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Tracking Autism Stigma in Italian Newspapers: A Longitudinal Analysis of Media Discourse (2016–2025)

Paper · 2026 · Ginevra Martinelli, Chiara Barattieri di San Pietro, Daniela Ovadia et al.

Summary

This study asks whether Italian press coverage of autism has kept moving toward less stigmatizing portrayals, and whether the COVID-19 pandemic interrupted that trend. The authors gathered 2,854 articles mentioning autism-related keywords from 18 Italian newspapers via Nexis Uni. They sampled three one-year windows: four years before the pandemic, its first year, and four years after. They built a layered NLP pipeline from off-the-shelf pretrained models, with no fine-tuning. Each layer targets one mechanism of stigma: hate speech detection for discriminatory language, emotion classification for prejudicial affect, NLI-based matching against known autism myths for stereotypes, and BERTopic for the thematic settings where stigma appears. Overt hostility proved rare. Emotional tone was mostly positive, and stereotypes declined or held steady. The pandemic caused only a short-lived setback, with anger rising and joy falling before both returned to earlier levels. The savant stereotype persisted, tied mainly to film and arts coverage. The pipeline is presented as reusable for monitoring stigma around other conditions and in other media.

Key points

  • Corpus: 2,854 articles averaging about 332 words each, retrieved with the keywords autistic*, autism* and Asperger. The three periods are T1 (2016–17), T2 (2020–21) and T3 (2024–25).
  • Models:
    • unitary toxic-bert, scoring seven hate categories at sentence level.
    • An Italian BERT emotion model (bert-base-italian-xxl, finetuned), scoring six emotions per paragraph.
    • mDeBERTa-v3-base-xnli-multilingual-nli-2mil7, measuring entailment against seven autism myths drawn from John et al. (2018).
    • BERTopic, which produced 74 initial topics, narrowed to a final set of 23.
  • Statistics: Kruskal–Wallis tests with Dunn's post-hoc comparisons, plus Spearman correlations between topics and stigma measures. All tests used FDR correction and were run in the R package rstatix.
  • Main findings:
    • Only 89 articles scored above 0.05 in any hate category.
    • Love rose steadily and sadness fell over time.
    • The madness and social-disengagement stereotypes decreased across all periods.
    • Introversion and rejection-blindness peaked temporarily during COVID.
    • The savant stereotype showed no change.
  • Limitations: Only traditional media were analyzed, with no social media. Model outputs were checked only by one evaluator inspecting roughly 5% of the data, with no formal error analysis. Autistic people were not involved in the study.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Social Sciences · 1,954 worksPsychology · 1,649 worksSociology and Political Science · 626 worksSocial Psychology · 330 worksCommunication · 104 works

🧭 Topics 1Research subjects and languages the work is about.Discourse analysis · 471 works

Cite

Cite as Ginevra Martinelli, Chiara Barattieri di San Pietro, Daniela Ovadia, Marta Bosia and Valentina Bambini. 2026. Tracking Autism Stigma in Italian Newspapers: A Longitudinal Analysis of Media Discourse (2016–2025). Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.healing-1.18

@inproceedings{martinelli2026tracking,
  title = {{Tracking Autism Stigma in Italian Newspapers: A Longitudinal Analysis of Media Discourse (2016–2025)}},
  author = {Ginevra Martinelli, Chiara Barattieri {di San Pietro}, Daniela Ovadia, Marta Bosia and Valentina Bambini},
  booktitle = {Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.healing-1.18},
  url = {https://doi.org/10.18653/v1/2026.healing-1.18}
}

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