  Recently, the term "fake news" has been broadly and extensively utilized for
disinformation, misinformation, hoaxes, propaganda, satire, rumors, click-bait,
and junk news. It has become a serious problem around the world. We present a
new system, FaNDS, that detects fake news efficiently. The system is based on
several concepts used in some previous works but in a different context. There
are two main concepts: an Inconsistency Graph and Energy Flow. The
Inconsistency Graph contains news items as nodes and inconsistent opinions
between them for edges. Energy Flow assigns each node an initial energy and
then some energy is propagated along the edges until the energy distribution on
all nodes converges. To illustrate FaNDS we use the original data from the Fake
News Challenge (FNC-1). First, the data has to be reconstructed in order to
generate the Inconsistency Graph. The graph contains various subgraphs with
well-defined shapes that represent different types of connections between the
news items. Then the Energy Flow method is applied. The nodes with high energy
are the candidates for being fake news. In our experiments, all these were
indeed fake news as we checked each using several reliable web sites. We
compared FaNDS to several other fake news detection methods and found it to be
more sensitive in discovering fake news items.
