Acceptance of workshop proposals will be based on the experience and background of the organizers in the topic, and on the relevance of the subject matter with regard to the topics addressed in the main conference.

The deadline for submitting proposals will be January 10, 2012January 24, 2012.

Submission of Proposals

Proposals for Workshops should include the following elements:

• Title and acronym;
• Name(s), affiliation(s), mailing address(es) and e-mail address(es) of the proposers;
• A description of the topic and motivation of the workshop (not exceeding 500 words);
• A short description of the target audience;
• A first version of the Call for Papers;
• Type of the workshop (full-day or half-day);
• Arrangements for the organization of the workshop, including a brief outline of the workshop describing anticipated format, possible session names, invited talks, panels, demos;
• A short description on how the workshop will be advertised so as to ensure a sufficiently wide range of authors and high quality papers along with an estimation of the number of participants;
• Names, addresses, and home page links of people who have agreed to be part of the program committee of the workshop;
• A brief description of the experience and background of the organizers in the topic, and links to homepages of the workshop chairs.

The organizers are encouraged to provide the following additional information:

• A preliminary list of reviewers
• A list of potential authors
• A list of potential attendees

Proposals should be submitted electronically as a single PDF file to the workshop chairs:

• Arkaitz Zubiaga (City University of New York): arkaitz@zubiaga.org
• Carlos Solis (Fexco): csolis@fexco.com

Key dates:

• January 10, 2012January 24, 2012: Workshop/Tutorial submissions due
• January 17, 2012January 30, 2012: Notification to proposers
• January 25, 2012February 6, 2012: The organizers of each workshop send out the Call for Papers
• June 25, 2012: Workshop day

Organization of Workshops

After the acceptance of a workshop proposal the organizer(s) should:

• Create and distribute a Call for Papers and a Call for Participation;
• Create a Web page for the workshop, the link of which will be published on the Conference Web site;
• Create a Program Committee;
• Review and select contributions to be included in the workshop proceedings (at least 2 reviewers for each paper);
• Schedule and coordinate the workshop activities.
• Put together accepted papers into electronic workshop proceedings and make them available online.

For details, please visit: http://www.ht2012.org/

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Abstract

Twitter summarizes the great deal of messages posted by users in the form of trending topics that reflect the top conversations being discussed at a given moment. These trending topics tend to be connected to current affairs. Different happenings can give rise to the emergence of these trending topics. For instance, a sports event broadcasted on TV, or a viral meme introduced by a community of users. Detecting the type of origin can facilitate information filtering, enhance real-time data processing, and improve user experience. In this paper, we introduce a typology to categorize the triggers that leverage trending topics: news, current events, memes, and commemoratives. We define a set of straightforward language-independent features that rely on the social spread of the trends to discriminate among those types of trending topics. Our method provides an efficient way to immediately and accurately categorize trending topics without need of external data, outperforming a content-based approach.

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Abstract

Recent research has shown that different tagging motivation and user behavior can effect the overall usefulness of social tagging systems for certain tasks. In this paper, we provide further evidence for this observation by demonstrating that tagging data obtained from certain types of users – so-called Categorizers – outperforms data from other users on a social classification task. We show that segmenting users based on their tagging behavior has significant impact on the performance of automated classification of tagged data by using (i) tagging data from two different social tagging systems, (ii) a Support Vector Machine as a classification mechanism and (iii) existing classification systems such as the Library of Congress Classification System as ground truth. Our results are relevant for scientists studying pragmatics and semantics of social tagging systems as well as for engineers interested in influencing emerging properties of deployed social tagging systems.

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Have a look at it!

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The dataset, including both articles’ content and social tags, can be downloaded from here.

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English

Spanish

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Abstract

Social tagging has become an interesting approach to improve search and navigation over the actual Web, since it aggregates the tags added by different users to the same resource in a collaborative way. This way, it results in a list of weighted tags describing its resource. Combined to a classical taxonomic classification system such as that by Wikipedia, social tags can enhance document navigation and search. On the one hand, social tags suggest alternative navigation ways, including pivot-browsing, popularity-driven navigation, and filtering. On the other hand, it provides new metadata, sometimes uncovered by the documents’ content, that can substantially improve document search. In this work, the inclusion of an interface to add user-defined tags describing Wikipedia articles is proposed, as a way to improve article navigation and retrieval.

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Abstract

User-generated annotations on social bookmarking sites can provide interesting and promising metadata for web document management tasks like web page classification. These user-generated annotations include diverse types of information, such as tags and comments. Nonetheless, each kind of annotation has a different nature and popularity level. In this work, we analyze and evaluate the usefulness of each of these social annotations to classify web pages over a taxonomy like that proposed by the Open Directory Project. We compare them separately to the content-based classification, and also combine the different types of data to augment performance. Our experiments show encouraging results with the use of social annotations for this purpose, and we found that combining these metadata with web page content improves even more the classifier’s performance.

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Abstract

Las anotaciones generadas por usuarios en sistemas de marcadores sociales pueden proveer metadatos interesantes y muy útiles para la clasificación de páginas web. Estas anotaciones incluyen diversos tipos de información, como etiquetas y comentarios. No obstante, cada tipo de anotación tiene una naturaleza y un nivel de popularidad diferente. En este trabajo, analizamos y evaluamos la utilidad de cada una de estas anotaciones sociales para clasificar páginas web sobre una taxonomía como la del Open Directory Project. Las comparamos por separado a la clasificación basada en contenido, y también las combinamos. Nuestros experimentos muestran resultados prometedores con la utilización de anotaciones sociales para este propósito. Y además indican que su combinación con el contenido textual mejora el rendimiento de la clasificación.

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