Unmasking Bias in News
This work addresses bias detection in news for media analysis, but it is incremental as it corroborates previous research and suggests improvements rather than introducing major breakthroughs.
The study tackled hyperpartisanship detection in news by comparing style and content features, finding that topic-related features performed better than stylistic ones, and showed competitive results using higher-length n-grams.
We present experiments on detecting hyperpartisanship in news using a 'masking' method that allows us to assess the role of style vs. content for the task at hand. Our results corroborate previous research on this task in that topic related features yield better results than stylistic ones. We additionally show that competitive results can be achieved by simply including higher-length n-grams, which suggests the need to develop more challenging datasets and tasks that address implicit and more subtle forms of bias.