Propaganda Detection And Challenges Managing Smart Cities Information On Social Media

Pir Noman Ahmad; Khalid Khan

doi:10.4108/eetsc.v7i2.2925

Authors

Pir Noman Ahmad School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Khalid Khan Computer Science and Software Engineering, University Of Stirling, UK

DOI:

https://doi.org/10.4108/eetsc.v7i2.2925

Keywords:

Machine translation, Span, linguistic, neural architectures, BiLSM

Abstract

Misinformation, false news, and various forms of propaganda have increased as a consequence of the rapid spread of information on social media. The Covid-19 spread deeply transformed citizens' day-to-day lives due to the overview of new methods of effort and access to facilities based on smart technologies. Social media propagandistic data and high-quality information on smart cities are the most challenging elements of this study. As a result of a natural language processing perspective, we have developed a system that automatically extracts information from bi-lingual sources. This information is either in Urdu or English (Ur or Eng), and we apply machine translation to obtain the target language. We explore different neural architectures and extract linguistic layout and relevant features in the bi-lingual corpus. Moreover, we fine-tune RoBERTa and ensemble BiLSM, CRF and BiRNN model. Our solution uses fine-tuned RoBERTa, a pretrained language model, to perform word-level classification. This paper provides insight into the model's learning abilities by analyzing its attention heads and the model's evaluation results.

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