Differential Bias: On the Perceptibility of Stance Imbalance in Argumentation
Alonso Palomino, Martin Potthast, Khalid Al-Khatib, Benno Stein

TL;DR
This paper explores the perceptibility of stance bias in argumentation, proposing a relative bias model that accounts for sociocultural diversity and demonstrating that humans can perceive bias differences with support.
Contribution
It introduces a relative bias framework for argumentation, emphasizing perceptibility over absolute bias classification, and presents a crowdsourcing study on stance bias perception.
Findings
Humans can perceive stance bias differences with training or visual aids.
A relative bias model reduces the subjectivity in bias assessment.
Perception of bias is enhanced by supporting tools.
Abstract
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…
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Taxonomy
TopicsNatural Language Processing Techniques
