  Traditional approaches to classify the political bias of news articles have
failed to generate accurate, generalizable results. Existing networks premised
on CNNs and DNNs lack a model to identify and extrapolate subtle indicators of
bias like word choice, context, and presentation. In this paper, we propose a
network architecture that achieves human-level accuracy in assigning bias
classifications to articles. The underlying model is based on a novel Mesh
Neural Network (MNN),this structure enables feedback and feedforward synaptic
connections between any two neurons in the mesh. The MNN ontains six network
configurations that utilize Bernoulli based random sampling, pre-trained DNNs,
and a network modelled after the C. Elegans nematode. The model is trained on
over ten-thousand articles scraped from AllSides.com which are labelled to
indicate political bias. The parameters of the network are then evolved using a
genetic algorithm suited to the feedback neural structure. Finally, the best
performing model is applied to five popular news sources in the United States
over a fifty-day trial to quantify political biases in the articles they
display. We hope our project can spur research into biological solutions for
NLP tasks and provide accurate tools for citizens to understand subtle biases
in the articles they consume.
