Papers with propaganda detection
Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data (D19-50)
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| Challenge: | Popular NLP tasks such as sentiment analysis and event extraction from social media are examples of imbalanced classification problems. |
| Approach: | They propose a method to generalise on dissimilar training and test data using a measure of similarity between datasets. |
| Outcome: | The proposed method achieves the second highest score on sentence-level propaganda classification. |
NSIT@NLP4IF-2019: Propaganda Detection from News Articles using Transfer Learning (D19-50)
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| Challenge: | In this paper, we describe our approach and system description for NLP4IF 2019 Workshop: Shared Task on Fine-Grained Propaganda Detection. |
| Approach: | They propose to use document Embeddings and LSTM to detect whether a sentence contains a propagandistic agenda. |
| Outcome: | The proposed approach ranked 21st in the NLP4IF 2019 Workshop: Shared Task on Fine-Grained Propaganda Detection. |
Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles (2024.lrec-main)
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| Challenge: | Using large language models (LLMs) to detect propaganda from text is a challenge for the development of sophisticated models. |
| Approach: | They propose to use a large propaganda dataset to identify propagandistic content in text, visual, or multimodal languages to improve their models. |
| Outcome: | The proposed model performs better on a large propaganda dataset than the existing models on skewed datasets. |
TWEETSPIN: Fine-grained Propaganda Detection in Social Media Using Multi-View Representations (2022.naacl-main)
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| Challenge: | Recent studies on propaganda detection involve document and fragment-level analyses of news articles. |
| Approach: | They propose a neural approach to detect and categorize propaganda tweets across fine-grained categories . they use a dataset containing tweets weakly annotated with different propaganda techniques . |
| Outcome: | The proposed method outperforms benchmark methods and transfers knowledge to low-resource news domains. |
HQP: A Human-Annotated Dataset for Detecting Online Propaganda (2024.findings-acl)
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| Challenge: | Existing datasets for detecting online propaganda use weak labels that can be noisy and incorrect. |
| Approach: | They propose a dataset for detecting online propaganda with high-quality labels . they show that state-of-the-art language models fail in detecting propaganda when trained with weak labels compared to prompt-based learning . |
| Outcome: | The proposed dataset is the first large-scale dataset for detecting online propaganda that was created through human annotation. |
Fine-Grained Analysis of Propaganda in News Article (D19-1)
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| Challenge: | Existing methods for detecting propaganda are noisy and lack of explainability. |
| Approach: | They propose to perform fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. |
| Outcome: | The proposed model outperforms several strong BERT-based baselines. |
Detecting Propaganda Techniques in Code-Switched Social Media Text (2023.emnlp-main)
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| Challenge: | a new study aims to detect propaganda in multiple languages using code-switching . social media platforms have made it easier for anyone to spread information to a wide audience . |
| Approach: | They propose to detect propaganda techniques in code-switched texts using a corpus of 1,030 texts . they propose to model multilinguality directly rather than using translation . |
| Outcome: | The proposed method combines different languages within the same text, presenting a challenge for automatic systems. |
NarratEX Dataset: Explaining the Dominant Narratives in News Texts (2025.findings-emnlp)
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Nuno Guimarães, Purificação Silvano, Ricardo Campos, Alipio Jorge, Ana Filipa Pacheco, Dimitar Iliyanov Dimitrov, Nikolaos Nikolaidis, Roman Yangarber, Elisa Sartori, Nicolas Stefanovitch, Preslav Nakov, Jakub Piskorski, Giovanni Da San Martino
| Challenge: | a dataset is created to explain the choice of the dominant narrative in a news article . the dataset is intended to address discourse polarization and propaganda detection . |
| Approach: | They propose a dataset for explaining the choice of the dominant narrative in a news article . the dataset is annotated manually with a dominant narrative and sub-narrative labels . |
| Outcome: | The proposed dataset is designed to explain the choice of the dominant narrative in a news article. |