Towards an Ontology for Propaganda Detection in News Articles

Hamilton, Kyle

doi:10.1007/978-3-030-80418-3_35

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Some existing propaganda news detection methods rely on sequential features heavily, such as bidirectional long short-term memory (Bi-LSTM), CLSTM-TMN (Wang et al , 2020) and convolutional neural networks (CNN) (Da San Martino et al , 2021; Hamilton, 2021). However, they emphasize the locality and sequentiality of words and thus have the limitations in capturing long-distance and nonconsecutive word interactions (Zhang et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Graph-based multi-information integration network with external news environment perception for Propaganda detection

Liu,

Ma,

et al. 2024

IJWIS

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Purpose Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection. Design/methodology/approach G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them. Findings G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively. Originality/value An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.

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