Propaganda Detection And Challenges Managing Smart Cities Information On Social Media


  • 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 image/svg+xml



Machine translation, Span, linguistic, neural architectures, BiLSM


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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G. S. Jowett and V. O’donnell, Propaganda & persuasion. Sage publications, 2018.

G. D. S. Martino, S. Yu, A. Barrón-Cedeño, R. Petrov, and P. Nakov, “Fine-grained analysis of propaganda in news articles,” arXiv preprint arXiv:1910.02517, 2019. DOI:

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.

A. Vaswani et al., “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.

J. Devlin, R. Zbib, Z. Huang, T. Lamar, R. Schwartz, and J. Makhoul, “Fast and robust neural network joint models for statistical machine translation,” in proceedings of the 52nd annual meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2014, pp. 1370–1380. DOI:

E. Costales, “Identifying sources of innovation: Building a conceptual framework of the Smart City through a social innovation perspective,” Cities, vol. 120, p. 103459, 2022. DOI:

S. Zeng, Y. Hu, and C. Llopis-Albert, “Stakeholder-inclusive multi-criteria development of smart cities,” Journal of Business Research, vol. 154, p. 113281, 2023. DOI:

S. Ahmed, K. Hinkelmann, and F. Corradini, “Fact Checking: An Automatic End to End Fact Checking System,” in Combating Fake News with Computational Intelligence Techniques, Springer, 2022, pp. 345–366. DOI:

A. Ali, M. F. Pasha, O. H. Fang, R. Khan, M. A. Almaiah, and A. K. Al Hwaitat, “Big Data Based Smart Blockchain for Information Retrieval in Privacy-Preserving Healthcare System,” in Big Data Intelligence for Smart Applications, Y. Baddi, Y. Gahi, Y. Maleh, M. Alazab, and L. Tawalbeh, Eds. Cham: Springer International Publishing, 2022, pp. 279–296. doi: 10.1007/978-3-030-87954-9_13. DOI:

K. A. B. Ahmad, H. Khujamatov, N. Akhmedov, M. Y. Bajuri, M. N. Ahmad, and A. Ahmadian, “Emerging trends and evolutions for smart city healthcare systems,” Sustainable Cities and Society, vol. 80, p. 103695, 2022. DOI:

Q. Li, H. Ji, and L. Huang, “Joint event extraction via structured prediction with global features,” in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2013, pp. 73–82.

M. Miwa and M. Bansal, “End-to-end relation extraction using lstms on sequences and tree structures,” arXiv preprint arXiv:1601.00770, 2016. DOI:

K. Clark, U. Khandelwal, O. Levy, and C. D. Manning, “What does bert look at? an analysis of bert’s attention,” arXiv preprint arXiv:1906.04341, 2019. DOI:

I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” Advances in neural information processing systems, vol. 27, 2014.

K. Cho et al., “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014. DOI:

Q. Li and H. Ji, “Incremental Joint Extraction of Entity Mentions and Relations.,” in ACL (1), 2014, pp. 402–412. DOI:

S. Singh, S. Riedel, B. Martin, J. Zheng, and A. McCallum, “Joint inference of entities, relations, and coreference,” in Proceedings of the 2013 workshop on Automated knowledge base construction, 2013, pp. 1–6. DOI:

H. Rashkin, E. Choi, J. Y. Jang, S. Volkova, and Y. Choi, “Truth of varying shades: Analyzing language in fake news and political fact-checking,” in Proceedings of the 2017 conference on empirical methods in natural language processing, 2017, pp. 2931–2937. DOI:

P. Gupta, H. Schütze, and B. Andrassy, “Table filling multi-task recurrent neural network for joint entity and relation extraction,” in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2016, pp. 2537–2547.

S. Afroz, M. Brennan, and R. Greenstadt, “Detecting hoaxes, frauds, and deception in writing style online,” in 2012 IEEE Symposium on Security and Privacy, 2012, pp. 461–475. DOI:

P. Juola, “Detecting stylistic deception,” in Proceedings of the Workshop on Computational Approaches to Deception Detection, 2012, pp. 91–96.

L. Zhou, D. P. Twitchell, T. Qin, J. K. Burgoon, and J. F. Nunamaker, “An exploratory study into deception detection in text-based computer-mediated communication,” in 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the, 2003, pp. 10-pp. DOI:

M. Miwa and Y. Sasaki, “Modeling joint entity and relation extraction with table representation,” in Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 2014, pp. 1858–1869. DOI:

L. Song, W. Zhang, S. SY Liao, and R. CW Kwok, “A critical analysis of the state-of-the-art on automated detection of deceptive behavior in social media,” 2012.

T. Solorio, R. Hasan, and M. Mizan, “A case study of sockpuppet detection in wikipedia,” in Proceedings of the Workshop on Language Analysis in Social Media, 2013, pp. 59–68.

F. Loia, “A Co-evolutionary Perspective on Data-driven Organization: Highlights from Smart Cities in the Covid-19 Era,” in Big Data and Decision-Making: Applications and Uses in the Public and Private Sector, Emerald Publishing Limited, 2023, pp. 181–201. DOI:

A. van Twist, E. Ruijer, and A. Meijer, “Smart cities & citizen discontent: A systematic review of the literature,” Government Information Quarterly, p. 101799, 2023. DOI:

W. Basmi, A. Boulmakoul, L. Karim, and A. Lbath, “Distributed and scalable platform architecture for smart cities complex events data collection: Covid19 pandemic use case,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 1, pp. 75–83, 2021. DOI:

H. Abusaada and A. Elshater, “COVID-19 challenge, information technologies, and smart cities: considerations for well-being,” International Journal of Community well-being, vol. 3, no. 3, pp. 417–424, 2020. DOI:

H. Ji, H. Deng, and J. Han, “Uncertainty reduction for knowledge discovery and information extraction on the world wide web,” Proceedings of the IEEE, vol. 100, no. 9, pp. 2658–2674, 2012. DOI:

G. Levchuk, M. Jackobsen, and B. Riordan, “Detecting misinformation and knowledge conflicts in relational data,” in Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 2014, vol. 9091, pp. 235–248. DOI:

M. Petrova and I. Tairov, “Solutions to Manage Smart Cities’ Risks in Times of Pandemic Crisis,” Risks, vol. 10, no. 12, p. 240, 2022. DOI:

A. I. Tahirkheli et al., “A survey on modern cloud computing security over smart city networks: Threats, vulnerabilities, consequences, countermeasures, and challenges,” Electronics, vol. 10, no. 15, p. 1811, 2021. DOI:

A. Sajid, S. W. Shah, and T. Magsi, “Comprehensive Survey on Smart Cities Architectures and Protocols,” EAI Endorsed Transactions on Smart Cities, vol. 6, no. 18, 2022. DOI:

A. Barrón-Cedeno, I. Jaradat, G. Da San Martino, and P. Nakov, “Proppy: Organizing the news based on their propagandistic content,” Information Processing & Management, vol. 56, no. 5, pp. 1849–1864, 2019. DOI:

A. Tundis, G. Mukherjee, and M. Mühlhäuser, “An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet,” Applied Sciences, vol. 11, no. 5, p. 2196, 2021. DOI:

O. Altiti, M. Abdullah, and R. Obiedat, “JUST at SemEval-2020 task 11: Detecting propaganda techniques using BERT pre-trained model,” in Proceedings of the Fourteenth Workshop on Semantic Evaluation, 2020, pp. 1749–1755. DOI:

S. Kausar, B. Tahir, and M. A. Mehmood, “ProSOUL: a framework to identify propaganda from online Urdu content,” IEEE access, vol. 8, pp. 186039–186054, 2020. DOI:

C. Shao, G. L. Ciampaglia, A. Flammini, and F. Menczer, “Hoaxy: A platform for tracking online misinformation,” in Proceedings of the 25th international conference companion on world wide web, 2016, pp. 745–750. DOI:

R. Torok, “Symbiotic radicalisation strategies: Propaganda tools and neuro linguistic programming,” 2015.

K. Ahmad, M. Maabreh, M. Ghaly, K. Khan, J. Qadir, and A. Al-Fuqaha, “Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges,” Computer Science Review, vol. 43, p. 100452, 2022. DOI:

Y. Liu et al., “Roberta: A robustly optimized bert pretraining approach,” arXiv preprint arXiv:1907.11692, 2019.

V. Balakrishnan, Z. Shi, C. L. Law, R. Lim, L. L. Teh, and Y. Fan, “A deep learning approach in predicting products’ sentiment ratings: a comparative analysis,” The Journal of Supercomputing, vol. 78, no. 5, pp. 7206–7226, 2022. DOI:

V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter,” arXiv preprint arXiv:1910.01108, 2019.

Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” Advances in neural information processing systems, vol. 32, 2019.

Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, “Albert: A lite bert for self-supervised learning of language representations,” arXiv preprint arXiv:1909.11942, 2019.

Z. Abbasiantaeb and S. Momtazi, “Text-based question answering from information retrieval and deep neural network perspectives: A survey,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 11, no. 6, p. e1412, 2021. DOI:

G. D. S. Martino, A. Barrón-Cedeño, and P. Nakov, “Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection,” arXiv preprint arXiv:1910.09982, 2019. DOI:

O. Troisi, G. Fenza, M. Grimaldi, and F. Loia, “Covid-19 sentiments in smart cities: The role of technology anxiety before and during the pandemic,” Computers in Human Behavior, vol. 126, p. 106986, 2022. DOI:

M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” IEEE transactions on Signal Processing, vol. 45, no. 11, pp. 2673–2681, 1997. DOI:

A. Graves, N. Jaitly, and A. Mohamed, “Hybrid speech recognition with deep bidirectional LSTM,” in 2013 IEEE workshop on automatic speech recognition and understanding, 2013, pp. 273–278. DOI:




How to Cite

P. N. Ahmad and K. Khan, “Propaganda Detection And Challenges Managing Smart Cities Information On Social Media”, EAI Endorsed Trans Smart Cities, vol. 7, no. 2, p. e2, Mar. 2023.