SWEAT: Scoring Polarization of Topics across Different Corpora
This addresses the need for quantifying polarization in computational social sciences, though it appears incremental as it builds on existing embedding methods.
The authors tackled the problem of measuring viewpoint differences across corpora by proposing SWEAT, a statistical measure for computing topic polarization, and validated it with a case study.
Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences. In this paper, we propose the Sliced Word Embedding Association Test (SWEAT), a novel statistical measure to compute the relative polarization of a topical wordset across two distributional representations. To this end, SWEAT uses two additional wordsets, deemed to have opposite valence, to represent two different poles. We validate our approach and illustrate a case study to show the usefulness of the introduced measure.