Study of Classifiers for the Identification of Fake News
Keywords:
online deception, misinformation, Fake news, disinformation, online social networks and information disorder.Abstract
A significant source of local and international news for millions of individuals, online social networking websites are rapidly growing. OSNs, on the contrary side, have two disadvantages. Despite the numerous benefits they provide, such as endless, simple communication and instant access to data and news, they can also come with a number of drawbacks and concerns. The spread of misleading information is one of their toughest obstacles. Fake news detection is a difficult & unsolved issue. Nevertheless, designing a solution is anything but straightforward given the unique characteristics and challenges of identifying bogus news on OSNs. Artificial intelligence (AI) techniques, on the other hand, are still incapable of overcoming this difficult obstacle. Even worse, by creating and disseminating false information, artificial intelligence (AI) systems like deep learning and machine learning are being abused to trick customers. Because the material is meant to closely mirror reality, it can occasionally be difficult to assess its reliability using AI alone and without support from outside sources. As a result, it's still very difficult to recognise bogus news automatically. This research seeks to offer a fundamental review of the methods currently used to recognise false news and prevent it from spreading over OSNs in addition to classifying fake news using several classifiers.
References
Ahmed, H. "Detection of online fake news using N-gram analysis and machine learning techniques." Conference paper, October 2017.
Khan, J. Y. "A benchmark study of machine learning models for online fake news detection." Machine Learning with Applications, 4, 2021, 100032.
Shu, K. "Studying fake news via network analysis: detection and mitigation," 2018.
Xu, K. "Detecting fake news over online social media via domain reputations and content understanding." Tsinghua Science and Technology, vol. 25, no. 1, 2020, pp. 20-27.
Aïmeur, S. "Fake news, disinformation and misinformation in social media: A review - social network analysis and Mining." SpringerLink, 09-Feb-2023. https://link.springer.com/article/10.1007/s13278-023-01028-5.
Molina, M. D. "‘Fake news’ is not simply false information: A concept explication and taxonomy of online content." American Behavioral Scientist, 65(2), 2021, 180-212.
Ahmed, H. "Intelligent secure and dependable systems in distributed and cloud environments." Proc. 1st Int. Conf. Intell. Secur. Dependable Syst. Distrib. Cloud Environ., vol. 10618, 2017, pp. 169-181.
Umer, M. "Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)." IEEE Access, vol. 8, 2020, pp. 156695-156706.
Shu, K. "FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media," 2018.
Collins, B. "Trends in combating fake news on social media–a survey." Journal of Information and Telecommunication, 5(2), 2021, pp. 247-266.
Jiang, T. "A Novel Stacking Approach for Accurate Detection of Fake News." IEEE Access, vol. 9, 2021, pp. 22626-22639. doi: 10.1109/access.2021.3056079.
Holan, D. "2016 Lie of the Year: Fake News." Politifact, Washington, DC, USA, 2016.
Sansonetti, G. "Unreliable Users Detection in Social Media: Deep Learning Techniques for Automatic Detection." IEEE Access, vol. 8, 2020, pp. 213154-213167. doi: 1109/access.2020.3040604.
Garcıa, S. A. "The impact of term fake news on the scientific community scientific performance and mapping in web of science." Social Sciences, vol. 9, no. 5, 2020.
Gilda, S. "Notice of violation of IEEE publication principles: Evaluating machine learning algorithms for fake news detection." Proc. IEEE 15th Student Conf. Res. Develop. (SCOReD), 2017, pp. 110-115.
Ghafari, S. "A Survey on Trust Prediction in Online Social Networks." IEEE Access, vol. 8, 2020, pp. 144292-144309. doi: 10.1109/access.2020.3009445.
Douglas. "News consumption and the new electronic media." 7e International Journal of Press/Politics, vol. 11, no.1, 2006, pp. 29–52.
Ghafari, M. "Social context-aware trust prediction: Methods for identifying fake news" in Web Information Systems Engineering—WISE 2018, Springer, 2018, pp. 161-177.
Mridha, M. F. "A Comprehensive Review on Fake News Detection with Deep Learning." IEEE Access, 2021.
Ksieniewicz, P. "Machine learning methods for fake news classification" in Intelligent Data Engineering and Automated Learning, Springer, Manchester, U.K., vol. 11872, 2019.
Seyam, A. "Deep Learning Models to Detect Online False Information: a Systematic Literature Review." The 7th Annual International Conference on, 2021.
Guo B. "The future of false information detection on social media: new perspectives and trends." ACM Comput Surv (CSUR) 53(4), 2020, pp. 1–36.
Hamdi, T. "A hybrid approach for fake news detection in Twitter based on user features and graph embedding." International conference on distributed computing and internet technology. Springer, Berlin, 2020, pp. 266–280.
W. Duan. "Weighted Naive Bayesian Classifier Model Based on Information Gain." Intelligent System Design and Engineering Application (ISDEA) 2010 International Conference on, vol. 2, 2010, pp. 819-822.
Vats, V. "A comparative analysis of unsupervised machine techniques for liver disease prediction." 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Louisville, KY, USA, 2018, pp. 486-489.
Aldwairi, M. "Detecting fake news in social media networks." Procedia Computer Science, vol. 141, 2018, pp. 215-222.
Sivaranjani, S. "Diabetes prediction using machine learning algorithms with feature selection and dimensionality reduction." 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). Vol. 1. IEEE, 2021.
Wong. "Almost all the traffic to fake news sites is from Facebook, new data show," 2016. D. M. J. Lazer. "The science of fake news." Science, vol. 359, no. 6380, 2018, pp. 1094–1096.
Lamont, P. "Paranormal belief and the avowal of prior scepticism." Theory and Psychology, vol. 17, 2007, pp. 681–696.
Sharaff, A. "Extra-tree classifier with metaheuristics approach for email classification." Proceedings Advances Computer Communication Computational Science. Springer, Singapore, 2019, pp. 189-197.
Agudelo, G. E. R. "Raising a model for fake news detection using machine learning in Python." Conference on e-Business, e-Services and e-Society, 2018, pp. 596-604.
Alsubaei, F. "IoMT-SAF: Internet of Medical Things security assessment framework." Internet Things 8, 2019, 100123.
Quinlan, J. R. "Learning decision tree classifiers." ACM Comput Surv (CSUR) 28(1), 1996, pp. 71–72.
Marwick, A. "Media manipulation and disinformation online." Data and Society Research Institute, 2017.
Anderson, C. A. "Perseverance of social theories: The role of explanation in the persistence of discredited information." Journal of Personality and Social Psychology, vol. 396, 1980, pp. 1037–1049.
Forne, B. D. "Robust fake news detection over time and attack." ACM Transactions on Intelligent Systems and Technology (TIST), vol. 11(1), 2019, pp. 1-23.
Thota, A. "Fake news detection: a deep learning approach." SMU Data Science Review, vol. 1(3), 2018, p. 10.
Punjabi, V. D. "Study on fake news recognition and detection methods using machine learning techniques." Webology. Available at: https://www.webology.org/abstract.php?id=1970.
Robb. "Anatomy of a fake news scandal." Rolling Stone, vol. 1301, 2017, pp. 28–33. Soll. "The long and brutal history of fake news." Politico Magazine, vol. 18, no. 12, 2016.
Pardeshi, S. M. "A study of sentiment analysis from text through social networking sites." Webology. Available at: http://webology.org/abstract.php?id=1948 (Accessed: November 9, 2022).
Ayers, M. S. "A theoretical review of the misinformation effect: Predictions from an activation-based memory model." Psychonomic Bulletin & Review, vol. 5, 1998, pp. 1–21.
Vosoughi, S. "The spread of true and false news online." Science, vol. 359, no. 6380, 2018, pp. 1146–1151.
Fazio, R. H. "Implicit measures in social cognition research: Their meaning and use." Annual Review of Psychology, vol. 54, 2003, pp. 297–327.
WEI Xiao-zhang. "Enhancement of K-Nearest Neighbour algorithm using information gain [J]." Computer engineering and applications, vol. 43, no. 19, 2007, pp. 188-191.
Aufderheide, P. "Media Literacy. A Report of the National Leadership Conference on Media Literacy." Aspen Institute, Communications and Society Program, 1993.
http://webology.org/abstract.php?id=1948 (Accessed: November 9, 2022).
Ayers, M. S., and L. M. Reder. "A theoretical review of the misinformation effect: Predictions from an activation-based memory model." Psychonomic Bulletin & Review, vol. 5, 1998, pp. 1–21.
Vosoughi, S., D. Roy, and S. Aral. "The spread of true and false news online." Science, vol. 359, no. 6380, 2018, pp. 1146–1151.
Fazio, R. H., and M. A. Olson. "Implicit measures in social cognition research: Their meaning and use." Annual Review of Psychology, vol. 54, 2003, pp. 297–327.
WEI Xiao-zhang and DOU Zeng-fa. "Enhancement of K-Nearest Neighbour algorithm using information gain[J]." Computer engineering and applications, vol. 43, no. 19, 2007, pp. 188-191.
Aufderheide, P. "Media Literacy. A Report of the National Leadership Conference on Media Literacy." Aspen Institute, Communications and Society Program, 1993.
