Eva Zangerle

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Online social networks like Facebook or Twitter have become powerful information diffusion platforms as they have attracted hundreds of millions of users. The possibility of reaching millions of users within these networks not only attracted standard users, but also cyber-criminals who abuse the networks by spreading spam. This is accomplished by either(More)
Microblogging applications such as Twitter are experiencing tremendous success. Microblog users utilize hashtags to categorize posted messages which aim at bringing order to the myriads of microblog messages. However, the percentage of messages incorporating hashtags is small and the used hashtags are very heterogeneous as hashtags may be chosen freely and(More)
Twitter is the largest source of public opinion and also contains a vast amount of information about its users’ music favors or listening behaviour. However, this source has not been exploited for the recommendation of music yet. In this paper, we present how Twitter can be facilitated for the creation of a data set upon which music recommendations can be(More)
The constant growth of available RDF data requires fast and efficient querying facilities of graph data. So far, such data sets have been stored by using mapping techniques from graph structures to relational models, secondary memory structures or even complex main memory based models. We present the main memory database SpiderStore which is capable of(More)
The extraction of information from online social networks has become popular in both industry and academia as these data sources allow for innovative applications. However, in the area of music recommender systems and music information retrieval, respective data is hardly exploited. In this paper, we present the #nowplaying dataset, which leverages social(More)
Twitter is one of the leading social media platforms, where hundreds of millions of tweets cover a wide range of topics, including the music a user is listening to. Such #nowplaying tweets may serve as an indicator for future charts, however, this has not been thoroughly studied yet. Therefore, we investigate to which extent such tweets correlate with the(More)
Knowledge is structured - until it is stored to a wiki-like information system. In this paper we present the multi-user system <i>SnoopyDB</i>, which preserves the structure of knowledge without restricting the type or schema of inserted information. A self-learning schema system and recommendation engine support the user during the process of inserting(More)