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Multidisciplinary Fusion Perspective Analysis Method for False Information RecognitionFAN, W. , WANG, Y. |
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Author keywords
artificial intelligence, machine learning, support vector machines, social computing, natural language processing
References keywords
news(24), fake(18), detection(12), approach(7), social(6), networks(6), media(6), text(5), personality(5), learning(5)
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About this article
Date of Publication: 2024-02-29
Volume 24, Issue 1, Year 2024, On page(s): 61 - 70
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.01007
Web of Science Accession Number: 001178765900001
SCOPUS ID: 85189456069
Abstract
Combating misinformation is one of the urgent social crises. Much research has shown that disinformation can lead to social panic and adversely affect society. It is crucial to promptly detect and counteract misinformation to reduce its adverse effects. Although progress in text-based fact verification has been made, the community needs further exploration into the user-oriented results. To address this gap, we integrate theories from linguistics, journalism, psychology, and cognitive science. We propose a disinformation detection algorithm based on multidimensional content analysis. This algorithm combines human factors and user perception in text interactive media to establish six dimensions for comprehensive content analysis. We have proposed a quantitative calculation method corresponding to six dimensions to detect misinformation. The average accuracy of the proposed model test on four datasets is 95.28%. The results show that this algorithm can effectively analyze from a multidisciplinary theoretical perspective and effectively identify misinformation in Chinese and English. |
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[1] W. Fan, Y. Wang, H. Hu, "Mimicking human verification behavior for news media credibility evaluation," Applied Sciences, 2023, 13(17): 9553. [CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 3] [2] S. Devi, S. Kannimuthu, G. Ravikumar, et al., "Author profiling in code-mixed WhatsApp messages using stacked convolution networks and contextualized embedding based text augmentation," Neural Processing Letters 55.1 (2023): 589-614. [CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 13] [3] A. Bovet, H. A. Makse, "Influence of fake news in Twitter during the 2016 US presidential election," Nature communications, vol. 10 no.1, 2019. [CrossRef] [Web of Science Times Cited 445] [SCOPUS Times Cited 603] [4] D. Orso, N. Federici, R. Copetti et al., "Infodemic and the spread of fake news in the COVID-19-era," European Journal of Emergency Medicine, 2020. [CrossRef] [Web of Science Times Cited 170] [SCOPUS Times Cited 201] [5] A. Yahyaoui, J. Rasheed, S. Alsubai, et al., "Performance comparison of deep and machine learning approaches toward COVID-19 detection," Intelligent Automation & Soft Computing, vol. 37, no. 2, 2023. [CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 6] [6] A. Qaiser, S. Hina, A. Kazi, et al., "Fake news encoder classifier (FNEC) for online published news related to COVID-19 vaccines," Intelligent Automation & Soft Computing, vol. 37, no. 1, 2023. [CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 3] [7] L. Wei, Z. Ruiping, C. Yixin, "Cognitive confrontation based on social media platforms in the Russia-Ukraine conflict: Reasons, characteristics and enlightenment," Journal of Intelligence, vol. 42, no. 5, 2023. [CrossRef] [8] C. Daniel, "The misinformation threat: A techno-governance approach for curbing the fake news of tomorrow," Digital Government: Research and Practice, 2023. [CrossRef] [SCOPUS Times Cited 1] [9] B. Hu, Q. Sheng, J. Cao et al., "Learn over past, evolve for future: Forecasting temporal trends for fake news detection," in Proc. ACL, Toronto, Canada, 2023. [CrossRef] [10] J. Gaspers, A. Kumar et al., "Temporal generalization for spoken language understanding," in Proc. NAACL, Seattle, Washington, pp. 37-44, 2022. [CrossRef] [11] Y. Mu, K. Bontcheva, N. Aletras, "It's about time: Rethinking evaluation on rumor detection benchmarks using chronological splits," Findings of the Association for Computational Linguistics: EACL 2023. [CrossRef] [12] X. Zhang, J. Cao, X. Li et al., "Mining dual emotion for fake news detection," in Proc. WWW, Ljubljana, Slovenia, pp. 3465-3476, 2021. [CrossRef] [Web of Science Times Cited 103] [SCOPUS Times Cited 163] [13] C. Wardle, H. Derakhshan, "Toward an interdisciplinary framework for research and policymaking," Strasbourg: Council of Europe, 2017 [14] P. Devarsh, N. D'Souza, and R. Gawande, "Automatic Twitter rumour detection using machine learning," 2022 IEEE Bombay Section Signature Conference (IBSSC). IEEE, 2022. [CrossRef] [SCOPUS Times Cited 2] [15] V. L. Rubin, N. J. Conroy, Y. Chen, "Towards news verification: Deception detection methods for news discourse," in Proc. HICSS, pp. 5-8, 2015. [CrossRef] [16] M. Potthast, J. Kiesel, K. Reinartz et al., "A stylometric inquiry into hyperpartisan and fake news," arXiv preprint, arXiv:1702.05638, 2017. [CrossRef] [SCOPUS Times Cited 289] [17] S. Castelo, T. Almeida, A. Elghafari et al., "A topic-agnostic approach for identifying fake news pages," in Proc. WWW, San Francisco, USA, pp. 975-980, 2019. [CrossRef] [Web of Science Times Cited 48] [SCOPUS Times Cited 70] [18] S. Vosoughi, D. Roy, S. Aral, "The spread of true and false news online," science, vol. 359 no.6380, pp.1146-1151, 2018. [CrossRef] [Web of Science Times Cited 3534] [SCOPUS Times Cited 4671] [19] K. P. Kumar, G. Geethakumari, "Detecting misinformation in online social networks using cognitive psychology," Human-centric Computing and Information Sciences, vol. 4, no. 1, pp. 1-22, 2014. [CrossRef] [Web of Science Times Cited 122] [SCOPUS Times Cited 176] [20] X. Guo, Y. Sun, S. Vosoughi, "Emotion-based modeling of mental disorders on social media," in Proc. WI-IAT, Melbourne, Australia, pp. 8-16, 2021. [CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 10] [21] K. Mansooreh, T. H. Nazer, H. Liu. "Profiling fake news spreaders on social media through psychological and motivational factors," Proceedings of the 32nd ACM conference on hypertext and social media. 2021, pp. 225-230. [CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 21] [22] B. Sampat, S. Raj, "Fake or real news? Understanding the gratifications and personality traits of individuals sharing fake news on social media platforms," Aslib Journal of Information Management, vol.74, no.5, pp. 840-876, 2022. [CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 39] [23] K. Anoop, P. Deepak, V. L. Lajish, "Emotion cognizance improves health fake news identification," in Proc. IDEAS, Incheon (Seoul), South Korea and Montreal, Canada, 2020. [CrossRef] [SCOPUS Times Cited 7] [24] S. Aneja, N. Aneja, B. Bhargava, and R. R. Chowdhury, "Device fingerprinting using deep convolutional neural networks," International Journal of Communication Networks and Distributed Systems, vol. 28, no. 2, pp. 171-198, 2022. [CrossRef] [SCOPUS Times Cited 18] [25] S. Aneja, N. Aneja, P. E. Abas, and A. G. Naim, "Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising," IAES International Journal of Artificial Intelligence (IJ-AI), vol. 11, no. 3, pp. 961-968, 2022. [CrossRef] [SCOPUS Times Cited 5] [26] S. Ryan, T. Parr, K. J. Friston. "Simulating emotions: An active inference model of emotional state inference and emotion concept learning," Frontiers in psychology, vol 10, 2019: 2844. [CrossRef] [Web of Science Times Cited 69] [SCOPUS Times Cited 71] [27] H. Alotaibi, "The role of lexical cohesion in writing quality," International Journal of Applied Linguistics and English Literature, vol. 4, no.1, pp. 261-269, 2015. [CrossRef] [SCOPUS Times Cited 4] [28] L. Qian, R. Xu, Z. Zhou, "MRDCA: A multimodal approach for fine-grained fake news detection through integration of RoBERTa and DenseNet based upon fusion mechanism of co-attention," Annals of Operations Research (2022): 1-22. [CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 5] [29] N. Reimers, I. Gurevych, "Sentence-bert: Sentence embeddings using siamese bert-networks," arXiv preprint, arXiv:1908.10084, 2019. [CrossRef] [30] C. E. Shannon, "A mathematical theory of communication," The Bell system technical journal, vol. 27, no. 3, pp. 379-423, 1948. [CrossRef] [SCOPUS Times Cited 35173] [31] Z. C. Ren, Q. Shen, X. Diao, H. Xu, "A sentiment-aware deep learning approach for personality detection from text," Information Processing & Management, vol. 58, no.3, 2021. [CrossRef] [Web of Science Times Cited 65] [SCOPUS Times Cited 89] [32] G. J. Boyle, "MyersâBriggs type indicator (MBTI): Some psychometric limitations," Australian Psychologist, vol. 30 no. 1, pp. 71-74, 1995. [CrossRef] [Web of Science Times Cited 79] [SCOPUS Times Cited 113] [33] L. Bailly, "Lacan: A beginner's guide," Simon and Schuster, 2012 [34] B. W. Lee, J. H. J. Lee, "Traditional readability formulas compared for English," arXiv preprint arXiv:2301.02975, 2023. [CrossRef] [35] C. Kinnvall, C. Tereza, "The psychology of extremist identification," European Psychologist, 2021. [CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 6] [36] J. W. Pennebaker, M. R. Mehl, K. G. Niederhoffer, "Psychological aspects of natural language use: Our words, our selves," Annual review of psychology, vol. 54 no. 1, pp. 547-577, 2003. [CrossRef] [Web of Science Times Cited 1506] [SCOPUS Times Cited 1856] [37] X. Y. Zhou, Z. Reza, "Network-based fake news detection: A pattern-driven approach," ACM SIGKDD explorations newsletter, vol. 21 no. 2, pp. 48-60, 2019. [CrossRef] [38] H. Ahmed, T. Issa, S. Sherif, "Detecting opinion spams and fake news using text classification," Security and Privacy, vol. 1 no.1, e9, 2018. [CrossRef] [Web of Science Times Cited 188] [39] P. Patwa, S. Sharma, S. Pykl, V. Guptha, G. Kumari, M. S. Akhtar et al., "Fighting an infodemic: Covid-19 fake news dataset," in Combating Online Hostile Posts in Regional Languages during Emergency Situation: First International Workshop, CONSTRAINT 2021, Collocated with AAAI 2021, Virtual Event, Online, pp. 21-29, 2021. [CrossRef] [SCOPUS Times Cited 182] [40] Q. Nan, J. Cao, Y. Zhu et al., "MDFEND: Multi-domain fake news detection," in Proc. CIKM, Gold Coast, Queensland, Australia, 2021. [CrossRef] [Web of Science Times Cited 68] [SCOPUS Times Cited 89] [41] Z. Ma, M. Liu, G. Fang, Y. Shen, "LTCR: Long-text chinese rumor detection dataset," arXiv preprint arXiv:2306.07201, 2023. [CrossRef] [42] C. Blackledge, A. Atapour-Abarghouei, "Transforming fake news: Robust generalisable news classification using transformers, " in 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, pp: 3960-3968, 2021. [CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 10] [43] J. Ayoub, X. Yang, F. Zhou, "Combat COVID-19 infodemic using explainable natural language processing models," Information Processing & Management, vol. 58, no. 4, 2021. [CrossRef] [Web of Science Times Cited 70] [SCOPUS Times Cited 100] [44] T. Yang, J. Deng, X. Quan, Q. Wang, "Orders are unwanted: Dynamic deep graph convolutional network for personality detection," Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. No. 11. 2023. [CrossRef] [45] S. Bharadwaj, S. Sridhar, R. Choudhary and R. Srinath, "Persona Traits Identification based on Myers-Briggs Type Indicator(MBTI) - A Text Classification Approach," 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, 2018, pp. 1076-1082, [CrossRef] [46] M. H. Amirhosseini, H. Kazemian, "Machine learning approach to personality type prediction based on the Myers-Briggs type indicator®," Multimodal Technologies and Interaction, 2020, 4(1): 9. [CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 85] [47] Y. Mehta, S. Fatehi, A. Kazameini, et al., "Bottom-up and top-down: Predicting personality with psycholinguistic and language model features," 2020 IEEE International Conference on Data Mining (ICDM). 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