Papers with propaganda techniques
CAUnLP at NLP4IF 2019 Shared Task: Context-Dependent BERT for Sentence-Level Propaganda Detection (D19-50)
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| Challenge: | Sentence-level and fragment-level propaganda detection tasks are more challenging compared to document-level detection. |
| Approach: | They propose to use context-dependent input pairs to fine-tune the pretrained propaganda detection BERT to better utilize document information. |
| Outcome: | The proposed system can detect propaganda on document-level, sentence-level and fragment-level. |
JUSTDeep at NLP4IF 2019 Task 1: Propaganda Detection using Ensemble Deep Learning Models (D19-50)
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| Challenge: | Detecting fake news is not well established yet, but it can be classified under several labels: false, biased, or framed to mislead the readers. |
| Approach: | They propose a deep learning model using BiLSTM, XGBoost, and BERT to detect propaganda using a corpus from a challenge. |
| Outcome: | The proposed model outperforms the baseline model on a dataset from the challenge NLP4IF 2019 . |
Detection of Propaganda Using Logistic Regression (D19-50)
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| Challenge: | Various propaganda techniques are used to manipulate peoples perspectives to foster a predetermined agenda. |
| Approach: | They propose a Logistic Regression-based tool that automatically classifies whether a sentence is propagandistic or not. |
| Outcome: | The proposed tool outperforms the baseline on linguistic and semantic features. |
Understanding BERT performance in propaganda analysis (D19-50)
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| Challenge: | Despite the challenging nature of the shared task, our pretrained BERT model scored 0.62 F1 on the test set and ranked third among 25 teams who participated in the contest. |
| Approach: | They propose to use a dataset to fine-tune a model for propaganda analysis at sentence level to determine whether a text is 'propaganda' and to examine false-positive cases. |
| Outcome: | The proposed model scored 0.62 F1 on the test set and ranked third among 25 teams who participated in the shared task. |
Prta: A System to Support the Analysis of Propaganda Techniques in the News (2020.acl-demos)
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Giovanni Da San Martino, Shaden Shaar, Yifan Zhang, Seunghak Yu, Alberto Barrón-Cedeño, Preslav Nakov
| Challenge: | recent events have brought the public attention to the dangers of online disinformation. |
| Approach: | a new tool helps users analyze propaganda using specific rhetorical and psychological techniques. a prta system identifies the spans in which propaganda techniques occur and compares them. |
| Outcome: | a new tool can analyze articles crawled on a regular basis and compare them on the basis of their use of propaganda techniques. |
Fine-tuning with Hierarchical Prompting for Robust Propaganda Classification Across Annotation Schemas (2026.findings-acl)
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Lukas Stähelin, Veronika Solopova, Max Upravitelev, David Kaplan, Premtim Sahitaj, Ariana Sahitaj, Charlott Jakob, Sebastian Möller, Vera Schmitt
| Challenge: | Propaganda detection in social media is challenging due to noisy, short texts and low annotation agreements. |
| Approach: | They propose a new intent-focused taxonomy of propaganda techniques and compare it against an established, higher-agreement schema. |
| Outcome: | The proposed taxonomy outperforms existing models and reveals methodological differences hidden in base models. |
Faking Fake News for Real Fake News Detection: Propaganda-Loaded Training Data Generation (2023.acl-long)
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| Challenge: | despite advances in detecting fake news, there is a sizable gap between machine-generated and human-authored fake news . a nave solution is to collect human-written news articles that contain inaccurate information by crawling untrustworthy news media. |
| Approach: | They propose a framework for generating training examples informed by the styles and strategies of human-authored propaganda. |
| Outcome: | The proposed framework improves detection of human-written disinformation by 3.62–7.69% on two public datasets. |
Large Language Models for Propaganda Span Annotation (2024.findings-emnlp)
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| Challenge: | Using propagandistic techniques to manipulate online audiences is increasing in recent years. |
| Approach: | They investigate whether Large Language Models (LLMs) such as GPT-4 can extract propagandistic spans and the potential of employing them to collect more cost-effective annotations. |
| Outcome: | The proposed model provides labels that have higher agreement with expert annotators and lead to specialized models that achieve state-of-the-art over an unseen Arabic testing set. |
Deconstruct, Diagnose, and Deliberate: A Protocol-Adaptive Role-Specific Multi-Agent Framework for Fake News Detection (2026.findings-acl)
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| Challenge: | Existing methods for fake news detection rely on monolithic verification methods . Existing approaches often yield ambiguous verdicts due to superficial processing . |
| Approach: | They propose a protocol-adaptive role-specific multi-agent framework that decomposes verification into factual, logical, and contextual dimensions. |
| Outcome: | The proposed framework outperforms baseline methods in both predictive accuracy and explanatory quality. |
ZenPropaganda: A Comprehensive Study on Identifying Propaganda Techniques in Russian Coronavirus-Related Media (2024.lrec-main)
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| Challenge: | a new classification scheme for automatic detection of propaganda techniques is proposed . the capabilities of algorithms increase the risks of propaganda impact on the audience . |
| Approach: | They propose a novel multi-level classification scheme for automatic detection of propaganda techniques. |
| Outcome: | The proposed classification scheme outperforms existing methods in a Russian dataset and provides a valuable resource for future research. |