AAFNDL - An Accurate Fake Information Recognition Model Using Deep Learning for the Vietnamese Language
Keywords:
social networking, computational modeling, deep learning, feature extraction, classification algorithms, fake news, BERT, TF-IDF, PhoBERTAbstract
On the Internet, "fake news" is a common phenomenon that frequently disturbs society because it contains intentionally false information. The issue has been actively researched using supervised learning for automatic fake news detection. Although accuracy is increasing, it is still limited to identifying fake information through channels on social platforms. This study aims to improve the reliability of fake news detection on social networking platforms by examining news from unknown domains. Especially, information on social networks in Vietnam is difficult to detect and prevent because everyone has equal rights to use the Internet for different purposes. These individuals have access to several social media platforms. Any user can post or spread the news through online platforms. These platforms do not attempt to verify users or the content of their locations. As a result, some users try to spread fake news through these platforms to propagate against an individual, a society, an organization, or a political party. In this paper, we proposed analyzing and designing a model for fake news recognition using Deep learning (called AAFNDL). The method to do the work is: 1) First, we analyze the existing techniques such as Bidirectional Encoder Representation from Transformer (BERT); 2) We proceed to build the model for evaluation; and finally, 3) We approach some Modern techniques to apply to the model, such as the Deep Learning technique, classifier technique and so on to classify fake information. Experiments show that our method can improve by up to 8.72% compared to other methods.References
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30. Shahi G.K., Nandini D. Fakecovid – A multilingual cross-domain fact check news dataset for COVID-19. CoRR, abs/2006.11343. 2020. 16 p. Available at: https://arxiv.org/abs/2006.11343.
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32. Kumar V., Kumar A., Singh A.K., Pachauri A. Fake news detection using machine learning and natural language processing. International Conference on Technological Advancements and Innovations (ICTAI). 2021. pp. 547–552.
33. Della Vedova M.L., Tacchini E., Moret S., Ballarin G., DiPierro M., de Alfaro L. Automatic online fake news detection combining content and social signals. 22nd Conference of Open Innovations Association (FRUCT). 2018. pp. 272–279.
34. Bian T., Xiao X., Xu T., Zhao P., Huang W., Rong Y., Huang J. Rumor detection on social media with bi-directional graph convolutional networks. AAAI Conference on Artificial Intelligence. 2020. DOI: 10.1609/AAAI.V34I01.5393.
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39. Sebastian D., Purnomo H.D., Sembiring I. Bert for natural language processing in bahasa Indonesia. 2nd International Conference on Intelligent Cybernetics Technology Applications (ICICyTA). 2022. pp. 204–209.
40. Holbert R.L. A typology for the study of entertainment television and politics. American Behavioral Scientist. 2005. vol. 49. no. 3. pp. 436–453.
41. Baptista J.P., Gradim A. A working definition of fake news. Encyclopedia. 2022. vol. 2. no. 1. pp. 632–645.
42. Farkas J., Schou J. Fake news as a floating signifier: Hegemony, antagonism and the politics of falsehood. Javnost-The Public. 2018. vol. 25. no. 3. pp. 298–314.
43. Thi T.-A.N., Vuong T.-H., Le T.-H., Phan X.-H., Le T.-T., Ha Q.-T. Knowledge base completion with transfer learning using bert and fasttext. 14th International Conference on Knowledge and Systems Engineering (KSE). 2022. pp. 1–6.
44. Nguyen Thi C.-V., Vuong T.-T., Le D.-T., Ha Q.-T. v3mfnd: A deep multi-domain multimodal fake news detection model for Vietnamese. Intelligent Information and Database Systems (Eds.: Nguyen N.T., Tran T.K., Tukayev U., Hong T.-P., Trawinski B., Szczerbicki E.). Cham: Springer International Publishing, 2022. pp. 608–620.
45. Pham N.-D., Le T.-H., Do T.-D., Vuong T.-T., Vuong T.-H., Ha Q.-T. Vietnamese fake news detection based on hybrid transfer learning model and TF-IDF. 13th International Conference on Knowledge and Systems Engineering (KSE). 2021. pp. 1–6.
46. Shahid W., Li Y., Staples D., Amin G., Hakak S., Ghorbani A. Are you a cyborg, bot or human? – a survey on detecting fake news spreaders. IEEE Access, 2022. vol. 10. pp. 27069–27083.
47. Wang C.-C. Fake news and related concepts: Definitions and recent research development. Contemporary Management Research. 2020. vol. 16. no. 3. pp. 145–174.
48. Umer M., Imtiaz Z., Ullah S., Mehmood A., Choi G.S., On B.-W. Fake news stance detection using deep learning architecture (CNN-LSTM). IEEE Access, 2020. vol. 8. pp. 156695–156706.
49. Abonizio H.Q., de Morais J.I., Tavares G.M., Barbon Junior V. Language-independent fake news detection: English, portuguese, and spanish mutual features. Future Internet. 2020. vol. 12. no. 5. Available at: https://www.mdpi.com/1999-5903/12/5/87.
50. Sayyadiharikandeh M., Varol O., Yang K.-C., Flammini A., Menczer F. Detection of novel social bots by ensembles of specialized classifiers. CoRR, abs/2006.06867. 2020. Available at: https://arxiv.org/abs/2006.06867.
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53. Qu Y., Liu P., Song W., Liu L., Cheng M. A text generation and prediction system: Pre-training on new corpora using BERT and GPT-2. IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC). 2020. pp. 323–326.
54. Du L., Hu C. Text similarity detection method of power customer service work order based on tfidf algorithm. IEEE 5th International Conference on Information Systems and Computer Aided Education (ICISCAE). 2022. pp. 978–982.
55. Nguyen D.Q., Nguyen A.T. PhoBERT: Pre-trained language models for Vietnamese in Findings of the Association for Computational Linguistics: EMNLP 2020. 2020. pp. 1037–1042.
56. Liu Y., Wu Y.-F.B. FNED: A deep network for fake news early detection on social media. ACM Trans. Inf. Syst. 2020. vol. 38. no. 3. DOI: 10.1145/3386253.
57. Fake news dataset. Available at: https://github.com/Hung1239/fake-news.git (accessed 02.05.2023).
58. Nguyen H., Dao T.N., Pham N.S., Dang T.L., Nguyen T.D., Truong T.H. An accurate viewport estimation method for 360 video streaming using deep learning. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems. 2022. vol. 9. no. 4. p. e2. DOI: 10.4108/eetinis.v9i4.2218.
59. Panda M., Mousa A.A.A., Hassanien A.E. Developing an efficient feature engineering and machine learning model for detecting iot-botnet cyber attacks. IEEE Access. 2021. vol. 9. pp. 91038–91052.
2. Apuke O.D., Omar B. Fake news and covid-19: modelling the predictors of fake news sharing among social media users. Telematics and Informatics. 2021. vol. 56. p. 101475. Available at: https: //www.sciencedirect.com/science/article/pii/S0736585320301349.
3. Nguyen H, Tan N., Quan N., Huong T., Phat N. Building a chatbot system to analyze opinions of english comments. Informatics and Automation. 2023. vol. 22. no. 2. pp. 289–315.
4. Yuslee N.S., Abdullah N.A.S. Fake news detection using naive bayes. IEEE 11th International Conference on System Engineering and Technology (ICSET). 2021. pp. 112–117.
5. Babar A., Jagtap N., Mithari A., Shukla A., Chaudhari P. A survey on fake news detection techniques and using a blockchain based system to combat fake news. International Journal of Computer Applications. 2020. vol. 176. no. 27. pp. 47–53.
6. Kaliyar R.K. Fake news detection using a deep neural network. 4th International Conference on Computing Communication and Automation (ICCCA). 2018. pp. 1–7.
7. Sastrawan I.K., Bayupati I., Arsa D.M.S. Detection of fake news using deep learning cnn–rnn based methods. ICT Express. 2022. vol. 8. no. 3. pp. 396–408.
8. Vinothkumar S., Varadhaganapathy S., Ramalingam M., Ramkishore D., Rithik S., Tharanies K. Fake news detection using svm algorithm in machine learning,” in 2022 International Conference on Computer Communication and Informatics (ICCCI). 2022. pp. 1–7.
9. Hussain M.G., Hasan M.R., Rahman M., Protim J., Hasan S.A. Detection of bangla fake news using mnb and svm classifier. 2020. 5 p. DOI: 10.1109/iCCECE49321.2020.9231167.
10. Hussain M.G., Hasan M.R., Rahman M., Protim J., Al Hasan S. Detection of bangla fake news using mnb and svm classifier. International Conference on Computing, Electronics Communications Engineering (iCCECE). 2020. pp. 81–85.
11. Aphiwongsophon S., Chongstitvatana P. Detecting fake news with machine learning method. 2018. pp. 528–531.
12. Mailjan Je.K., Kulikov A.A. [Analysis of fake news detection algorithms] Vserossijskaja konferencija molodyh issledovatelej s mezhdunarodnym uchastiem «Social'no-gumanitarnye problemy obrazovanija i professional'noj samorealizacii «Social'nyj inzhener-2020» [All-Russian Conference of Young Researchers with International Participation “Social and Humanitarian Problems of Education and Professional Self-Realization “Social Engineer-2020”]. 2020. pp. 204–209.
13. Vasil'kova V.V., Sadchikov D.I. [Fakes and bots as mechanisms of information distortion in social networks]. Kazanskij social'no-gumanitarnyj vestnik – Kazan Social and Humanitarian Bulletin. 2019. no. 2(37). pp. 24–30.
14. Tret'jakov A.O., Filatova O.G., Zhuk D.V., Gorlushkina N.N., Puchkovskaja A.A. [A method for detecting Russian-language fake news using elements of artificial intelligence]. International Journal of Open Information Technologies. 2018. vol. 6. no. 12. pp. 99–105.
15. Zhuk D.A., Zhuk D.V., Tret'jakov A.O. [Methods for detecting fake news in social networks using machine learning]. Informacionnye resursy Rossii – Information resources of Russia. 2018. no. 3(163). pp. 29–32.
16. Face news. Available at: https://en.wikipedia.org/wiki/Fake_news (accessed 10.02.2023).
17. Wang Y., Ma F., Jin Z., Yuan Y., Xun G., Jha K., Su L., Gao J. Eann: Event adversarial neural networks for multi-modal fake news detection. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. NY, USA: Association for Computing Machinery, 2018. p. 849–857. DOI: 10.1145/3219819.3219903.
18. Du J., Dou Y., Xia C., Cui L., Ma J., Yu P.S. Cross-lingual covid-19 fake news detection. International Conference on Data Mining Workshops (ICDMW). 2021. pp. 859–862. DOI: 10.1109/ICDMW53433.2021.00110.
19. Perez-Rosas V., Kleinberg B., Lefevre A., Mihalcea R. Automatic detection of fake news. Proceedings of the 27th International Conference on Computational Linguistics. Santa Fe, New Mexico, USA: Association for Computational Linguistics, 2018. pp. 3391–3401.
20. Sharma U., Saran S., Patil S. Fake news detection using machine learning algorithms. international journal of creative research thoughts – IJCRT. 2020. vol. 8(6). pp. 2320–2882.
21. Ahmed A.A.A., Aljabouh A., Donepudi P.K., Choi M.S. Detecting fake news using machine learning: A systematic literature review. Psychology and education. 2021. vol. 58(1). pp. 1932-1939.
22. Aldwairi M., Alwahedi A. Detecting fake news in social media networks. Procedia Computer Science. The 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-2018). 2018. vol. 141. pp. 215–222.
23. Hu L., Wei S., Zhao Z., Wu B. Deep learning for fake news detection: A comprehensive survey. AI Open. 2022. vol. 3. pp. 133–155. Available at: https://www.sciencedirect.com/science/article/pii/S2666651022000134.
24. Jose X., Kumar S.M., Chandran P. Characterization, classification and detection of fake news in online social media networks. 2021 IEEE Mysore Sub Section International Conference (MysuruCon). 2021. pp. 759–765.
25. Kato S., Yang L., Ikeda D. Domain bias in fake news datasets consisting of fake and real news pairs. 12th International Congress on Advanced Applied Informatics (IIAI-AAI). 2022. pp. 101–106.
26. Yu W., Ge J., Yang Z., Dong Y., Zheng Y., Dai H. Multi-domain fake news detection for history news environment perception. IEEE 17th Conference on Industrial Electronics and Applications (ICIEA). 2022. pp. 428–433.
27. Borkar T.H., Ahuja T. Comparative study of supervised learning algorithms for fake news classification. 6th International Conference on Trends in Electronics and Informatics (ICOEI). 2022. pp. 1405–1411.
28. Lu M.F., Renaldy, Ciptadi V., Nathanael R., Andaria K.S., Girsang A.S. Fake news classifier with deep learning. International Conference on Informatics Electrical and Electronics (ICIEE). 2022. pp. 1–4. DOI: 10.1109/ICIEE55596.2022.10010120.
29. Zubiaga A., Liakata M., Procter R. Exploiting context for rumour detection in social media. Social Informatics. (Eds: Ciampaglia G.L., Mashhadi A., Yasser T.). Cham: Springer International Publishing, 2017. pp. 109–123.
30. Shahi G.K., Nandini D. Fakecovid – A multilingual cross-domain fact check news dataset for COVID-19. CoRR, abs/2006.11343. 2020. 16 p. Available at: https://arxiv.org/abs/2006.11343.
31. Li Y., Jiang B., Shu K., Liu H. MM-COVID: A multilingual and multimodal data repository for combating COVID-19 disinformation. CoRR, abs/2011.04088. 2020. Available at: https://arxiv.org/abs/2011.04088.
32. Kumar V., Kumar A., Singh A.K., Pachauri A. Fake news detection using machine learning and natural language processing. International Conference on Technological Advancements and Innovations (ICTAI). 2021. pp. 547–552.
33. Della Vedova M.L., Tacchini E., Moret S., Ballarin G., DiPierro M., de Alfaro L. Automatic online fake news detection combining content and social signals. 22nd Conference of Open Innovations Association (FRUCT). 2018. pp. 272–279.
34. Bian T., Xiao X., Xu T., Zhao P., Huang W., Rong Y., Huang J. Rumor detection on social media with bi-directional graph convolutional networks. AAAI Conference on Artificial Intelligence. 2020. DOI: 10.1609/AAAI.V34I01.5393.
35. Sharma D.K., Shrivastava P., Garg S. Utilizing word embedding and linguistic features for fake news detection. 9th International Conference on Computing for Sustainable Global Development (INDIACom). 2022. pp. 844–848.
36. Trang N.T.M., Shcherbakov M. Vietnamese question answering system from multilingual bert models to monolingual BERT model. 9th International Conference System Modeling and Advancement in Research Trends (SMART). 2020. pp. 201–206.
37. Chinnalagu A., Durairaj A.K. Comparative analysis of BERT-base transformers and deep learning sentiment prediction models. 11th International Conference on System Modeling Advancement in Research Trends (SMART). 2022. pp. 874–879.
38. Min C., Ahn J., Lee T., Im D.-H. TK-BERT: Effective model of language representation using topic-based knowledge graphs. 17th International Conference on Ubiquitous Information Management and Communication (IMCOM). 2023. pp. 1–4.
39. Sebastian D., Purnomo H.D., Sembiring I. Bert for natural language processing in bahasa Indonesia. 2nd International Conference on Intelligent Cybernetics Technology Applications (ICICyTA). 2022. pp. 204–209.
40. Holbert R.L. A typology for the study of entertainment television and politics. American Behavioral Scientist. 2005. vol. 49. no. 3. pp. 436–453.
41. Baptista J.P., Gradim A. A working definition of fake news. Encyclopedia. 2022. vol. 2. no. 1. pp. 632–645.
42. Farkas J., Schou J. Fake news as a floating signifier: Hegemony, antagonism and the politics of falsehood. Javnost-The Public. 2018. vol. 25. no. 3. pp. 298–314.
43. Thi T.-A.N., Vuong T.-H., Le T.-H., Phan X.-H., Le T.-T., Ha Q.-T. Knowledge base completion with transfer learning using bert and fasttext. 14th International Conference on Knowledge and Systems Engineering (KSE). 2022. pp. 1–6.
44. Nguyen Thi C.-V., Vuong T.-T., Le D.-T., Ha Q.-T. v3mfnd: A deep multi-domain multimodal fake news detection model for Vietnamese. Intelligent Information and Database Systems (Eds.: Nguyen N.T., Tran T.K., Tukayev U., Hong T.-P., Trawinski B., Szczerbicki E.). Cham: Springer International Publishing, 2022. pp. 608–620.
45. Pham N.-D., Le T.-H., Do T.-D., Vuong T.-T., Vuong T.-H., Ha Q.-T. Vietnamese fake news detection based on hybrid transfer learning model and TF-IDF. 13th International Conference on Knowledge and Systems Engineering (KSE). 2021. pp. 1–6.
46. Shahid W., Li Y., Staples D., Amin G., Hakak S., Ghorbani A. Are you a cyborg, bot or human? – a survey on detecting fake news spreaders. IEEE Access, 2022. vol. 10. pp. 27069–27083.
47. Wang C.-C. Fake news and related concepts: Definitions and recent research development. Contemporary Management Research. 2020. vol. 16. no. 3. pp. 145–174.
48. Umer M., Imtiaz Z., Ullah S., Mehmood A., Choi G.S., On B.-W. Fake news stance detection using deep learning architecture (CNN-LSTM). IEEE Access, 2020. vol. 8. pp. 156695–156706.
49. Abonizio H.Q., de Morais J.I., Tavares G.M., Barbon Junior V. Language-independent fake news detection: English, portuguese, and spanish mutual features. Future Internet. 2020. vol. 12. no. 5. Available at: https://www.mdpi.com/1999-5903/12/5/87.
50. Sayyadiharikandeh M., Varol O., Yang K.-C., Flammini A., Menczer F. Detection of novel social bots by ensembles of specialized classifiers. CoRR, abs/2006.06867. 2020. Available at: https://arxiv.org/abs/2006.06867.
51. Wang R. Shi Y. Research on application of article recommendation algorithm based on word2vec and TFIDF. IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). 2022. pp. 454–457.
52. Devlin J., Chang M.-W., Lee K., Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. 2019. 16 p. DOI: 10.48550/arXiv.1810.04805.
53. Qu Y., Liu P., Song W., Liu L., Cheng M. A text generation and prediction system: Pre-training on new corpora using BERT and GPT-2. IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC). 2020. pp. 323–326.
54. Du L., Hu C. Text similarity detection method of power customer service work order based on tfidf algorithm. IEEE 5th International Conference on Information Systems and Computer Aided Education (ICISCAE). 2022. pp. 978–982.
55. Nguyen D.Q., Nguyen A.T. PhoBERT: Pre-trained language models for Vietnamese in Findings of the Association for Computational Linguistics: EMNLP 2020. 2020. pp. 1037–1042.
56. Liu Y., Wu Y.-F.B. FNED: A deep network for fake news early detection on social media. ACM Trans. Inf. Syst. 2020. vol. 38. no. 3. DOI: 10.1145/3386253.
57. Fake news dataset. Available at: https://github.com/Hung1239/fake-news.git (accessed 02.05.2023).
58. Nguyen H., Dao T.N., Pham N.S., Dang T.L., Nguyen T.D., Truong T.H. An accurate viewport estimation method for 360 video streaming using deep learning. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems. 2022. vol. 9. no. 4. p. e2. DOI: 10.4108/eetinis.v9i4.2218.
59. Panda M., Mousa A.A.A., Hassanien A.E. Developing an efficient feature engineering and machine learning model for detecting iot-botnet cyber attacks. IEEE Access. 2021. vol. 9. pp. 91038–91052.
Published
2023-07-06
How to Cite
Hung, N., Loi, T., Huong, N., Hang, T. T., & Huong, T. (2023). AAFNDL - An Accurate Fake Information Recognition Model Using Deep Learning for the Vietnamese Language. Informatics and Automation, 22(4), 795-825. https://doi.org/10.15622/ia.22.4.4
Section
Information Security
Copyright (c) Нгуен Вьет Хунг, Тран Куанг Лои, Нгуен Ти Хыонг, Тран Тхи Туй Ханг, Чыонг Тху Хыонг
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