  Early rumor detection (ERD) on social media platform is very challenging when
limited, incomplete and noisy information is available. Most of the existing
methods have largely worked on event-level detection that requires the
collection of posts relevant to a specific event and relied only on
user-generated content. They are not appropriate to detect rumor sources in the
very early stages, before an event unfolds and becomes widespread. In this
paper, we address the task of ERD at the message level. We present a novel
hybrid neural network architecture, which combines a task-specific
character-based bidirectional language model and stacked Long Short-Term Memory
(LSTM) networks to represent textual contents and social-temporal contexts of
input source tweets, for modelling propagation patterns of rumors in the early
stages of their development. We apply multi-layered attention models to jointly
learn attentive context embeddings over multiple context inputs. Our
experiments employ a stringent leave-one-out cross-validation (LOO-CV)
evaluation setup on seven publicly available real-life rumor event data sets.
Our models achieve state-of-the-art(SoA) performance for detecting unseen
rumors on large augmented data which covers more than 12 events and 2,967
rumors. An ablation study is conducted to understand the relative contribution
of each component of our proposed model.
