  Fake tweets are observed to be ever-increasing, demanding immediate
countermeasures to combat their spread. During COVID-19, tweets with
misinformation should be flagged and neutralized in their early stages to
mitigate the damages. Most of the existing methods for early detection of fake
news assume to have enough propagation information for large labeled tweets --
which may not be an ideal setting for cases like COVID-19 where both aspects
are largely absent. In this work, we present ENDEMIC, a novel early detection
model which leverages exogenous and endogenous signals related to tweets, while
learning on limited labeled data. We first develop a novel dataset, called CTF
for early COVID-19 Twitter fake news, with additional behavioral test sets to
validate early detection. We build a heterogeneous graph with
follower-followee, user-tweet, and tweet-retweet connections and train a graph
embedding model to aggregate propagation information. Graph embeddings and
contextual features constitute endogenous, while time-relative web-scraped
information constitutes exogenous signals. ENDEMIC is trained in a
semi-supervised fashion, overcoming the challenge of limited labeled data. We
propose a co-attention mechanism to fuse signal representations optimally.
Experimental results on ECTF, PolitiFact, and GossipCop show that ENDEMIC is
highly reliable in detecting early fake tweets, outperforming nine
state-of-the-art methods significantly.
