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Kraker Peter, Wagner Claudia, Jeanquartier Fleur, Lindstaedt Stefanie

On the Way to a Science Intelligence: Visualizing TEL Tweets for Trend Detection

Sixth European Conference on Technology Enhanced Learning (EC-TEL 2011), Springer, 2011

This paper presents an adaptable system for detecting trends based on the micro-blogging service Twitter, and sets out to explore to what extent such a tool can support researchers. Twitter has high uptake in the scientific community, but there is a need for a means of extracting the most important topics from a Twitter stream. There are too many tweets to read them all, and there is no organized way of keeping up with the backlog. Following the cues of visual analytics, we use visualizations to show both the temporal evolution of topics, and the relations between different topics. The Twitter Trend Detection was evaluated in the domain of Technology Enhanced Learning (TEL). The evaluation results indicate that our prototype supports trend detection but reveals the need for refined preprocessing, and further zooming and filtering facilities.

Lindstaedt Stefanie , Pammer-Schindler Viktoria, Mörzinger Roland, Kern Roman, Mülner Helmut, Wagner Claudia

Recommending tags for pictures based on text, visual content and user context

Third International Conference on Internet and Web Applications and Services (ICIW), IEEE, 2008

Imagine you are member of an online social systemand want to upload a picture into the community pool. In currentsocial software systems, you can probably tag your photo, shareit or send it to a photo printing service and multiple other stuff.The system creates around you a space full of pictures, otherinteresting content (descriptions, comments) and full of users aswell. The one thing current systems do not do, is understandwhat your pictures are about.We present here a collection of functionalities that make a stepin that direction when put together to be consumed by a tagrecommendation system for pictures. We use the data richnessinherent in social online environments for recommending tags byanalysing different aspects of the same data (text, visual contentand user context). We also give an assessment of the quality ofthus recommended tags.
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