Recognition of Propaganda Techniques in Newspaper Texts: Fusion of Content and Style Analysis

HORÁK, Aleš, Radoslav SABOL, Ondřej HERMAN and Vít BAISA. Recognition of Propaganda Techniques in Newspaper Texts: Fusion of Content and Style Analysis. Expert Systems with Applications. Elsevier, 2024, vol. 2024, No 251, p. 124085-124095. ISSN 0957-4174. Available from: https://dx.doi.org/10.1016/j.eswa.2024.124085.

Basic information

• Original name: Recognition of Propaganda Techniques in Newspaper Texts: Fusion of Content and Style Analysis
• Authors: HORÁK, Aleš, Radoslav SABOL, Ondřej HERMAN and Vít BAISA.
• Edition: Expert Systems with Applications, Elsevier, 2024, 0957-4174.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10200 1.2 Computer and information sciences
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 8.500 in 2022
• Organization unit: Faculty of Informatics
• Doi: http://dx.doi.org/10.1016/j.eswa.2024.124085
• Keywords in English: propaganda; disinformation; manipulative techniques; text style analysis; benchmark dataset

Abstract

Public texts aiming at reader manipulation for propaganda or disinformation purposes pose a significant threat to society. The ability to detect the presence of a specific manipulative technique in a text offers an informed warning to readers and guides them to carefully judge the actual statement. In this article, we address the problem of developing new models capable of analyzing newspaper articles for propagandistic features. We introduce a new large dataset of manipulative techniques obtained via gathering and human annotation of 8,646 newspaper articles in Czech, which represents one of the former Soviet influence area languages. The dataset allows both to train new methods to recognize propaganda and disinformation and offer a general comparable benchmark for the techniques. We evaluate the dataset against selected state-of-the-art machine learning approaches to provide high-performing baselines for detecting seventeen annotated manipulative techniques. We also present thorough measurements of inter-annotator agreements that approximate the difficulty level of each of the attributes. As a new finding, we propose a set of text style analysis features that lean on the assumption that each manipulation leads to a specific style pattern. We show that the style analysis improves the detection results for most of the manipulative techniques. The viability of the approach is also confirmed on the well-known QProp propaganda dataset, providing new state-of-the-art results.

Links

• LM2023062, research and development project: Name: Digitální výzkumná infrastruktura pro jazykové technologie, umění a humanitní vědy

Investor: Ministry of Education, Youth and Sports of the CR