  The proliferation of fake news, i.e., news intentionally spread for
misinformation, poses a threat to individuals and society. Despite various
fact-checking websites such as PolitiFact, robust detection techniques are
required to deal with the increase in fake news. Several deep learning models
show promising results for fake news classification, however, their black-box
nature makes it difficult to explain their classification decisions and
quality-assure the models. We here address this problem by proposing a novel
interpretable fake news detection framework based on the recently introduced
Tsetlin Machine (TM). In brief, we utilize the conjunctive clauses of the TM to
capture lexical and semantic properties of both true and fake news text.
Further, we use the clause ensembles to calculate the credibility of fake news.
For evaluation, we conduct experiments on two publicly available datasets,
PolitiFact and GossipCop, and demonstrate that the TM framework significantly
outperforms previously published baselines by at least $5\%$ in terms of
accuracy, with the added benefit of an interpretable logic-based
representation. Further, our approach provides higher F1-score than BERT and
XLNet, however, we obtain slightly lower accuracy. We finally present a case
study on our model's explainability, demonstrating how it decomposes into
meaningful words and their negations.
