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

HORÁK, Aleš, Radoslav SABOL, Ondřej HERMAN a Vít BAISA. Recognition of Propaganda Techniques in Newspaper Texts: Fusion of Content and Style Analysis. Expert Systems with Applications. Elsevier, 2024, roč. 2024, č. 251, s. 124085-124095. ISSN 0957-4174. Dostupné z: https://dx.doi.org/10.1016/j.eswa.2024.124085.

Základní údaje

• Originální název: Recognition of Propaganda Techniques in Newspaper Texts: Fusion of Content and Style Analysis
• Autoři: HORÁK, Aleš, Radoslav SABOL, Ondřej HERMAN a Vít BAISA.
• Vydání: Expert Systems with Applications, Elsevier, 2024, 0957-4174.

Další údaje

• Originální jazyk: angličtina
• Typ výsledku: Článek v odborném periodiku
• Obor: 10200 1.2 Computer and information sciences
• Stát vydavatele: Spojené státy
• Utajení: není předmětem státního či obchodního tajemství
• WWW: URL
• Impakt faktor: Impact factor: 8.500 v roce 2022
• Organizační jednotka: Fakulta informatiky
• Doi: http://dx.doi.org/10.1016/j.eswa.2024.124085
• Klíčová slova anglicky: propaganda; disinformation; manipulative techniques; text style analysis; benchmark dataset

Anotace

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.

Návaznosti

• LM2023062, projekt VaV: Název: Digitální výzkumná infrastruktura pro jazykové technologie, umění a humanitní vědy

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, LINDAT/CLARIAH-CZ - Digitální výzkumná infrastruktura pro jazykové technologie, umění a humanitní vědy