| 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. |
Similar Papers
On Sentence Representations for Propaganda Detection: From Handcrafted Features to Word Embeddings (D19-50)
Copied to clipboard
| Challenge: | The rise of fake and hyperpartisan news on social media and online news outlets calls for improved automatic detection of propaganda in texts. |
| Approach: | They propose to use handcrafted features and learn dense semantic representations to detect propaganda in sentence-level and with random undersampling of the majority class (non-propaganda) |
| Outcome: | The proposed system achieves a ranking of 10 among 25 participants, with 59.5 F1-score. |
JUSTDeep at NLP4IF 2019 Task 1: Propaganda Detection using Ensemble Deep Learning Models (D19-50)
Copied to clipboard
| 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 . |
Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles (2024.lrec-main)
Copied to clipboard
| 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. |
Fine-Grained Analysis of Propaganda in News Article (D19-1)
Copied to clipboard
| 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. |
Synthetic Propaganda Embeddings To Train A Linear Projection (D19-50)
Copied to clipboard
| Challenge: | Using contextualized token embeddings, we can extract features of propaganda from contextualized embeddnings without fine-tuning the large parameters of the base model. |
| Approach: | They propose a method for detecting fine-grained categories of propaganda in text by generating synthetically generated embeddings from pre-trained language models. |
| Outcome: | The proposed method is used in the first shared task in fine-grained propaganda detection at NLP4IF as Team Stalin. |
PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent (2025.coling-main)
Copied to clipboard
Jiateng Liu, Lin Ai, Zizhou Liu, Payam Karisani, Zheng Hui, Yi Fung, Preslav Nakov, Julia Hirschberg, Heng Ji
| Challenge: | Existing research on propaganda detection does not capture the motives behind the content or its broader impact. |
| Approach: | They propose a framework that dissects propaganda into techniques, arousal appeals, and underlying intent. |
| Outcome: | The proposed framework improves performance in a wide range of scenarios and can be used to identify and categorize propaganda techniques. |
NSIT@NLP4IF-2019: Propaganda Detection from News Articles using Transfer Learning (D19-50)
Copied to clipboard
| 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. |
Unleashing the Power of Discourse-Enhanced Transformers for Propaganda Detection (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing systems focused on the surface words, ignoring the linguistic structure of the texts. |
| Approach: | They propose to use discourse analysis to analyze paragraph-level and token-level classifications and propose a Transformer architecture that can be used to detect propaganda. |
| Outcome: | The proposed system improves on English and Russian texts and shows strong correlations between propaganda instances and discourse spans. |
Exploring the Usability of Persuasion Techniques for Downstream Misinformation-related Classification Tasks (2024.lrec-main)
Copied to clipboard
| Challenge: | systematically explore the predictive power of features derived from Persuasion Techniques detected in texts for different tasks of interest for media analysis. |
| Approach: | They propose a set of meaningful features aimed at capturing persuasiveness of a text . they also assess the discriminatory power of these features in different text classification tasks . |
| Outcome: | The proposed features can be applied to detecting mis/disinformation, fake news, propaganda, partisan news and conspiracy theories. |
Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model (D19-50)
Copied to clipboard
| Challenge: | a new task is needed to detect propaganda in news articles . the need for communication has increased in online social media platforms . a proposed solution to the problem of sentence-level propaganda classification is ranked 12th . |
| Approach: | They propose to build a binary classifier able to provide corresponding propaganda labels . their solution ranks 12th among 26 teams in the NLP4IF-2019 Shared Task SLC . |
| Outcome: | The proposed model outperforms baseline approach and the winning system on a similar task. |