Hybrid Feature Extraction and Capsule Neural Network Model for Fake News Detection
DOI:
https://doi.org/10.56294/dm2023190Keywords:
Fake News, Preprocessing, Hybrid Feature Extractor, Capsule Neural Network, Fake News DetectionAbstract
The introduction and widespread use of social media has altered how information is generated and disseminated, along with the expansion of the Internet. Through social media, information is now more quickly, cheaply, and easily available. Particularly harmful content includes misinformation propagated by social media users, such as false news. Users find it simple to post comments and false information on social networks. Realising the difference between authentic and false news is the biggest obstacle. The current study's aim of identifying bogus news involved the deployment of a capsule neural network. However, with time, this technique as a whole learns how to report user accuracy. This paper offers a three-step strategy for spotting bogus news on social networks as a solution to this issue. Pre-processing is executed initially to transform unstrsuctrured data into a structured form. The second part of the project brought the HFEM (Combined Feature Extraction Model), which also revealed new relationships between themes, authors, and articles as well as undiscovered features of false news. based on a collection of traits that were explicitly and implicitly collected from text. This study creates a capsule neural network model in the third stage to concurrently understand how creators, subjects, and articles are presented. This work uses four performance metrics in evaluations of the suggested classification algorithm using on existing public data sets
References
1. Khan, J. Y., Khondaker, M., Islam, T., Iqbal, A., & Afroz, S. (2019). A benchmark study on machine learning methods for fake news detection. arXiv preprint arXiv:1905.04749.
2. Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter, 19(1), 22-36.
3. Zhou, X., & Zafarani, R. (2018). Fake news: A survey of research, detection methods, and opportunities. arXiv preprint arXiv:1812.00315, 2.
4. Oshikawa, R., Qian, J., & Wang, W. Y. (2018). A survey on natural language processing for fake news detection. arXiv preprint arXiv:1811.00770.
5. Bondielli, A., & Marcelloni, F. (2019). A survey on fake news and rumour detection techniques. Information Sciences, 497, 38-55.
6. Lillie, A. E., & Middelboe, E. R. (2019). Fake news detection using stance classification: A survey. arXiv preprint arXiv:1907.00181.
7. Reis, J. C., Correia, A., Murai, F., Veloso, A., & Benevenuto, F. (2019). Supervised learning for fake news detection. IEEE Intelligent Systems, 34(2), 76-81.
8. Rubin, V. L., Chen, Y., & Conroy, N. K. (2015). Deception detection for news: three types of fakes. Proceedings of the Association for Information Science and Technology, 52(1), 1-4.
9. Tacchini, E., Ballarin, G., Della Vedova, M. L., Moret, S., & de Alfaro, L. (2017). Some like it hoax: Automated fake news detection in social networks. arXiv preprint arXiv:1704.07506.
10. Pérez-Rosas, V., Kleinberg, B., Lefevre, A., & Mihalcea, R. (2017). Automatic detection of fake news. arXiv preprint arXiv:1708.07104.
11. Saikh, T., De, A., Ekbal, A., & Bhattacharyya, P. (2020). A deep learning approach for automatic detection of fake news. arXiv preprint arXiv:2005.04938.
12. Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., & Stein, B. (2017). A stylometric inquiry into hyperpartisan and fake news. arXiv preprint arXiv:1702.05638.
13. Goldani, M. H., Momtazi, S., & Safabakhsh, R. (2021). Detecting fake news with capsule neural networks. Applied Soft Computing, 101, 106991.
14. Ozbay, F. A., & Alatas, B. (2020). Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A: Statistical Mechanics and its Applications, 540, 123174.
15. Huancahuire-Vega S, Newball-Noriega EE, Rojas-Humpire R, Saintila J, Rodriguez-Vásquez M, Ruiz-Mamani PG, et al. Changes in Eating Habits and Lifestyles in a Peruvian Population during Social Isolation for the COVID-19 Pandemic. Journal of Nutrition and Metabolism 2021;2021. https://doi.org/10.1155/2021/4119620.
16. Cabrera-Aguilar E, Zevallos-Francia M, Morales-García M, Ramírez-Coronel AA, Morales-García SB, Sairitupa-Sanchez LZ, et al. Resilience and stress as predictors of work engagement: the mediating role of self-efficacy in nurses. Frontiers in Psychiatry 2023;14. https://doi.org/10.3389/fpsyt.2023.1202048.
17. Cayupe JC, Bernedo-Moreira DH, Morales-García WC, Alcaraz FL, Peña KBC, Saintila J, et al. Self-efficacy, organizational commitment, workload as predictors of life satisfaction in elementary school teachers: the mediating role of job satisfaction. Frontiers in Psychology 2023;14. https://doi.org/10.3389/fpsyg.2023.1066321.
18. Plaza-Ccuno JNR, Puri CV, Calizaya-Milla YE, Morales-García WC, Huancahuire-Vega S, Soriano-Moreno AN, et al. Physical Inactivity is Associated with Job Burnout in Health Professionals During the COVID-19 Pandemic. Risk Management and Healthcare Policy 2023;16:725-33. https://doi.org/10.2147/RMHP.S393311.
19. Huaman N, Morales-García WC, Castillo-Blanco R, Saintila J, Huancahuire-Vega S, Morales-García SB, et al. An Explanatory Model of Work-family Conflict and Resilience as Predictors of Job Satisfaction in Nurses: The Mediating Role of Work Engagement and Communication Skills. Journal of Primary Care and Community Health 2023;14. https://doi.org/10.1177/21501319231151380.
20. Dilas D, Flores R, Morales-García WC, Calizaya-Milla YE, Morales-García M, Sairitupa-Sanchez L, et al. Social Support, Quality of Care, and Patient Adherence to Tuberculosis Treatment in Peru: The Mediating Role of Nurse Health Education. Patient Preference and Adherence 2023;17:175-86. https://doi.org/10.2147/PPA.S391930.
21. Morales-García WC, Huancahuire-Vega S, Saintila J, Morales-García M, Fernández-Molocho L, Ruiz Mamani PG. Predictors of Intention to Vaccinate Against COVID-19 in a Peruvian Sample. Journal of Primary Care and Community Health 2022;13. https://doi.org/10.1177/21501319221092254.
22. Chura S, Saintila J, Mamani R, Ruiz Mamani PG, Morales-García WC. Predictors of Depression in Nurses During COVID-19 Health Emergency; the Mediating Role of Resilience: A Cross-Sectional Study. Journal of Primary Care and Community Health 2022;13. https://doi.org/10.1177/21501319221097075.
23. Marquez NM, Saintila J, Castellanos-Vazquez AJ, Dávila-Villavicencio R, Turpo-Chaparro J, Sánchez-Tarrillo JA, et al. Telehealth-based interventions on lifestyle, body mass index, and glucose concentration in university staff during the coronavirus disease 2019 pandemic: A pre-experimental study. Digital Health 2022;8. https://doi.org/10.1177/20552076221129719.
24. Soto CEH, Lizarme EAV. Administrative management and user satisfaction of tele-consultation in a FEBAN polyclinic in Lima. SCT Proceedings in Interdisciplinary Insights and Innovations 2024;2:217-217. https://doi.org/10.56294/piii2024217.
25. Huirse SAH, Panique JCA. Relationship Marketing and customer loyalty in the company Saga Falabella S.A. Cusco. SCT Proceedings in Interdisciplinary Insights and Innovations 2024;2:206-206. https://doi.org/10.56294/piii2024206.
26. Otero DL, Licourt MT. Clinical and genetic characterization of Duchenne Muscular Dystrophy. SCT Proceedings in Interdisciplinary Insights and Innovations 2024;2:221-221. https://doi.org/10.56294/piii2024221.
27. Gonzalez-Argote J, Castillo-González W. Update on the use of gamified educational resources in the development of cognitive skills. AG Salud 2024;2:41-41. https://doi.org/10.62486/agsalud202441.
28. Quiroz FJR, Gamarra NH. Psychometric evidence of the mobile dependence test in the young population of Lima in the context of the pandemic. AG Salud 2024;2:40-40. https://doi.org/10.62486/agsalud202440.
29. Trovat V, Ochoa M, Hernández-Runque E, Gómez R, Jiménez M, Correia P. Quality of work life in workers with disabilities in manufacturing and service companies. AG Salud 2024;2:43-43. https://doi.org/10.62486/agsalud202443.
30. Olguín-Martínez CM, Rivera RIB, Perez RLR, Guzmán JRV, Romero-Carazas R, Suárez NR, et al. Rescue of the historical-cultural heritage of the Yanesha: interculturality and inclusive education of the oral traditions. AG Multidisciplinar 2023;1:5-5. https://doi.org/10.62486/agmu20235.
31. Cuervo MED. Exclusive breastfeeding. Factors that influence its abandonment. AG Multidisciplinar 2023;1:6-6. https://doi.org/10.62486/agmu20236.
32. Auza-Santivañez JC, Lopez-Quispe AG, Carías A, Huanca BA, Remón AS, Condo-Gutierrez AR, et al. Work of the emergency system in polytraumatized patients transferred to the hospital. AG Multidisciplinar 2023;1:9-9. https://doi.org/10.62486/agmu20239.
33. Saavedra MOR. Revaluation of Property, Plant and Equipment under the criteria of IAS 16: Property, Plant and Equipment. AG Managment 2023;1:11-11. https://doi.org/10.62486/agma202311.
34. Solano AVC, Arboleda LDC, García CCC, Dominguez CDC. Benefits of artificial intelligence in companies. AG Managment 2023;1:17-17. https://doi.org/10.62486/agma202317.
35. Ríos-Quispe CF. Analysis of ABC Cost Systems. AG Managment 2023;1:12-12. https://doi.org/10.62486/agma202312.
36. Ledesma-Céspedes N, Leyva-Samue L, Barrios-Ledesma L. Use of radiographs in endodontic treatments in pregnant women. AG Odontologia 2023;1:3-3. https://doi.org/10.62486/agodonto20233.
37. Figueredo-Rigores A, Blanco-Romero L, Llevat-Romero D. Systemic view of periodontal diseases. AG Odontologia 2023;1:14-14. https://doi.org/10.62486/agodonto202314.
38. Millán YA, Montano-Silva RM, Ruiz-Salazar R. Epidemiology of oral cancer. AG Odontologia 2023;1:17-17. https://doi.org/10.62486/agodonto202317.
39. Granik, M., & Mesyura, V. (2017, May). Fake news detection using naive Bayes classifier. In 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON) (pp. 900-903). IEEE.
40. Ahmed, H., Traore, I., & Saad, S. (2017, October). Detection of online fake news using n-gram analysis and machine learning techniques. In International conference on intelligent, secure, and dependable systems in distributed and cloud environments (pp. 127-138). Springer, Cham.
41. Ruchansky, N., Seo, S., & Liu, Y. (2017, November). Csi: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 797-806).
42. Gilda, S. (2017, December). Notice of Violation of IEEE Publication Principles: Evaluating machine learning algorithms for fake news detection. In 2017 IEEE 15th student conference on research and development (SCOReD) (pp. 110-115). IEEE.
43. Parikh, S. B., & Atrey, P. K. (2018, April). Media-rich fake news detection: A survey. In 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (pp. 436-441). IEEE.
44. Zhou, X., Zafarani, R., Shu, K., & Liu, H. (2019, January). Fake news: Fundamental theories, detection strategies and challenges. In Proceedings of the twelfth ACM international conference on web search and data mining (pp. 836-837).
45. Manzoor, S. I., & Singla, J. (2019, April). Fake news detection using machine learning approaches: A systematic review. In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 230-234). IEEE.
46. Elhadad, M. K., Li, K. F., & Gebali, F. (2019, August). Fake news detection on social media: a systematic survey. In 2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) (pp. 1-8). IEEE.
47. Zhang, J., Dong, B., & Philip, S. Y. (2020, April). Fakedetector: Effective fake news detection with deep diffusive neural network. In 2020 IEEE 36th International Conference on Data Engineering (ICDE) (pp. 1826-1829). IEEE.
48. Rădescu, R. (2020, July). AUTOMATIC ADAPTIVE SYSTEM FOR FILTERING FAKE NEWS BY METHODS BASED ON ARTIFICIAL INTELLIGENCE USING MACHINE LEARNING. In Proceedings of EDULEARN20 Conference (Vol. 6, p. 7th).
Published
Issue
Section
License
Copyright (c) 2023 R. Uma Maheswari , N. Sudha (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
