NSIT@NLP4IF-2019: Propaganda Detection from News Articles using Transfer Learning

Aggarwal, Kartik; Sadana, Anubhav

doi:10.18653/v1/d19-5021

Cited by 9 publications

(4 citation statements)

References 12 publications

(5 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the paper presents preliminary classification results by achieving about 63% of accuracy by using the Neural Network classification. Using the same database, in [38] the authors exploited the BERT classification to identify single nonpropaganda rows by employing ELMo, BERT and RoBERTa approach, by achieving 79% recall, 66% precision and 60% f1-score.…”

Section: Online Propaganda Detectionmentioning

confidence: 99%

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

2021

View full text Add to dashboard Cite

Internet-based communication systems have become an increasing tool for spreading misinformation and propaganda. Even though there exist mechanisms that are able to track unwarranted information and messages, users made up different ways to avoid their scrutiny and detection. An example is represented by the mixed-code language, that is text written in an unconventional form by combining different languages, symbols, scripts and shapes. It aims to make more difficult the detection of specific content, due to its custom and ever changing appearance, by using special characters to substitute for alphabet letters. Indeed, such substitute combinations of symbols, which tries to resemble the shape of the intended alphabet’s letter, makes it still intuitively readable to humans, however nonsensical to machines. In this context, the paper explores the possibility of identifying propaganda in such mixed-code texts over the Internet, centred on a machine learning based approach. In particular, an algorithm in combination with a deep learning models for character identification is proposed in order to detect and analyse whether an element contains propaganda related content. The overall approach is presented, the results gathered from its experimentation are discussed and the achieved performances are compared with the related works.

show abstract

Section: Online Propaganda Detectionmentioning

confidence: 99%

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

2021

View full text Add to dashboard Cite

show abstract

“…In addition, it may also contain logical fallacies, such as misrepresenting someone’s opinion or introducing irrelevant data (Da San Martino et al , 2020). These propaganda could be hidden in news published by both mainstream and alternative outlets to influence people’s mindsets by spreading ideas, facts, or allegations (Aggarwal and Sadana, 2019). Due to the large number of resources and techniques, the manual tagging is very laborious (Xu et al , 2023a).…”

Section: Introductionmentioning

confidence: 99%

Graph-based multi-information integration network with external news environment perception for Propaganda detection

Liu,

Ma,

et al. 2024

IJWIS

View full text Add to dashboard Cite

Purpose Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection. Design/methodology/approach G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them. Findings G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively. Originality/value An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.

show abstract

“…Propaganda is biased information that deliberately propagates a particular ideology or political orientation (Aggarwal and Sadana, 2019). Propaganda aims to influence the public's mentality and emotions, targeting their reciprocation due to their personal beliefs (Jowett and O'donnell, 2018).…”

Section: Introductionmentioning

confidence: 99%

BPGC at SemEval-2020 Task 11: Propaganda Detection in News Articles with Multi-Granularity Knowledge Sharing and Linguistic Features Based Ensemble Learning

Patil

Singh

Agarwal

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

View full text Add to dashboard Cite

Propaganda spreads the ideology and beliefs of like-minded people, brainwashing their audiences, and sometimes leading to violence. SemEval 2020 Task-11 aims to design automated systems for news propaganda detection. Task-11 consists of two sub-tasks, namely, Span Identification -given any news article, the system tags those specific fragments which contain at least one propaganda technique and Technique Classification -correctly classify a given propagandist statement amongst 14 propaganda techniques. For sub-task 1, we use contextual embeddings extracted from pre-trained transformer models to represent the text data at various granularities and propose a multi-granularity knowledge sharing approach. For sub-task 2, we use an ensemble of BERT and logistic regression classifiers with linguistic features. Our results reveal that the linguistic features are the reliable indicators for covering minority classes in a highly imbalanced dataset.

show abstract

NSIT@NLP4IF-2019: Propaganda Detection from News Articles using Transfer Learning

Cited by 9 publications

References 12 publications

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

Graph-based multi-information integration network with external news environment perception for Propaganda detection

BPGC at SemEval-2020 Task 11: Propaganda Detection in News Articles with Multi-Granularity Knowledge Sharing and Linguistic Features Based Ensemble Learning

Contact Info

Product

Resources

About