Seminar Web Science - THEMEN

 

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Sessions des Seminars

 

30.11.09: Kick-off meeting

  • Sergej Sizov: Presentation Skills and Scientific Writing 

07.12.09: Research & WeST (1)

14.12.09: Research & WeST (2)

 

18.01.10: Media Intelligence

Session Chairs: Mohamed Bakdasch

01.02.10: Verschiebung, kein Seminar!

08.02.10: User Behavior

Session Chairs: Dorothee Terres

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Die Themen im Einzelnen

Ranking and Classifying Attractiveness of Photos in Folksonomies (WWW09)

Jose San Pedro, Stefan Siersdorfer

Abstract: Web 2.0 applications like Flickr, YouTube, or Del.icio.us are increasingly popular online communities for creating, editing and sharing content. The growing size of these folksonomies poses new challenges in terms of search and data mining. In this paper we introduce a novel methodology for automatically ranking and classifying photos according to their attractiveness for folksonomy members. To this end, we exploit image features known for having significant effects on the visual quality perceived by humans (e.g. sharpness and colorfulness) as well as textual meta data, in what is a multi-modal approach. Using feedback and annotations available in the Web 2.0 photo sharing system Flickr, we assign relevance values to the photos and train classification and regression models based on these relevance assignments. With the resulting machine learning models we categorize and rank photos according to their attractiveness. Applications include enhanced ranking functions for search and recommender methods for attractive content. Large scale experiments on a collection of Flickr photos demonstrate the viability of our approach.

 

 

Mapping the world's Photos (WWW09)

David J. Crandall, Lars Backstrom, Daniel Huttenlocher, Jon Kleinberg

Abstract: We investigate how to organize a large collection of geotagged photos, working with a dataset of about 35 million images collected from Flickr. Our approach combines content analysis based on text tags and image data with structural analysis based on geospatial data. We use the spatial distribution of where people take photos to define a relational structure between the photos that are taken at popular places. We then study the interplay between this structure and the content, using classification methods for predicting such locations from visual, textual and temporal features of the photos. We find that visual and temporal features improve the ability to estimate the location of a photo, compared to using just textual features. We illustrate using these techniques to organize a large photo collection, while also revealing various interesting properties about popular cities and landmarks at a global scale.

 

Web search behavior of Internet experts and newbies (WWW00)

Christoph Hölscher, Gerhard Strube

Abstract: Searching for relevant information on the World Wide Web is often a laborious and frustrating task for casual and experienced users. To help improve searching on the Web based on a better understanding of user characteristics, we investigate what types of knowledge are relevant for Web-based information seeking, and which knowledge structures and strategies are involved. Two experimental studies are presented, which address these questions from different angles and with different methodologies. In the first experiment 12 established Internet experts are first interviewed about search strategies and then perform a series of realistic search tasks on the WWW. From this study a model of information seeking on the WWW is derived and then tested in a second study. In the second experiment two types of potentially relevant types of knowledge are compared directly. Effects of Web experience and domain-specific background knowledge are investigated with a series of search tasks in an economics-related domain (introduction of the EURO currency). We find differential and combined effects of both Web experience and domain knowledge: While successful search performance requires the combination of the two types of expertise, specific strategies directly related to Web experience or domain knowledge can be identified.

 

Telling Experts from Spammers: Expertise Ranking in Folksonomies (SIGIR09)

Michael G. Noll, Ching-man Au Yeung, Nicholas Gibbins, Christoph Meinel, Nigel Shadbolt

Abstract: With a suitable algorithm for ranking the expertise of a user in a collaborative tagging system, we will be able to identify experts and discover useful and relevant resources through them. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. Firstly, an expert should possess a high quality collection of resources, while the quality of a Web resource depends on the expertise of the users who have assigned tags to it. Secondly, an expert should be one who tends to identify interesting or useful resources before other users do. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements these ideas for ranking users in a folksonomy. We evaluate our method with experiments on data sets collected from Delicious.com comprising over 71,000 Web documents, 0.5 million users and 2 million shared bookmarks. We also show that the algorithm is more resistant to spammers than other methods such as the original HITS algorithm and simple statistical measures.

 

Analysis of a Real Online Social Network Using Semantic Web Frameworks (ISWC09)

Guillaume Erétéo, Michel Buffa, Fabien Gandon, and Olivier Corby

Abstract: Social Network Analysis (SNA) provides graph algorithms to characterize
the structure of social networks, strategic positions in these networks, specific
sub-networks and decompositions of people and activities. Online social
platforms like Facebook form huge social networks, enabling people to connect,
interact and share their online activities across several social applications. We
extended SNA operators using semantic web frameworks to include the semantics
of these graph-based representations when analyzing such social networks
and to deal with the diversity of their relations and interactions. We present here
the results of this approach when it was used to analyze a real social network
with 60,000 users connecting, interacting and sharing content.

 

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