Recognition of propaganda techniques in newspaper texts: Fusion of content and style analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ASJ4IZL2C" target="_blank" >RIV/00216208:11320/25:SJ4IZL2C - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14330/24:00135846

Výsledek na webu

<a href="https://www.webofscience.com/wos/woscc/summary/80725160-5a91-43b8-9a0c-c1eadd4658ae-fed151e5/relevance/1" target="_blank" >https://www.webofscience.com/wos/woscc/summary/80725160-5a91-43b8-9a0c-c1eadd4658ae-fed151e5/relevance/1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2024.124085" target="_blank" >10.1016/j.eswa.2024.124085</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Recognition of propaganda techniques in newspaper texts: Fusion of content and style analysis

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Recognition of propaganda techniques in newspaper texts: Fusion of content and style analysis

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LM2023062" target="_blank" >LM2023062: Digitální výzkumná infrastruktura pro jazykové technologie, umění a humanitní vědy</a><br>

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

EXPERT SYSTEMS WITH APPLICATIONS

ISSN

0957-4174

e-ISSN

1873-6793

Svazek periodika

251

Číslo periodika v rámci svazku

2024-10-01

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

1-11

Kód UT WoS článku

001235661700001

EID výsledku v databázi Scopus

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