Paper: Linguistic Models for Analyzing and Detecting Biased Language

ACL ID P13-1162
Title Linguistic Models for Analyzing and Detecting Biased Language
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013

Unbiased language is a requirement for reference sources like encyclopedias and scientific texts. Bias is, nonetheless, ubiq- uitous, making it crucial to understand its nature and linguistic realization and hence detect bias automatically. To this end we analyze real instances of human edits de- signed to remove bias from Wikipedia ar- ticles. The analysis uncovers two classes of bias: framing bias, such as praising or perspective-specific words, which we link to the literature on subjectivity; and episte- mological bias, related to whether propo- sitions that are presupposed or entailed in the text are uncontroversially accepted as true. We identify common linguistic cues for these classes, including factive verbs, implicatives, hedges, and subjective inten- sifiers. These insights help ...