Do You Speak Disinformation? Computational Detection of Deceptive News-Like Content Using Linguistic and Stylistic Features

Bibliographic Data

ID22010659
AuthorsNoëlle S Lebernegg (0000-0003-1120-6445, University of Vienna, corresponding author), Noëlle Lebernegg (University of Vienna), Jakob-Moritz Eberl (0000-0002-5613-760X, University of Vienna), Petro Tolochko (0000-0001-5971-8816, University of Vienna), Hajo Boomgaarden (0000-0002-5260-1284, University of Vienna)
Year2025
Volume13
Issue8
Pages1373-1398
Publication date2025-09-14
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueDigital Journalism (JOURNAL)
Journal identifiersISSN: 2167-0811 • E-ISSN: 2167-082X
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/21670811.2024.2305792
OpenAlexW4391836808
LanguageEN
Citations received4
References cited57

Amid growing concerns about the proliferation and belief in false or misleading information, the study addresses the need for automated detection in the public domain. It revisits and replicates scattered findings using a comprehensive, content-oriented, and feature-based approach. This method reliably identifies deceptive news-like content and highlights the importance of individual features in guiding the prediction algorithm. Employing explainable machine learning, the study explores content patterns for disinformation detection. Results from a tree-based approach on real-world data indicate that content-related characteristics can—when used in combination—facilitate the early detection of deceptive news-like articles. The study concludes by discussing the practical implications of computationally detecting the malicious language of disinformation

Disinformation · Fake news · Internet privacy · Linguistic analysis · Linguistics · Natural language processing · Political science · Social media · World Wide Web · Computer Science · Deception detection and forensic psychology · Misinformation and Its Impacts · Philosophy · Spam and Phishing Detection · Artificial Intelligence

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