  False information and true information fact checking it, often co-exist in
social networks, each competing to influence people in their spread paths. An
efficient strategy here to contain false information is to proactively identify
if nodes in the spread path are likely to endorse false information (i.e.
further spread it) or refutation information (thereby help contain false
information spreading). In this paper, we propose SCARLET (truSt and
Credibility bAsed gRaph neuraL nEtwork model using aTtention) to predict likely
action of nodes in the spread path. We aggregate trust and credibility features
from a node's neighborhood using historical behavioral data and network
structure and explain how features of a spreader's neighborhood vary. Using
real world Twitter datasets, we show that the model is able to predict false
information spreaders with an accuracy of over 87%.
