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