how to use explicit tags. It will be taken into special consideration which HTML tags with explicit tags are used more frequently.Estelles Arolas, E.; González Ladrón De Guevara, FR. (2012). Study about the different use of explicit and implicit tags in social bookmarking. Journal of the American Society for Information Science and Technology. 63(2):313-322. doi:10.1002/asi.21663S313322632Bar-Ilan, J., Zhitomirsky-Geffet, M., Miller, Y., & Shoham, S. (2010). The effects of background information and social interaction on image tagging. Journal of the American Society for Information Science and Technology, 61(5), 940-951. doi:10.1002/asi.21306Bateman, S., Muller, M. J., & Freyne, J. (2009). Personalized retrieval in social bookmarking. Proceedinfs of the ACM 2009 international conference on Supporting group work - GROUP ’09. doi:10.1145/1531674.1531688Delicious' Blog 2010 What's next for Delicious http://blog.delicious.com/blog/2010/12/whats-next-for-delicious.htmlDing, Y., Jacob, E. K., Zhang, Z., Foo, S., Yan, E., George, N. L., & Guo, L. (2009). Perspectives on social tagging. Journal of the American Society for Information Science and Technology, 60(12), 2388-2401. doi:10.1002/asi.21190Eisterlehner , F. Hotho , A. Jäschke , R. ECML PKDD Discovery Challenge 2009 (DC09)Farooq, U., Kannampallil, T. G., Song, Y., Ganoe, C. H., Carroll, J. M., & Giles, L. (2007). Evaluating tagging behavior in social bookmarking systems. Proceedings of the 2007 international ACM conference on Conference on supporting group work - GROUP ’07. doi:10.1145/1316624.1316677Farooq , U. Zhang , S.M. Carroll , J. 2009 Sensemaking of scholarly literature through taggingFu, W.-T., Kannampallil, T., Kang, R., & He, J. (2010). Semantic imitation in social tagging. ACM Transactions on Computer-Human Interaction, 17(3), 1-37. doi:10.1145/1806923.1806926Furnas, G. W., Landauer, T. K., Gomez, L. M., & Dumais, S. T. (1987). The vocabulary problem in human-system communication. Communications of the ACM, 30(11), 964-971. doi:10.1145/32206.32212Golder , S.A. Huberman , B.A. 2005 The structure of collaborative tagging systems http://www.hpl.hp.com/research/idl/papers/tagsKörner, C., Benz, D., Hotho, A., Strohmaier, M., & Stumme, G. (2010). Stop thinking, start tagging. Proceedings of the 19th international conference on World wide web - WWW ’10. doi:10.1145/1772690.1772744Koutrika, G., Effendi, F. A., Gyöngyi, Z., Heymann, P., & Garcia-Molina, H. (2008). Combating spam in tagging systems. ACM Transactions on the Web, 2(4), 1-34. doi:10.1145/1409220.1409225Lipczak, M., & Milios, E. (2010). The impact of resource title on tags in collaborative tagging systems. Proceedings of the 21st ACM conference on Hypertext and hypermedia - HT ’10. doi:10.1145/1810617.1810648Marinho, L. B., Nanopoulos, A., Schmidt-Thieme, L., Jäschke, R., Hotho, A., Stumme, G., & Symeonidis, P. (2010). Social Tagging Recommender Systems. Recommender Systems Handbook, 615-644. doi:10.1007/978-0-387-85820-3_19Marlow, C., Naaman, M., Boyd, D., & Davis, M. (2006). HT06, tagging paper, taxonomy, Flickr, academic article, to read. Proceedings of the seventeenth conference on Hypertext and hypermedia - HYPERTEXT ’06. doi:10.1145/1149941.1149949Mathes , A. 2004 Folksonomies-Cooperative classification and communication through shared metadata http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.htmlMelenhorst, M., & van Setten, M. (2007). Usefulness of Tags in Providing Access to Large Information Systems. 2007 IEEE International Professional Communication Conference. doi:10.1109/ipcc.2007.4464070Millen, D., Feinberg, J., & Kerr, B. (2005). Social bookmarking in the enterprise. Queue, 3(9), 28. doi:10.1145/1105664.1105676Robu, V., Halpin, H., & Shepherd, H. (2009). Emergence of consensus and shared vocabularies in collaborative tagging systems. ACM Transactions on the Web, 3(4), 1-34. doi:10.1145/1594173.1594176Schmitz, C., Hotho, A., Jäschke, R., & Stumme, G. (s. f.). Mining Association Rules in Folksonomies. Data Science and Classification, 261-270. doi:10.1007/3-540-34416-0_28Smith , G. 2004 Atomiq: Folksonomy: social classification http://atomiq.org/archives/2004/08/folksonomy_social_classification.htmlSubramanya, S. B., & Liu, H. (2008). Socialtagger - collaborative tagging for blogs in the long tail. Proceeding of the 2008 ACM workshop on Search in social media - SSM ’08. doi:10.1145/1458583.1458588Au Yeung, C., Gibbins, N., & Shadbolt, N. (2009). Contextualising tags in collaborative tagging systems. Proceedings of the 20th ACM conference on Hypertext and hypermedia - HT ’09. doi:10.1145/1557914.1557958Zhang, N., Zhang, Y., & Tang, J. (2009). A tag recommendation system for folksonomy. Proceeding of the 2nd ACM workshop on Social web search and mining - SWSM ’09. doi:10.1145/1651437.165144

Semantic Stability in Social Tagging Streams

One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources may become semantically stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the semantic stability of social tagging systems in a robust and methodical way? (ii) Does semantic stabilization of tags vary across different social tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization processes in a wide range of social tagging systems with distinct domains and properties and (iii) detecting potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams which are generated via imitation dynamics or natural language streams alone

Semantics, sensors, and the social web: The live social semantics experiments

The Live Social Semantics is an innovative application that encourages and guides social networking between researchers at conferences and similar events. The application integrates data and technologies from the Semantic Web, online social networks, and a face-to-face contact sensing platform. It helps researchers to find like-minded and influential researchers, to identify and meet people in their community of practice, and to capture and later retrace their real-world networking activities at conferences. The application was successfully deployed at two international conferences, attracting more than 300 users in total. This paper describes this application, and discusses and evaluates the results of its two deployment

On content-based recommendation and user privacy in social-tagging systems

Recommendation systems and content filtering approaches based on annotations and ratings, essentially rely on users expressing their preferences and interests through their actions, in order to provide personalised content. This activity, in which users engage collectively has been named social tagging, and it is one of the most popular in which users engage online, and although it has opened new possibilities for application interoperability on the semantic web, it is also posing new privacy threats. It, in fact, consists of describing online or offline resources by using free-text labels (i.e. tags), therefore exposing the user profile and activity to privacy attacks. Users, as a result, may wish to adopt a privacy-enhancing strategy in order not to reveal their interests completely. Tag forgery is a privacy enhancing technology consisting of generating tags for categories or resources that do not reflect the user's actual preferences. By modifying their profile, tag forgery may have a negative impact on the quality of the recommendation system, thus protecting user privacy to a certain extent but at the expenses of utility loss. The impact of tag forgery on content-based recommendation is, therefore, investigated in a real-world application scenario where different forgery strategies are evaluated, and the consequent loss in utility is measured and compared.Peer ReviewedPostprint (author’s final draft

The other side of the social web: A taxonomy for social information access

The power of the modern Web, which is frequently called the Social Web or Web 2.0, is frequently traced to the power of users as contributors of various kinds of contents through Wikis, blogs, and resource sharing sites. However, the community power impacts not only the production of Web content, but also the access to all kinds of Web content. A number of research groups worldwide explore what we call social information access techniques that help users get to the right information using "collective wisdom" distilled from actions of those who worked with this information earlier. This invited talk offers a brief introduction into this important research stream and reviews recent works on social information access performed at the University of Pittsburgh's PAWS Lab lead by the author. Copyright © 2012 by the Association for Computing Machinery, Inc. (ACM)

The impact of image descriptions on user tagging behavior: A study of the nature and functionality of crowdsourced tags

Crowdsourcing has emerged as a way to harvest social wisdom from thousands of volunteers to perform a 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 on user tagging behavior. Although 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 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, and so on. 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 reuse 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, whereas tags produced without description increased user success in the search task

Metadata enrichment for digital heritage: users as co-creators

This paper espouses the concept of metadata enrichment through an expert and user-focused approach to metadata creation and management. To this end, it is argued the Web 2.0 paradigm enables users to be proactive metadata creators. As Shirky (2008, p.47) argues Web 2.0’s social tools enable “action by loosely structured groups, operating without managerial direction and outside the profit motive”. Lagoze (2010, p. 37) advises, “the participatory nature of Web 2.0 should not be dismissed as just a popular phenomenon [or fad]”. Carletti (2016) proposes a participatory digital cultural heritage approach where Web 2.0 approaches such as crowdsourcing can be sued to enrich digital cultural objects. It is argued that “heritage crowdsourcing, community-centred projects or other forms of public participation”. On the other hand, the new collaborative approaches of Web 2.0 neither negate nor replace contemporary standards-based metadata approaches. Hence, this paper proposes a mixed metadata approach where user created metadata augments expert-created metadata and vice versa. The metadata creation process no longer remains to be the sole prerogative of the metadata expert. The Web 2.0 collaborative environment would now allow users to participate in both adding and re-using metadata. The case of expert-created (standards-based, top-down) and user-generated metadata (socially-constructed, bottom-up) approach to metadata are complementary rather than mutually-exclusive. The two approaches are often mistakenly considered as dichotomies, albeit incorrectly (Gruber, 2007; Wright, 2007) . This paper espouses the importance of enriching digital information objects with descriptions pertaining the about-ness of information objects. Such richness and diversity of description, it is argued, could chiefly be achieved by involving users in the metadata creation process. This paper presents the importance of the paradigm of metadata enriching and metadata filtering for the cultural heritage domain. Metadata enriching states that a priori metadata that is instantiated and granularly structured by metadata experts is continually enriched through socially-constructed (post-hoc) metadata, whereby users are pro-actively engaged in co-creating metadata. The principle also states that metadata that is enriched is also contextually and semantically linked and openly accessible. In addition, metadata filtering states that metadata resulting from implementing the principle of enriching should be displayed for users in line with their needs and convenience. In both enriching and filtering, users should be considered as prosumers, resulting in what is called collective metadata intelligence