Spotting Rumors via Novelty Detection
This addresses the challenge of preventing harm from spreading rumors for social media and news platforms, though it is incremental as it builds on existing real-time methods.
The paper tackles the problem of early rumor detection by introducing novelty-based features and pseudo feedback, achieving significantly better performance compared to other real-time approaches immediately after publication.
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.