Long-Term Development of Attitudes towards Schizophrenia and Depression in Scientific Abstracts (2025)

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.

## introduction according to the icd-10, schizophrenic disorders (f20-f29) are defined by "fundamental and characteristic distortions of thinking and perception, and affects that are inappropriate or blunted," while recurrent depressive disorder is characterized by "repeated episodes of depression as described for depressive episode (f32.-), without any history of independent episodes of mood elevation and increased energy (mania)" @xcite . research articles on these conditions often begin with sentences like: "schizophrenia is among the most severe and debilitating of psychiatric disorders" @xcite , and "major depression is a common illness that severely limits psychosocial functioning and diminishes quality of life" @xcite . what is striking about such sentences is that they tend to convey a negative sentiment and pessimism. importantly, neither schizophrenia nor depression are invariably linked to a poor prognosis. although some people experience persistent symptoms, others have a more favorable course. in the case of schizophrenia for example, a subset of patients experiences only a single psychotic episode, followed by stable remission @xcite , while others achieve functional recovery with appropriate interventions, including pharmacological and psychosocial treatments @xcite . the prognosis is influenced by a variety of factors, including early intervention @xcite , adherence to treatment @xcite and psychotherapy @xcite , social support, and individual resilience @xcite . schizophrenia and depression are both classified as severe mental illnesses and are among the most extensively studied psychiatric conditions in biomedical research. despite this com-mon ground, emerging evidence suggests a divergence in societal perception: while public stigma surrounding depression has declined in recent decades-reflecting growing awareness and acceptance-schizophrenia continues to be associated with persistent or even increasing stigma @xcite . the way schizophrenia and depression are described in the scientific literature may play a crucial role in shaping public and professional perceptions of these disorders. different linguistic framings emphasize distinct aspects of the conditions, influencing attitudes toward prognosis, treatment, and stigma. the examples presented above highlight how variations in language can convey different sentiments about the nature and course of these disorders. scientific publications serve as a primary medium for disseminating objective knowledge about psychiatric conditions, yet the language used in these texts can shape both clinical practice and public discourse. over time, shifts in linguistic style and sentiment within academic literature may reflect broader developments in scientific understanding, medical advancements, and societal attitudes toward mental illness. recent advances in natural language processing (nlp) enable the analysis of large linguistic datasets in a systematic and replicable manner, allowing researchers to uncover patterns of linguistic representation that may not be immediately apparent in individual texts. in this study, we analyze the linguistic style of scientific publications over the past 50 years, with a particular focus on sentiment, to examine how perspectives on schizophrenia and depression have evolved. ## materials and methods in the following section, we describe the pubmed corpus construction, the fine-tuning procedure for sentiment estimation, and the statistical analysis of the results. ## results the results from the beta regression models on the sentiment scores extracted on the sentence level of the abstracts are summarized in a beta regression on the mean negative sentiment values from model 1 (sentencetransformers/all-mpnet-base-v2) revealed a significant positive association between schizophrenia and negative sentiment (β = .048, @xmath0.002, @xmath1.6, @xmath2.001), with a slight reduction over time (β = -.004, @xmath3.000, @xmath4.41, @xmath5.001). we found a corresponding effect in the mean positive sentiment values from the model. the beta regression for mean negative sentiment scores obtained with model 2 (baai/bge-small-en-v1.5) revealed a significant negative association between the centralized year variable and negative sentiment (β = -.002, @xmath6.000, @xmath7.19, @xmath8.001), although the magnitude was smaller. schizophrenia was associated with more negative sentiment (β = .031, @xmath9.003, @xmath10.73, @xmath11.001). a beta regression on the maximum negative sentiment from model 1 (sentence-transformers/allmpnet-base-v2) showed a positive effect of centralized years (β = .004, @xmath12.000, @xmath13.53, @xmath14.01) and schizophrenia (β = 0.031, @xmath15.003, @xmath16.5, @xmath17.001). the odds ration for negative sentiment is 1.004, meaning that each year the probability for negative sentiment increases with 0.4%. similarly, the beta regression for maximum positive sentiment indicated a significant positive effect of centralized years (β = .008, @xmath18.000, @xmath19.76, @xmath20.001), while schizophrenia was significantly negatively associated (β = -0.05, @xmath21.002, @xmath22.005, @xmath23.001). the log odds from model 2 show the same pattern. in addition, we examined the relationship between negative sentiment and relative position within abstracts, using average values for each year and position. based on the visual exploration of the heatmap plot shown in figure 3 , we fitted an ols model with a cubic term for position. the model explained 34% of the variance in sentiment scores (@xmath24.34, f(3, 1016) = 174.5, @xmath25.001). the linear term (β = -.0390, @xmath26.001) indicated a general decrease in sentiment across positions, while the quadratic (β = .01, @xmath27.001) and cubic (β = -.0007, @xmath28.001) terms suggest a more complex curvature. ## discussion in this study, we analyzed sentiment related to schizophrenia and depression in a large corpus of pubmed abstracts published over the past 50 years. to achieve this, we adopted a domain-specific approach and fine-tuned two sentence-transformer models using the setfit python package. our training dataset was derived from sentences in the lancet seminar series on schizophrenia and depression, which had been rated by psychiatrists and clinical psychologists. to minimize potential bias in both model fine-tuning and sentiment estimation, we excluded the tokens "schizophrenia" and "depression" from the linguistic data. our findings offer several key insights into sentiment trends. first, texts about schizophrenia tend to have a more negative tone. research abstracts focused on schizophrenia exhibit significantly more negative sentiment across all regression models, even when "schizophrenia" and "depression" were excluded from both the training data and the linguistic data used for evaluation. at this stage, we cannot provide a definitive explanation for this pattern, but several possibilities emerge. one possibility is that the language associated with schizophrenia -for example in relation to symptoms such as delusions and hallucinations -is inherently more negative. another explanation could be that certain research areas, such as treatment options and psychotherapy, could be more frequently studied in the context of depression, may be evaluated as less negative by the models. additionally, linguistic style may reflect broader societal imbalances in perceptions of mental illness. there is evidence suggesting increasing acceptance of depression, while stigma toward schizophrenia continues to rise @xcite . second, in terms of temporal analysis, we observed a significant increase of the mean positive sentiment for both titles and abstracts, suggesting that, on average, the tone of scientific writing has become more positive. our results on the trend appear to align with previous work @xcite @xcite @xcite despite the different methodological approaches. however, an in-depth analysis of the sentences revealed an interesting trend in extreme sentiment values: both maximum negative and positive sentiment have increased over time. the language has become more polarized, potentially reflecting a broader shift toward more passionate and assertive expressions in scientific discourse. this shift may be driven by the growing emphasis on research questions and the need to highlight the significance of findings. additionally, our exploratory analysis showed that negative sentiment is often concentrated at the beginning of an abstract, while the most positive sentences tend to appear at the end. this pattern aligns with the findings of @xcite , who observed that positive words are more frequent toward the end of abstracts. it also reflects the conventional structure of scientific abstracts, where researchers typically introduce a problem or knowledge gap at the outset and conclude with positive statements about solutions, contributions, or future research directions. this structural tendency mirrors the natural progression of scientific inquiry, from identifying a problem to presenting novel solutions. our findings are particularly relevant in the context of psychological constructs such as negativity bias and negativity dominance @xcite , which describe how negative information tends to carry more weight than positive information. additionally, they align with the work of @xcite , who demonstrated that the sequential order of presenting information influences recall-specifically, that negative statements introduced first are more likely to be remembered. this raises the possibility that the sentences with negative sentiment within abstracts may overshadow their overall positive tone. further research is needed to investigate how this shift in sentiment may impact impression formation, particularly among medical professionals, researchers, and the general public. a crucial question is whether this bias in sentiment could shape how scientific findings are interpreted, potentially influencing attitudes, clinical decision-making, and research priorities.