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Most research on natural language processing treats bias as an absoluteconcept: Based on a (probably complex) algorithmic analysis, a sentence, anarticle, or a text is classified as biased or not. Given the fact that forhumans the question of whether a text is biased can be difficult to answer oris answered contradictory, we ask whether an "absolute bias classification" isa promising goal at all. We see the problem not in the complexity ofinterpreting language phenomena but in the diversity of socioculturalbackgrounds of the readers, which cannot be handled uniformly: To decidewhether a text has crossed the proverbial line between non-biased and biased issubjective. By asking "Is text X more [less, equally] biased than text Y?" wepropose to analyze a simpler problem, which, by its construction, is ratherindependent of standpoints, views, or sociocultural aspects. In such a model,bias becomes a preference relation that induces a partial ordering from leastbiased to most biased texts without requiring a decision on where to draw theline. A prerequisite for this kind of bias model is the ability of humans toperceive relative bias differences in the first place. In our research, weselected a specific type of bias in argumentation, the stance bias, anddesigned a crowdsourcing study showing that differences in stance bias areperceptible when (light) support is provided through training or visual aid.

Differential Bias: On the Perceptibility of Stance Imbalance in Argumentation