Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study ; volume:3 ; number:1 ; day:6 ; month:12 ; year:2024 ; pages:1-10 ; date:12.2024
npj mental health research ; 3, Heft 1 (6.12.2024), 1-10, 12.2024

Creator: Chandrasekaran, Ranganathan
Kotaki, Suhas
Nagaraja, Abhilash Hosaagrahaara

Contributor: SpringerLink (Online service)

DOI: 10.1038/s44184-024-00107-5

URN: urn:nbn:de:101:1-2502202129507.039278593871

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:25 AM CEST

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Chandrasekaran, Ranganathan
Kotaki, Suhas
Nagaraja, Abhilash Hosaagrahaara
SpringerLink (Online service)

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Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study

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