The proliferation of misinformation and propaganda is a global challenge,
with profound effects during major crises such as the COVID-19 pandemic and the
Russian invasion of Ukraine. Understanding the spread of misinformation and its
social impacts requires identifying the news sources spreading false
information. While machine learning (ML) techniques have been proposed to
address this issue, ML models have failed to provide an efficient
implementation scenario that yields useful results. In prior research, the
precision of deployment in real traffic deteriorates significantly,
experiencing a decrement up to ten times compared to the results derived from
benchmark data sets. Our research addresses this gap by proposing a graph-based
approach to capture navigational patterns and generate traffic-based features
which are used to train a classification model. These navigational and
traffic-based features result in classifiers that present outstanding
performance when evaluated against real traffic. Moreover, we also propose
graph-based filtering techniques to filter out models to be classified by our
framework. These filtering techniques increase the signal-to-noise ratio of the
models to be classified, greatly reducing false positives and the computational
cost of deploying the model. Our proposed framework for the detection of
misinformation domains achieves a precision of 0.78 when evaluated in real
traffic. This outcome represents an improvement factor of over ten times over
those achieved in previous studies