Politics of Questions in News: A Mixed-Methods Study of Interrogative Stances as Markers of Voice and Power
Not on the map. Why?
Abstract (arXiv)
Interrogatives in news discourse have been examined in linguistics and conversation analysis, but mostly in broadcast interviews and relatively small, often English-language corpora, while large-scale computational studies of news rarely distinguish interrogatives from declaratives or differentiate their functions. This paper brings these strands together through a mixed-methods study of the "Politics of Questions" in contemporary French-language digital news. Using over one million articles published between January 2023 and June 2024, we automatically detect interrogative stances, approximate their functional types, and locate textual answers when present, linking these quantitative measures to a qualitatively annotated subcorpus grounded in semantic and pragmatic theories of questions. Interrogatives are sparse but systematically patterned: they mainly introduce or organize issues, with most remaining cases being information-seeking or echo-like, while explicitly leading or tag questions are rare. Although their density and mix vary across outlets and topics, our heuristic suggests that questions are overwhelmingly taken up within the same article and usually linked to a subsequent answer-like span, most often in the journalist's narrative voice and less often through quoted speech. Interrogative contexts are densely populated with named individuals, organizations, and places, whereas publics and broad social groups are mentioned much less frequently, suggesting that interrogative discourse tends to foreground already prominent actors and places and thus exhibits strong personalization. We show how interrogative stance, textual uptake, and voice can be operationalized at corpus scale, and argue that combining computational methods with pragmatic and sociological perspectives can help account for how questioning practices structure contemporary news discourse.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 64 references that are in the map. First posted on arXiv 23 Mar 2026.
Builds on
5 of 64 references.
References · 5
- 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 870
- 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 723
- 2023GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformercited by 88
- 2020A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017cited by 23
- 2024CamemBERT 2.0: A Smarter French Language Model Aged to Perfectioncited by 14
Built on by
Not cited yet.