Early Rumor Detection Using Neural Hawkes Process with a New Benchmark Dataset
This work addresses early rumor detection for social media and fact-checking applications, but it is incremental as it builds on existing rumor detection methods with a new dataset and model adaptation.
The paper tackles the problem of early rumor detection by constructing a new benchmark dataset (BEARD) that includes early-stage information and proposing a neural Hawkes process model (HEARD) for timely and accurate predictions. Experiments show HEARD achieves effective performance on multiple datasets, though specific numerical results are not provided.
Little attention has been paid on \underline{EA}rly \underline{R}umor \underline{D}etection (EARD), and EARD performance was evaluated inappropriately on a few datasets where the actual early-stage information is largely missing. To reverse such situation, we construct BEARD, a new \underline{B}enchmark dataset for \underline{EARD}, based on claims from fact-checking websites by trying to gather as many early relevant posts as possible. We also propose HEARD, a novel model based on neural \underline{H}awkes process for \underline{EARD}, which can guide a generic rumor detection model to make timely, accurate and stable predictions. Experiments show that HEARD achieves effective EARD performance on two commonly used general rumor detection datasets and our BEARD dataset.