@inproceedings{nenchev-etal-2025-long,
title = "Long-Term Development of Attitudes towards Schizophrenia and Depression in Scientific Abstracts",
author = {Nenchev, Ivan and
Scheffler, Tatjana and
Raithel, Lisa and
Kara, Elif and
Wilck, Benjamin and
Rabe, Maren and
St{\"o}tzner, Philip and
Montag, Christiane},
editor = "Atwell, Katherine and
Biester, Laura and
Borah, Angana and
Dementieva, Daryna and
Ignat, Oana and
Kotonya, Neema and
Liu, Ziyi and
Wan, Ruyuan and
Wilson, Steven and
Zhao, Jieyu",
booktitle = "Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nlp4pi-1.9/",
doi = "10.18653/v1/2025.nlp4pi-1.9",
pages = "99--113",
ISBN = "978-1-959429-19-7",
abstract = "We present a study investigating the linguistic sentiment associated with schizophrenia and depression in research-based texts. To this end, we construct a corpus of over 260,000 PubMed abstracts published between 1975 and 2025, covering both disorders. For sentiment analysis, we fine-tune two sentence-transformer models using SetFit with a training dataset consisting of sentences rated for valence by psychiatrists and clinical psychologists. Our analysis identifies significant temporal trends and differences between the two conditions. While the mean positive sentiment in abstracts and titles increases over time, a more detailed analysis reveals a marked rise in both maximum negative and maximum positive sentiment, suggesting a shift toward more polarized language. Notably, sentiment in abstracts on schizophrenia is significantly more negative overall. Furthermore, an exploratory analysis indicates that negative sentences are disproportionately concentrated at the beginning of abstracts. These findings suggest that linguistic style in scientific literature is evolving. We discuss the broader ethical and societal implications of these results and propose recommendations for more cautious language use in scientific discourse."
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<abstract>We present a study investigating the linguistic sentiment associated with schizophrenia and depression in research-based texts. To this end, we construct a corpus of over 260,000 PubMed abstracts published between 1975 and 2025, covering both disorders. For sentiment analysis, we fine-tune two sentence-transformer models using SetFit with a training dataset consisting of sentences rated for valence by psychiatrists and clinical psychologists. Our analysis identifies significant temporal trends and differences between the two conditions. While the mean positive sentiment in abstracts and titles increases over time, a more detailed analysis reveals a marked rise in both maximum negative and maximum positive sentiment, suggesting a shift toward more polarized language. Notably, sentiment in abstracts on schizophrenia is significantly more negative overall. Furthermore, an exploratory analysis indicates that negative sentences are disproportionately concentrated at the beginning of abstracts. These findings suggest that linguistic style in scientific literature is evolving. We discuss the broader ethical and societal implications of these results and propose recommendations for more cautious language use in scientific discourse.</abstract>
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%0 Conference Proceedings
%T Long-Term Development of Attitudes towards Schizophrenia and Depression in Scientific Abstracts
%A Nenchev, Ivan
%A Scheffler, Tatjana
%A Raithel, Lisa
%A Kara, Elif
%A Wilck, Benjamin
%A Rabe, Maren
%A Stötzner, Philip
%A Montag, Christiane
%Y Atwell, Katherine
%Y Biester, Laura
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Ignat, Oana
%Y Kotonya, Neema
%Y Liu, Ziyi
%Y Wan, Ruyuan
%Y Wilson, Steven
%Y Zhao, Jieyu
%S Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 978-1-959429-19-7
%F nenchev-etal-2025-long
%X We present a study investigating the linguistic sentiment associated with schizophrenia and depression in research-based texts. To this end, we construct a corpus of over 260,000 PubMed abstracts published between 1975 and 2025, covering both disorders. For sentiment analysis, we fine-tune two sentence-transformer models using SetFit with a training dataset consisting of sentences rated for valence by psychiatrists and clinical psychologists. Our analysis identifies significant temporal trends and differences between the two conditions. While the mean positive sentiment in abstracts and titles increases over time, a more detailed analysis reveals a marked rise in both maximum negative and maximum positive sentiment, suggesting a shift toward more polarized language. Notably, sentiment in abstracts on schizophrenia is significantly more negative overall. Furthermore, an exploratory analysis indicates that negative sentences are disproportionately concentrated at the beginning of abstracts. These findings suggest that linguistic style in scientific literature is evolving. We discuss the broader ethical and societal implications of these results and propose recommendations for more cautious language use in scientific discourse.
%R 10.18653/v1/2025.nlp4pi-1.9
%U https://aclanthology.org/2025.nlp4pi-1.9/
%U https://doi.org/10.18653/v1/2025.nlp4pi-1.9
%P 99-113
Markdown (Informal)
[Long-Term Development of Attitudes towards Schizophrenia and Depression in Scientific Abstracts](https://aclanthology.org/2025.nlp4pi-1.9/) (Nenchev et al., NLP4PI 2025)
ACL
- Ivan Nenchev, Tatjana Scheffler, Lisa Raithel, Elif Kara, Benjamin Wilck, Maren Rabe, Philip Stötzner, and Christiane Montag. 2025. Long-Term Development of Attitudes towards Schizophrenia and Depression in Scientific Abstracts. In Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI), pages 99–113, Vienna, Austria. Association for Computational Linguistics.