Towards Ongoing Detection of Linguistic Bias on Wikipedia

Wikipedia is a critical platform for organizing and disseminating knowledge. One of the key principles of Wikipedia is neutral point of view (NPOV), so that bias is not injected into objective treatment of subject matter. As part of our research vision to develop resilient bias detection models that can self-adapt over time, we present in this paper our initial investigation of the potential of a cross-domain transfer learning approach to improve Wikipedia bias detection. The ultimate goal is to future-proof Wikipedia in the face of dynamic, evolving kinds of linguistic bias and adversarial manipulations intended to evade NPOV issues. We highlight the impact of incorporating evidence of bias from other subjectivity rich domains into further pre-training a BERT-based model, resulting in strong performance in comparison with traditional methods.