A Comprehensive Low and High-level Feature Analysis for Early Rumor Detection on Twitter
This work addresses the problem of detecting misinformation early in microblogs, which is crucial for timely debunking, though it appears incremental by building on existing rumor detection methods.
The paper tackles early rumor detection on Twitter by leveraging neural models to learn hidden representations of individual tweets at the beginning of a rumor, achieving over 90% accuracy and outperforming baselines within the first 10 hours.
Recent work have done a good job in modeling rumors and detecting them over microblog streams. However, the performance of their automatic approaches are not relatively high when looking early in the diffusion. A first intuition is that, at early stage, most of the aggregated rumor features (e.g., propagation features) are not mature and distinctive enough. The objective of rumor debunking in microblogs, however, are to detect these misinformation as early as possible. In this work, we leverage neural models in learning the hidden representations of individual rumor-related tweets at the very beginning of a rumor. Our extensive experiments show that the resulting signal improves our classification performance over time, significantly within the first 10 hours. To deepen the understanding of these low and high-level features in contributing to the model performance over time, we conduct an extensive study on a wide range of high impact rumor features for the 48 hours range. The end model that engages these features are shown to be competitive, reaches over 90% accuracy and out-performs strong baselines in our carefully cured dataset.