FNED: Integrated Learning-Based Fake News Inspection
DOI:
https://doi.org/10.54097/knrwgd63Keywords:
Ensemble Detection, FakeNews Detection, Logistic Regression, Word2vec.Abstract
This paper addresses the challenge of inadequate detection accuracy and generalization in false news detection amid the rapid spread of misinformation in the news environment, proposing FNED, an integrated learning-based false detection method. The method enhances detection performance by integrating multiple models; it employs a combination of three models—KNN, LSTM, and XGBoost—utilizing a stacking technique to leverage their predictions as features, followed by logistic regression as a meta-classifier for comprehensive evaluation. Augment the model's resilience across varying data distributions. Experiments utilize the public FakeNewsNet standard dataset, revealing that FNED enhances accuracy by approximately 1.3% relative to previous approaches, indicating superior detection capability and generalization performance.
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[1] QIU X, OLIVEIRA D F M, SHIRAZI A S, et al. RETRACTION: Limited individual attention and online virality of low-quality information (Retraction of Vol 1, art no 0132, 2017) [J]. Nature Human Behaviour, 2019, 3(1): 102-.
[2] JORDAN M I, MITCHELL T M. Machine learning: Trends, perspectives, and prospects [J]. Science, 2015, 349(6245): 255-60.
[3] GUERA D, DELP E J. Deepfake Video Detection Using Recurrent Neural Networks [J]. 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2018: 1-6.
[4] CONROY N, RUBIN V L, CHEN Y. Automatic deception detection: Methods for finding fake news [J]. Proceedings of the Association for Information Science and Technology, 2015, 52.
[5] SHARMA U, SARAN S, PATIL S M. Fake news detection using machine learning algorithms [J]. International Journal of creative research thoughts (IJCRT), 2020, 8(6): 509-18.
[6] KHANAM Z, ALWASEL B, SIRAFI H, et al. Fake news detection using machine learning approaches; proceedings of the IOP conference series: materials science and engineering, F, 2021 [C]. IOP Publishing.
[7] PANDEY S, PRABHAKARAN S, REDDY N S, et al. Fake news detection from online media using machine learning classifiers; proceedings of the Journal of Physics: Conference Series, F, 2022 [C]. IOP Publishing.
[8] RUBIN V L, LUKOIANOVA T. Truth and Deception at the Rhetorical Structure Level [J]. Journal of the Association for Information Science and Technology, 2015, 66(5): 905-17.
[9] KWON S, CHA M, JUNG K, et al. Prominent features of rumor propagation in online social media; proceedings of the 2013 IEEE 13th international conference on data mining, F, 2013 [C]. IEEE.
[10] PéREZ-ROSAS V, KLEINBERG B, LEFEVRE A, et al. Automatic detection of fake news [J]. arXiv preprint arXiv:170807104, 2017.
[11] POTTHAST M, KIESEL J, REINARTZ K, et al. A stylometric inquiry into hyperpartisan and fake news [J]. arXiv preprint arXiv:170205638, 2017.
[12] DELLA VEDOVA M L, TACCHINI E, MORET S, et al. Automatic online fake news detection combining content and social signals; proceedings of the 2018 22nd conference of open innovations association (FRUCT), F, 2018 [C]. IEEE.
[13] LOOIJENGA M S. The detection of fake messages using machine learning [D]; University of Twente, 2018.
[14] AHMAD I, YOUSAF M, YOUSAF S, et al. Fake news detection using machine learning ensemble methods [J]. Complexity, 2020, 2020(1): 8885861.
[15] KALIYAR R K, GOSWAMI A, NARANG P. FakeBERT: Fake news detection in social media with a BERT-based deep learning approach [J]. Multimedia tools and applications, 2021, 80(8): 11765-88.
[16] SHU K, MAHUDESWARAN D, WANG S, et al. FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media [J]. Big Data, 2020, 8(3): 171-88.
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