Span identification and technique classification of propaganda in news articles

Li, Wei; Li, Shiqian; Liu, Chenhao; Lu, Longfei; Shi, Ziyu; Wen, Shiping

doi:10.1007/s40747-021-00393-y

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…In addition, it is also explored that fake-news propagation from news media is the main root of the generation of inter-religion conflicts ( Baugut & Neumann, 2020 ). More recently, a two-step system is introduced to identify the propaganda in news articles ( Li et al, 2021 ). Experiments on the 550 news articles exhibited that their model outperformed the state-of-the-art methods.…”

Section: Related Workmentioning

confidence: 99%

How to detect propaganda from social media? Exploitation of semantic and fine-tuned language models

Malik

Imran

Mamdouh

2023

PeerJ Computer Science

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Online propaganda is a mechanism to influence the opinions of social media users. It is a growing menace to public health, democratic institutions, and public society. The present study proposes a propaganda detection framework as a binary classification model based on a news repository. Several feature models are explored to develop a robust model such as part-of-speech, LIWC, word uni-gram, Embeddings from Language Models (ELMo), FastText, word2vec, latent semantic analysis (LSA), and char tri-gram feature models. Moreover, fine-tuning of the BERT is also performed. Three oversampling methods are investigated to handle the imbalance status of the Qprop dataset. SMOTE Edited Nearest Neighbors (ENN) presented the best results. The fine-tuning of BERT revealed that the BERT-320 sequence length is the best model. As a standalone model, the char tri-gram presented superior performance as compared to other features. The robust performance is observed against the combination of char tri-gram + BERT and char tri-gram + word2vec and they outperformed the two state-of-the-art baselines. In contrast to prior approaches, the addition of feature selection further improves the performance and achieved more than 97.60% recall, f1-score, and AUC on the dev and test part of the dataset. The findings of the present study can be used to organize news articles for various public news websites.

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Section: Related Workmentioning

confidence: 99%

How to detect propaganda from social media? Exploitation of semantic and fine-tuned language models

Malik

Imran

Mamdouh

2023

PeerJ Computer Science

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“…Li et al [42] developed a pre-trained BERT model to separate the problem into span identification and technique classification. In the experiment, Chaudhari et al [43] employed different supervised machine-learning methods that integrated a range of vectors and word embedding.…”

Section: Related Workmentioning

confidence: 99%

Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data

et al. 2023

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Social media, fake news, and different propaganda strategies have all contributed to an increase in misinformation online during the past ten years. As a result of the scarcity of high-quality data, the present datasets cannot be used to train a deep-learning model, making it impossible to establish an identification. We used a natural language processing approach to the issue in order to create a system that uses deep learning to automatically identify propaganda in news items. To assist the scholarly community in identifying propaganda in text news, this study suggested the propaganda texts (ProText) library. Truthfulness labels are assigned to ProText repositories after being manually and automatically verified with fact-checking methods. Additionally, this study proposed using a fine-tuned Robustly Optimized BERT Pre-training Approach (RoBERTa) and word embedding using multi-label multi-class text classification. Through experimentation and comparative research analysis, we address critical issues and collaborate to discover answers. We achieved an evaluation performance accuracy of 90%, 75%, 68%, and 65% on ProText, PTC, TSHP-17, and Qprop, respectively. The big-data method, particularly with deep-learning models, can assist us in filling out unsatisfactory big data in a novel text classification strategy. We urge collaboration to inspire researchers to acquire, exchange datasets, and develop a standard aimed at organizing, labeling, and fact-checking.

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Automatic Content Analysis Systems: Detecting Disinformation in Social Networks

Romanchuk,

Vysotska

2024

Qeios

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In the 21st century, the rapid rise of disinformation and propaganda has become a significant global issue, undermining democratic processes and socio-political institutions. Disinformation, defined as intentionally false or misleading information, aims to manipulate public opinion and cause economic harm. This paper explores the use of computational linguistics and machine learning methods to detect disinformation. Techniques such as text preprocessing, feature extraction, and classification algorithms (e.g., SVM, naive Bayes) are adapted for identifying fake news. Recent studies demonstrate the effectiveness of these methods in social media and news platforms, highlighting the importance of advanced models like GPT-4 in improving detection accuracy and combating the spread of disinformation.

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Span identification and technique classification of propaganda in news articles

Cited by 3 publications

References 18 publications

How to detect propaganda from social media? Exploitation of semantic and fine-tuned language models

How to detect propaganda from social media? Exploitation of semantic and fine-tuned language models

Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data

Automatic Content Analysis Systems: Detecting Disinformation in Social Networks

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