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    <abstract>Linguistic Inquiry and Word Count (LIWC) provides auditable psycholinguistic categories that are widely used to interpret depression-related language, but its incremental predictive role in modern multimodal systems remains unclear. We evaluate LIWC across two English and three Chinese depression-related corpora under matched participant-level cross-validation. We ask whether LIWC improves classification and what any performance change reflects. Intact LIWC is compared with three fold-local substitutes: a PCA-rotated version that removes direct access to named category coordinates, a participant-shuffled version that preserves real LIWC profiles while breaking participant alignment, and a random-marginal version that preserves feature-wise distributions. Across multiple fixed representation contexts, the results provide limited evidence for stable LIWC gains under frozen, participant-level early fusion. None of the prespecified dataset-blocked contrasts survives multiple-comparison correction. A separate SBERT calibration produces larger observed intact-versus-shuffled and intact-versus-random separations, indicating that larger participant-aligned signals can produce correspondingly larger separations under the same procedure, while not resolving the five-corpus power limitation. LIWC remains useful as an auditable, corpus-conditioned interpretive layer. These conclusions should not be generalized to fine-tuned, sequence-aware, or end-to-end architectures.</abstract>
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%0 Conference Proceedings
%T Disentangling the Interpretive and Predictive Roles of LIWC: Controlled Substitution in Depression-Related Classification
%A Yeh, Hsiang-Chen
%A Zhao, Xiutian
%A Mahapatra, Aurosweta
%A Chandra, Shreeram Suresh
%A Boyd, Ryan L.
%A Sisman, Berrak
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yeh-etal-2026-disentangling
%X Linguistic Inquiry and Word Count (LIWC) provides auditable psycholinguistic categories that are widely used to interpret depression-related language, but its incremental predictive role in modern multimodal systems remains unclear. We evaluate LIWC across two English and three Chinese depression-related corpora under matched participant-level cross-validation. We ask whether LIWC improves classification and what any performance change reflects. Intact LIWC is compared with three fold-local substitutes: a PCA-rotated version that removes direct access to named category coordinates, a participant-shuffled version that preserves real LIWC profiles while breaking participant alignment, and a random-marginal version that preserves feature-wise distributions. Across multiple fixed representation contexts, the results provide limited evidence for stable LIWC gains under frozen, participant-level early fusion. None of the prespecified dataset-blocked contrasts survives multiple-comparison correction. A separate SBERT calibration produces larger observed intact-versus-shuffled and intact-versus-random separations, indicating that larger participant-aligned signals can produce correspondingly larger separations under the same procedure, while not resolving the five-corpus power limitation. LIWC remains useful as an auditable, corpus-conditioned interpretive layer. These conclusions should not be generalized to fine-tuned, sequence-aware, or end-to-end architectures.
%U https://aclanthology.org/2026.emnlp-main.1535/
%P 35037-35057

Markdown (Informal)

[Disentangling the Interpretive and Predictive Roles of LIWC: Controlled Substitution in Depression-Related Classification](https://aclanthology.org/2026.emnlp-main.1535/) (Yeh et al., EMNLP 2026)

• Disentangling the Interpretive and Predictive Roles of LIWC: Controlled Substitution in Depression-Related Classification (Yeh et al., EMNLP 2026)

ACL

• Hsiang-Chen Yeh, Xiutian Zhao, Aurosweta Mahapatra, Shreeram Suresh Chandra, Ryan L. Boyd, and Berrak Sisman. 2026. Disentangling the Interpretive and Predictive Roles of LIWC: Controlled Substitution in Depression-Related Classification. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 35037–35057, Budapest, Hungary. Association for Computational Linguistics.