Papers by Hubert Plisiecki
Interpretable Semantic Gradients in SSD: A PCA Sweep Approach and a Case Study on AI Discourse (2026.findings-acl)
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| Challenge: | Supervised Semantic Differential (SSD) is a mixed quantitative–interpretive method that models how text meaning varies with continuous individual-difference variables . currently no systematic method exists for choosing the number of retained components, introducing avoidable researcher degrees of freedom in the analysis pipeline. |
| Approach: | They propose a PCA sweep procedure that treats dimensionality selection as a joint criterion over representation capacity, gradient interpretability, and stability across nearby values of K. |
| Outcome: | The proposed method is based on a corpus of short posts about artificial intelligence written by Prolific participants who also completed Admiration and Rivalry narcissism scales. |