AdaDebunk: An Efficient and Reliable Deep State Space Model for Adaptive Fake News Early Detection

Automatically detecting fake news as early as possible becomes increasingly necessary. Conventional approaches of fake news early detection (FNED) verify news' veracity with a predefined and indiscriminate detection position, which depends on domain experience and leads to unstable performance. More advanced methods address this problem with a proposed concept of adaptive detection position (ADP), i.e. the position where the veracity of the news record can be concluded. Yet these methods either lack theoretical reliability or weaken complex dependencies among multi-aspect clues, thus failing to provide practical and reasonable detection. This work focuses on the adaptive FNED problem and proposes a novel efficient and reliable deep state space model, namely AdaDebunk, which models the complex probabilistic dependencies. Specifically, a Bayes' theorem-based dynamic inference algorithm is designed to infer the ADPs and veracity, supporting the accumulation of multi-aspect clues. Besides, a training mechanism with hybrid loss is also designed to solve the over-/under-fitting problems, which further trades off the performance and generalization ability. Experiments on two real-world fake news datasets are conducted to evaluate the effectiveness and superiority of AdaDebunk. Compared with the state-of-the-art baselines, AdaDebunk achieves a 10% increase in F1 performance. Meanwhile, a case study is provided to demonstrate the reliability of AdaDebunk as well as our research motivation.