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Traditional approaches to classify the political bias of news articles havefailed to generate accurate, generalizable results. Existing networks premisedon CNNs and DNNs lack a model to identify and extrapolate subtle indicators ofbias like word choice, context, and presentation. In this paper, we propose anetwork architecture that achieves human-level accuracy in assigning biasclassifications to articles. The underlying model is based on a novel MeshNeural Network (MNN),this structure enables feedback and feedforward synapticconnections between any two neurons in the mesh. The MNN ontains six networkconfigurations that utilize Bernoulli based random sampling, pre-trained DNNs,and a network modelled after the C. Elegans nematode. The model is trained onover ten-thousand articles scraped from AllSides.com which are labelled toindicate political bias. The parameters of the network are then evolved using agenetic algorithm suited to the feedback neural structure. Finally, the bestperforming model is applied to five popular news sources in the United Statesover a fifty-day trial to quantify political biases in the articles theydisplay. We hope our project can spur research into biological solutions forNLP tasks and provide accurate tools for citizens to understand subtle biasesin the articles they consume.

An Automated News Bias Classifier Using Caenorhabditis Elegans Inspired Recursive Feedback Network Architecture