Propaganda detection
13 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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Analysing state-backed propaganda websites: a new dataset and linguistic study
Latest papers
Large Language Models for Propaganda Detection
We evaluate the models' performance by assessing metrics such as $F1$ score, $Precision$, and $Recall$, comparing the results with the current state-of-the-art approach using RoBERTa.
Detecting Propaganda Techniques in Code-Switched Social Media Text
mbzuai-nlp/propaganda-codeswitched-text
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Yet, it is common to find a mix of multiple languages in social media communication, a phenomenon known as code-switching.
HQP: A Human-Annotated Dataset for Detecting Online Propaganda
(3) To address the cost of labeling, we extend our work to few-shot learning.
Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
usc-isi-i2/logical-fallacy-identification
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Our three-stage framework natively consolidates prior datasets and methods from existing tasks, like propaganda detection, serving as an overarching evaluation testbed.
IITD at the WANLP 2022 Shared Task: Multilingual Multi-Granularity Network for Propaganda Detection
sm354/mmgn
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In addition to finding the techniques, Subtask 2 further asks to identify the textual span for each instance of each technique that is present in the tweet; the task can be modeled as a sequence tagging problem.
ProtoTEx: Explaining Model Decisions with Prototype Tensors
anubrata/prototex
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ACL 2022
We present ProtoTEx, a novel white-box NLP classification architecture based on prototype networks.
Semi-supervised Stance Detection of Tweets Via Distant Network Supervision
lcs2-iiitd/sands
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Detecting and labeling stance in social media text is strongly motivated by hate speech detection, poll prediction, engagement forecasting, and concerted propaganda detection.
Dataset of Propaganda Techniques of the State-Sponsored Information Operation of the People's Republic of China
The digital media, identified as computational propaganda provides a pathway for propaganda to expand its reach without limit.
YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles
This paper also compares the performances of different deep learning model architectures, such as the Bi-LSTM, LSTM, BERT, and XGBoost models, on the detection of news promotion techniques.
CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection
This paper describes our participation in the SemEval-2020 task Detection of Propaganda Techniques in News Articles.
