Dominik WurzerPhD student at the University of Edinburgh I am PhD student at the University of Edinburgh focusing on information retrieval on high volume data streams and applied machine learning.
- Publications -
Counteracting Novelty Decay in First Story Detection
Spotting Information biases in Chinese and Western Media
Newswire and Social Media are the major sources of information in our time. While the
Spotting Rumors via Novelty Detection
Rumour detection is hard because the most accurate systems operate retrospectively, only recognizing rumours once they have collected repeated signals. By then the rumours might have already spread and caused harm. We introduce a new category of features based on novelty, tailored to detect rumours early on. To compensate for the absence of repeated signals, we make use of news wire as an additional data source. Unconfirmed (novel) information with respect to the news articles is considered as an indication of rumours. Additionally we introduce pseudo feedback, which assumes that documents that are similar to previous rumours, are more likely to also be a rumour. Comparison with other real-time approaches shows that novelty based features in conjunction with pseudo feedback perform significantly better, when detecting rumours instantly after their publication.