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Enriching Conventional Ensemble Learner with Deep Contextual Semantics to Detect Fake News in Urdu

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Published:10 November 2021Publication History
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Abstract

Increased connectivity has contributed greatly in facilitating rapid access to information and reliable communication. However, the uncontrolled information dissemination has also resulted in the spread of fake news. Fake news might be spread by a group of people or organizations to serve ulterior motives such as political or financial gains or to damage a country’s public image. Given the importance of timely detection of fake news, the research area has intrigued researchers from all over the world. Most of the work for detecting fake news focuses on the English language. However, automated detection of fake news is important irrespective of the language used for spreading false information. Recognizing the importance of boosting research on fake news detection for low resource languages, this work proposes a novel semantically enriched technique to effectively detect fake news in Urdu—a low resource language. A model based on deep contextual semantics learned from the convolutional neural network is proposed. The features learned from the convolutional neural network are combined with other n-gram-based features and are fed to a conventional majority voting ensemble classifier fitted with three base learners: Adaptive Boosting, Gradient Boosting, and Multi-Layer Perceptron. Experiments are performed with different models, and results show that enriching the traditional ensemble learner with deep contextual semantics along with other standard features shows the best results and outperforms the state-of-the-art Urdu fake news detection model.

REFERENCES

  1. Aggarwal Akshay, Chauhan Aniruddha, Kumar Deepika, Mittal Mamta, and Verma Sharad. 2020. Classification of fake news by fine-tuning deep bidirectional transformers based language model. EAI Endorsed Transactions on Scalable Information Systems 7, 17 (2020), 1–12.Google ScholarGoogle Scholar
  2. Akram Qurat-ul-Ain, Naseer Asma, and Hussain Sarmad. 2009. Assas-band, an affix-exception-list based Urdu stemmer. In Proceedings of the 7th Workshop on Asian Language Resources. 4046.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Al-Ash Herley Shaori and Wibowo Wahyu Catur. 2018. Fake news identification characteristics using named entity recognition and phrase detection. In Proceedings of the 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE’18). IEEE, Los Alamitos, CA, 1217.Google ScholarGoogle ScholarCross RefCross Ref
  4. Alanazi Sarah Saleh and Khan Muhammad Badruddin. 2020. Arabic fake news detection in social media using readers’ comments: Text mining techniques in action. International Journal of Computer Science and Network Security 20, 9 (2020), 29–35.Google ScholarGoogle Scholar
  5. Amjad Maaz, Sidorov Grigori, Zhila Alisa, Gómez-Adorno Helena, Voronkov Ilia, and Gelbukh Alexander. 2020. “Bend the truth”: Benchmark dataset for fake news detection in Urdu language and its evaluation. Journal of Intelligent & Fuzzy Systems.Preprint (2020), 113.Google ScholarGoogle Scholar
  6. Bahad Pritika, Saxena Preeti, and Kamal Raj. 2019. Fake news detection using bi-directional LSTM-recurrent neural network. Procedia Computer Science 165 (2019), 7482.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Zia Haris Bin, Raza Agha Ali, and Athar Awais. 2018. Urdu word segmentation using conditional random fields (CRFs). In Proceedings of the 27th International Conference on Computational Linguistics.25622569. http://aclweb.org/anthology/C18-1217.Google ScholarGoogle Scholar
  8. Blake Aaron. 2018. A new study suggests fake news might have won Donald Trump the 2016 election. Washington Post. Retrieved October 17, 2021 from https://www.washingtonpost.com/news/the-fix/wp/2018/04/03/a-new-study-suggests-fake-news-might-have-won-donald-trump-the-2016-election/.Google ScholarGoogle Scholar
  9. Chen Tong, Li Xue, Yin Hongzhi, and Zhang Jun. 2018. Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 4052.Google ScholarGoogle ScholarCross RefCross Ref
  10. Chopra Sahil, Jain Saachi, and Sholar John Merriman. 2017. Towards automatic identification of fake news: Headline-article stance detection with LSTM attention models. In Stanford CS224d Deep Learning for NLP Final Project.Google ScholarGoogle Scholar
  11. Fernández Jacobo López and Ramırez Juan Antonio López. 2020. Approaches to the profiling fake news spreaders on Twitter task in English and Spanish. In Proceedings of the 2020 Conference and Labs of the Evaluation Forum (CLEF’20).Google ScholarGoogle Scholar
  12. Goldman Russell. 2016. Reading fake news, Pakistani minister directs nuclear threat at Israel. New York Times. Retrieved October 17, 2021 from https://www.nytimes.com/2016/12/24/world/asia/pakistan-israel-khawaja-asif-fake-news-nuclear.html.Google ScholarGoogle Scholar
  13. Jardaneh Ghaith, Abdelhaq Hamed, Buzz Momen, and Johnson Douglas. 2019. Classifying Arabic Tweets based on credibility using content and user features. In Proceedings of the 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT’19). IEEE, Los Alamitos, CA, 596601.Google ScholarGoogle ScholarCross RefCross Ref
  14. Kuzmin Gleb, Larionov Daniil, Pisarevskaya Dina, and Smirnov Ivan. 2020. Fake news detection for the Russian language. In Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM’20). 4557.Google ScholarGoogle Scholar
  15. Long Yunfei. 2017. Fake News Detection Through Multi-Perspective Speaker Profiles. Association for Computational Linguistics.Google ScholarGoogle Scholar
  16. Ma Jing, Gao Wei, and Wong Kam-Fai. 2017. Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning. Association for Computational Linguistics.Google ScholarGoogle Scholar
  17. Ma Jing, Gao Wei, and Wong Kam-Fai. 2018. Rumor Detection on Twitter with Tree-Structured Recursive Neural Networks. Association for Computational Linguistics.Google ScholarGoogle Scholar
  18. Mikolov Tomas, Chen Kai, Corrado Greg, and Dean Jeffrey. 2013. Efficient estimation of word representations in vector space. Arxiv Preprint arXiv:1301.3781 (2013).Google ScholarGoogle Scholar
  19. Monti Federico, Frasca Fabrizio, Eynard Davide, Mannion Damon, and Bronstein Michael M.. 2019. Fake news detection on social media using geometric deep learning. Arxiv Preprint arXiv:1902.06673 (2019).Google ScholarGoogle Scholar
  20. Nagoudi El Moatez Billah, Elmadany AbdelRahim, Abdul-Mageed Muhammad, Alhindi Tariq, and Cavusoglu Hasan. 2020. Machine generation and detection of Arabic manipulated and fake news. Arxiv Preprint arXiv:2011.03092 (2020).Google ScholarGoogle Scholar
  21. Ott Myle, Choi Yejin, Cardie Claire, and Hancock Jeffrey T.. 2011. Finding deceptive opinion spam by any stretch of the imagination. Arxiv Preprint arXiv:1107.4557 (2011).Google ScholarGoogle Scholar
  22. Posadas-Durán Juan-Pablo, Gómez-Adorno Helena, Sidorov Grigori, and Escobar Jesús Jaime Moreno. 2019. Detection of fake news in a new corpus for the Spanish language. Journal of Intelligent & Fuzzy Systems 36, 5 (2019), 48694876.Google ScholarGoogle ScholarCross RefCross Ref
  23. Qian Feng, Gong Chengyue, Sharma Karishma, and Liu Yan. 2018. Neural user response generator: Fake news detection with collective user intelligence. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18), Vol. 18. 38343840.Google ScholarGoogle ScholarCross RefCross Ref
  24. Rashkin Hannah, Choi Eunsol, Jang Jin Yea, Volkova Svitlana, and Choi Yejin. 2017. 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. 29312937.Google ScholarGoogle ScholarCross RefCross Ref
  25. Ruchansky Natali, Seo Sungyong, and Liu Yan. 2017. CSI: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM Conference on Information and Knowledge Management. 797806.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Shu Kai, Bernard H. Russell, and Liu Huan. 2019a. Studying fake news via network analysis: Detection and mitigation. In Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining. Springer, 4365.Google ScholarGoogle ScholarCross RefCross Ref
  27. Shu Kai, Sliva Amy, Wang Suhang, Tang Jiliang, and Liu Huan. 2017. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter 19, 1 (2017), 2236.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Shu Kai, Wang Suhang, and Liu Huan. 2018. Understanding user profiles on social media for fake news detection. In Proceedings of the 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR’18). IEEE, Los Alamitos, CA, 430435.Google ScholarGoogle ScholarCross RefCross Ref
  29. Shu Kai, Wang Suhang, and Liu Huan. 2019b. Beyond news contents: The role of social context for fake news detection. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. 312320.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Tacchini Eugenio, Ballarin Gabriele, Vedova Marco L. Della, Moret Stefano, and Alfaro Luca de. 2017. Some like it hoax: Automated fake news detection in social networks. Arxiv Preprint arXiv:1704.07506 (2017).Google ScholarGoogle Scholar
  31. Vogel Inna and Jiang Peter. 2019. Fake news detection with the new German dataset “GermanFakeNC.” In Proceedings of the International Conference on Theory and Practice of Digital Libraries. 288295.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Volkova Svitlana, Shaffer Kyle, Jang Jin Yea, and Hodas Nathan. 2017. Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on Twitter. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 647653.Google ScholarGoogle ScholarCross RefCross Ref
  33. Vosoughi Soroush, Roy Deb, and Aral Sinan. 2018. The spread of true and false news online. Science 359, 6380 (2018), 11461151.Google ScholarGoogle ScholarCross RefCross Ref
  34. Wang William Yang. 2017. “Liar, liar pants on fire”: A new benchmark dataset for fake news detection. Arxiv Preprint arXiv:1705.00648 (2017).Google ScholarGoogle Scholar
  35. Yang Shuo, Shu Kai, Wang Suhang, Gu Renjie, Wu Fan, and Liu Huan. 2019. Unsupervised fake news detection on social media: A generative approach. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 56445651.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Zetter Kim. 2008. Six-year-old news story causes United Airlines stock to plummet—UPDATE Google placed wrong date on story. Wired. Retrieved October 17, 2021 from https://www.wired.com/2008/09/six-year-old-st/.Google ScholarGoogle Scholar
  37. Zhou Xinyi and Zafarani Reza. 2018. Fake news: A survey of research, detection methods, and opportunities. Arxiv Preprint arXiv:1812.00315 2 (2018).Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 21, Issue 1
      January 2022
      442 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3494068
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      New York, NY, United States

      Publication History

      • Published: 10 November 2021
      • Accepted: 1 April 2021
      • Revised: 1 February 2021
      • Received: 1 November 2020
      Published in tallip Volume 21, Issue 1

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