  Existing rumor detection strategies typically provide detection labels while
ignoring their explanation. Nonetheless, providing pieces of evidence to
explain why a suspicious tweet is rumor is essential. As such, a novel model,
LOSIRD, was proposed in this paper. First, LOSIRD mines appropriate evidence
sentences and classifies them by automatically checking the veracity of the
relationship of the given claim and its evidence from about 5 million Wikipedia
documents. LOSIRD then automatically constructs two heterogeneous graph objects
to simulate the propagation layout of the tweets and code the relationship of
evidence. Finally, a graphSAGE processing component is used in LOSIRD to
provide the label and evidence. To the best of our knowledge, we are the first
one who combines objective facts and subjective views to verify rumor. The
experimental results on two real-world Twitter datasets showed that our model
exhibited the best performance in the early rumor detection task and its rumor
detection performance outperformed other baseline and state-of-the-art models.
Moreover, we confirmed that both objective information and subjective
information are fundamental clues for rumor detection.
