Knowledge Graphs for Real-World Rumour Verification

John Dougrez-Lewis, Elena Kochkina, Maria Liakata, Yulan He

Abstract: Despite recent progress in automated rumour verification, little has been done on evaluating rumours in a real-world setting. We advance the state-of-the-art on the PHEME dataset, which consists of Twitter response threads collected as a rumour was unfolding. We automatically collect evidence relevant to PHEME and use it to construct knowledge graphs in a time-sensitive manner, excluding information post-dating rumour emergence. We identify discrepancies between the evidence retrieved and PHEME’s labels, which are discussed in detail and amended to release an updated dataset. We develop a novel knowledge graph approach which finds paths linking disjoint fragments of evidence. Our rumour verification model which combines evidence from the graph outperforms the state-of-the-art on PHEME and has superior generisability when evaluated on a temporally distant rumour verification dataset.