Propaganda Detection And Challenges Managing Smart Cities Information On Social Media

Ahmad, Pir Noman; Khan, Khalid S.

doi:10.4108/eetsc.v7i2.2925

Cited by 4 publications

(3 citation statements)

References 47 publications

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“…In their work, ref. [22] worked on bi-lingual dataset of tweets related to Smart City in Urdu and English. The authors have reported the best results using a fine-tuned RoBERTa model.…”

Section: Related Workmentioning

confidence: 99%

Empowering Propaganda Detection in Resource-Restraint Languages: A Transformer-Based Framework for Classifying Hindi News Articles

Chaudhari,

Pawar

2023

BDCC

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Misinformation, fake news, and various propaganda techniques are increasingly used in digital media. It becomes challenging to uncover propaganda as it works with the systematic goal of influencing other individuals for the determined ends. While significant research has been reported on propaganda identification and classification in resource-rich languages such as English, much less effort has been made in resource-deprived languages like Hindi. The spread of propaganda in the Hindi news media has induced our attempt to devise an approach for the propaganda categorization of Hindi news articles. The unavailability of the necessary language tools makes propaganda classification in Hindi more challenging. This study proposes the effective use of deep learning and transformer-based approaches for Hindi computational propaganda classification. To address the lack of pretrained word embeddings in Hindi, Hindi Word2vec embeddings were created using the H-Prop-News corpus for feature extraction. Subsequently, three deep learning models, i.e., CNN (convolutional neural network), LSTM (long short-term memory), Bi-LSTM (bidirectional long short-term memory); and four transformer-based models, i.e., multi-lingual BERT, Distil-BERT, Hindi-BERT, and Hindi-TPU-Electra, were experimented with. The experimental outcomes indicate that the multi-lingual BERT and Hindi-BERT models provide the best performance, with the highest F1 score of 84% on the test data. These results strongly support the efficacy of the proposed solution and indicate its appropriateness for propaganda classification.

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“…In their work, ref. [22] worked on bi-lingual dataset of tweets related to Smart City in Urdu and English. The authors have reported the best results using a fine-tuned RoBERTa model.…”

Section: Related Workmentioning

confidence: 99%

Empowering Propaganda Detection in Resource-Restraint Languages: A Transformer-Based Framework for Classifying Hindi News Articles

Chaudhari,

Pawar

2023

BDCC

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“…This study provides important practical implications for information technology societies struggling to improve information system expert defensive performances. First, the feature set contains almost all the features used in the related literature, such as identifying misinformation [3,44], political bias, clickbait, and satire. There is a great deal of early research in automatically identifying different writing styles and propaganda techniques employed by news sources [11].…”

Section: Practical Implicationmentioning

confidence: 99%

“…When a news organization's voice is heard by the public, it is a significant win because it raises the bar for freedom of speech and makes it possible for anybody to be heard. The news is inaccurate, spreads phony stories, and is propagandistic because of freedom of speech [3]. The reader needs to be well-versed in the subject area to spot propaganda.…”

Section: Introductionmentioning

confidence: 99%

Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data

et al. 2023

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Social media, fake news, and different propaganda strategies have all contributed to an increase in misinformation online during the past ten years. As a result of the scarcity of high-quality data, the present datasets cannot be used to train a deep-learning model, making it impossible to establish an identification. We used a natural language processing approach to the issue in order to create a system that uses deep learning to automatically identify propaganda in news items. To assist the scholarly community in identifying propaganda in text news, this study suggested the propaganda texts (ProText) library. Truthfulness labels are assigned to ProText repositories after being manually and automatically verified with fact-checking methods. Additionally, this study proposed using a fine-tuned Robustly Optimized BERT Pre-training Approach (RoBERTa) and word embedding using multi-label multi-class text classification. Through experimentation and comparative research analysis, we address critical issues and collaborate to discover answers. We achieved an evaluation performance accuracy of 90%, 75%, 68%, and 65% on ProText, PTC, TSHP-17, and Qprop, respectively. The big-data method, particularly with deep-learning models, can assist us in filling out unsatisfactory big data in a novel text classification strategy. We urge collaboration to inspire researchers to acquire, exchange datasets, and develop a standard aimed at organizing, labeling, and fact-checking.

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Semantic web-based propaganda text detection from social media using meta-learning

Ahmad,

Yuanchao,

Aurangzeb

et al. 2024

SOCA

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Propaganda Detection And Challenges Managing Smart Cities Information On Social Media

Cited by 4 publications

References 47 publications

Empowering Propaganda Detection in Resource-Restraint Languages: A Transformer-Based Framework for Classifying Hindi News Articles

Empowering Propaganda Detection in Resource-Restraint Languages: A Transformer-Based Framework for Classifying Hindi News Articles

Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data

Semantic web-based propaganda text detection from social media using meta-learning

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