Identify Fake News: Solutions for Students
Main Article Content
Abstract
Evaluating fake news for factuality is a complex and critical task. This area of research has recently gained significant attention. Our study investigates the link between various factors and the ability to detect fake news, based on a survey of 1161 students from two Vietnamese universities. Most students struggle to discern fake news. To equip students with the tools to combat misinformation, we explore the connection between various factors and the ability to detect fake news. We propose two sets of solutions: individual-level solutions (perception, attitude, and behavior) and system-level solutions (strategic source identification, machine learning, propaganda awareness, and media literacy education). Analysis using the PLS-SEM model shows that the three individual-level solutions significantly contribute to detecting fake news, with a coefficient of determination (R2) of 0.503.
Keywords:
Fake news, identifying fake news.
References
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[42] M. V. Bronstein, G. Pennycook, A. Bear, D. G. Rand, T. D. Cannon, Belief in Fake News is Associated With Delusionality, Dogmatism, Religious Fundamentalism, and Reduced Analytic Thinking, Journal of Applied Research in Memory and Cognition, Vol. 8, No. 1, 2019, pp. 108-117.
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[48] V. L. Rubin, Disinformation and Misinformation Triangle: A Conceptual Model for Fake News Epidemic, Causal Factors and Interventions, Journal of Documentation, 2019.
[49] Rubin et al., Coherence, Disorganization, and Fragmentation in Traumatic Memory Reconsidered: A Response to, Journal of Abnormal Psychology, 2016, pp. 1011-1017.
[2] P. H. A. Faustini, T. F. Covoes, Fake News Detection in Multiple Platforms and Languages, Expert Systems with Applications, Vol. 158, 2020, pp. 113503.
[3] M. D. Vicario, W. Quattrociocchi, A. Scala, F. Zollo, Polarization and Fake News: Early Warning of Potential Misinformation Targets, ACM Trans, Web (TWEB), Vol. 13, No. 2, 2019, pp. 1-22.
[4] M. Z. Asghar, A. Khan, R. Ali, A. Khattak, Exploring Deep Neural Networks for Rumor Detection, Journal of Ambient Intelligence and Humanized Computing, Vol. 12, 2021, pp. 4315-4333.
[5] J. C. Reis, A. Correia, F. Murai, A. Veloso, F. Benevenuto, Supervised Learning for Fake News Detection, IEEE Intell, Syst, Vol. 34, No. 2, 2019, pp. 76-81.
[6] A. Gelfert, Fake News: A Definition, Informal Logic, Vol. 38, No. 1, 2018, pp. 84-117.
[7] X. Zhou, R. Zafarani, Fake News: A Survey of Research, Detection Methods, and Opportunities, arXiv preprint arXiv:1812.00315, 2018.
[8] K. Shu, A. Sliva, S. Wang, J. Tang, H. Liu, Fake News Detection on Social Media: A Data Mining Perspective, ACM SIGKDD Explorations Newsletter, Vol. 19, No. 1, 2017, pp. 22-36.
[9] S. Kumar, N. Shah, False Information on Web and Social Media: A Survey. arXiv Preprint arXiv:1804.08559, 2021.
[10] S. Zannettou, M. Sirivianos, J. Blackburn, J. N. Kourtellis, The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans, Journal of Data and Information Quality (JDIQ), Vol. 11, No. 3, 2019, pp. 1-37.
[11] S. M. Jang, J. K. Kim, Third-Person Effects of Fake News: Fake News Regulation and Media Literacy Interventions, Computers in Human Behavior, Vol. 80, 2018, pp. 295-302.
[12] P. Mihailidis, S. Viotty, Spreadable Spectacle in Digital Culture: Civic Expression, Fake News, and the Role of Media Literacies in “Post-Fact” Society, American Behavioral Scientist, Vol. 61, 2017, pp. 441-454.
[13] H. Allcott, M. Gentzkow, Social Media and Fake News in the 2016 Election, Journal of Economic Perspectives, Vol. 31, No. 2, 2017, pp. 211-236, https://doi.org/10.1257/jep.31.2.211.
[14] R. Sicilia, S. L. Giudice, Y. Pei, M. Pechenizkiy, P. Soda, Twitter Rumour Detection in the Health Domain, Expert Systems with Applications, Vol. 110, 2018, pp. 33-40.
[15] N. Mele, D. Lazer, M. Baum, N. Grinberg, L. Friedland, K. Joseph, C. Mattsson, Combating Fake News: An Agenda for Research and Action, https://shorensteincenter.org/combating-fake-news-agenda-for-research/, 2017 (accessed on: September 27th, 2021).
[16] Z. L. Mahid, S. Manickam, S. Karuppayah, Detection Techniques, In 2018 Fourth International Conference on Advances in Computing, Communication and Automation (ICACCA), 2018, pp. 1-5.
[17] A. A. Halimeh, P. Pourghomi, F. Safieddine, The Impact of Facebook’S News Fact-Checking on Information Quality (Iq) Shared on Social Media, MIT International Conference on Information Quality, 2017.
[18] C. Buntain, J. Golbeck, Automatically Identifying Fake News in Popular Twitter Threads, 2017 Ieee International Conference on Smart Cloud (SmartCloud), 2017, pp. 208-215.
[19] K. Clayton, S. Blair, J. A. Busam, S. Forstner, J. Glance, G. Green, B. Nyhan, Real Solutions for Fake News? Measuring the Effectiveness of General Warnings and Fact-Check Tags in Reducing Belief in False Stories on Social Media, Political Behavior, Vol. 42, No. 4, 2020, pp. 1073-1095.
[20] H. Wasserman, D. M. Morales, An Exploratory Study of Fake News and Media Trust in Kenya, Nigeria and South Africa, African Journalism Studies, Vol. 40, No. 1, 2019, pp. 107-123.
[21] Y. J. Hou, K. Okuda, C. E. Edwards, D. R. Martinez, T. Asakura, K. H. Dinnon, R. S. Baric, Sars-Cov-2 Reverse Genetics Reveals a Variable Infection Gradient in the Respiratory Tract, Cell, Vol. 182, No. 2, 2020, pp. 429-446.
[22] N. N. Cuong, Preventing and Combating Fake News in Cyberspace and How to Identify it, Paragraph, Vol. 3, 2020, pp. 3,
https://www.mic.gov.vn/mic_2020/Pages/TinTuc/147407/Phong--chong-tin-gia-tren-khong-gian-mang-va-cach-nhan-dien.html/, 2020 (accessed on: September 27th, 2021).
[23] K. Shu, L. Cui, S. Wang, D. Lee, H. Liu, Defend: Explainable Fake News Detection, In Proceedings of the 25th ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, 2019, pp. 395-405.
[24] K. K. Kumar, G. Geethakumari, Detecting Misinformation in Online Social Networks Using Cognitive Psychology, Humancentric Computing and Information Sciences, Vol. 4, No. 1, 2014, pp. 1-22.
[25] A. Zubiaga, A. Aker, K. Bontcheva, M. Liakata, R. Procter, Detection and Resolution of Rumours in Social Media: A Survey, ACM Computing Surveys (CSUR), Vol. 51, No. 2, 2018, pp. 1-36.
[26] G. Rampersad, T. Althiyabi, Fake News: Acceptance by Demographics and Culture on Social Media, Journal of Information Technology and Politics, Vol. 17, No. 1, 2020, pp. 1-11.
[27] D. Lazer, M. Baum, N. Grinberg, L. Friedland, K. Joseph, W. Hobbs, C. Mattsson, Combating Fake News: An Agenda for Research and Action, 2017.
[28] C. Ireton, J. Posetti, J. Journalism, Fake News and Disin-Formation: Handbook for Journalism Education and Training, France: UNESCO Publishing, 2018.
[29] J. F. Hair, I. J. Risher, M. Sarstedt, C. M. Ringle, When to use and How to Report the Results of Pls-Sem, European Business Review, Vol. 31, No. 1, 2019, pp. 2-24, https://doi.org/10.1108/EBR-11-2018- 0203.
[30] J. F. Hair, C. M. Ringle, M. Sarstedt, PLS-SEM: Indeed a Silver Bullet, Journal of Marketing Theory and Practice, Vol. 19, No. 2, 2011, pp. 139-152.
[31] C. Fornell, D. F. Larcker, Evaluating Structural Equation Models with Unobservable Variables and Measurement Error, Journal of Marketing Research, Vol. 18, No. 1, 1981, pp. 39-50.
[32] N. Abbas, M. A. ul Haq, U. Ashiq, S. Ubaid, Loneliness Among Elderly Widows and Its Effect on Social and Mental Well-being, Global Social Welfare, Vol. 7, No. 3, 2020, pp. 215-229.
[33] C. R. G. Tamez, M. C. D. Aguirre, J. N. B. Codina, P. G. Rodríguez, Analysis of the Elements of the Theory of Flow and Perceived Value and Their Influence in Craft Beer Consumer Loyalty, Journal of International Food and Agribusiness Marketing, 2020, pp. 1-31.
[34] J. Henseler, C. M. Ringle, M. Sarstedt, Using Pls Path Modeling in New Technology Research: Updated Guidelines, Industrial Management and Data Systems, Vol. 1, No. 2, 2016, pp. 2-20.
[35] T. Ramayah, J. Cheah, F. Chuah, H. Ting, M. A. Memon, Partial Least Squares Structural Equation Modeling Using SmartPLS 3.0 (1st Editi), Pearson Malaysia, 2017.
[36] J. F. Hair, M. Sarstedt, L. Hopkins, V. G. Kuppelwieser, Partial Least Squares Structural Equation Modeling (Pls-Sem): An Emerging Tool in Business Research, European Business Review, 2014.
[37] W. W. Chin, Commentary: Issues and Opinion on Structural Equation Modeling, 1998.
[38] J. F. Hair, M. Sarstedt, C. M. Ringle, S. P. Gudergan, Advanced Issues in Partial Least Squares Structural Equation Modeling, saGe Publications, 2017.
[39] K. K. Wong, Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS, Marketing Bulletin, Vol. 24, No. 1, 2013, pp. 1-32, https://doi.org/10.1108/EBR-10-2013-0128.
[40] J. Cohen, Statistical Power Analysis for Behavioral Science (2nd), Hillsdale, NJ: Lawrence Erlbaum Associates, 1998.
[41] G. Pennycook, D. G. Rand, Who Falls for Fake News? The Roles of Bullshit Receptivity, Overclaiming, Familiarity, and Analytic Thinking, Journal of Personality, Vol. 88, No. 2, 2020, pp. 185-200.
[42] M. V. Bronstein, G. Pennycook, A. Bear, D. G. Rand, T. D. Cannon, Belief in Fake News is Associated With Delusionality, Dogmatism, Religious Fundamentalism, and Reduced Analytic Thinking, Journal of Applied Research in Memory and Cognition, Vol. 8, No. 1, 2019, pp. 108-117.
[43] L. N. Hung, Public Opinion: Communication Theory and Formation Mechanism, Journal of Social Science Information, No. 2, 2019, pp. 44-50.
[44] L. T. M. Oanh, L. N. Hung, P. H. Tra et al, Factors Affecting Students' Fake News Identification During COVID-19 in Vietnam: Access From Sociological Study and Application of PLS-Sem Model, Wseas Transactions on Business and Economics, E-ISSN, 2023, pp. 2224-2899, https://doi.org/10.37394/23207.2023.20.126.
[45] W. Y. William, Liar, Liar Pants on Fire: A New Benchmark Dataset for Fake News Detection, Department of Computer Science University of California, Santa Barbara Santa Barbara, CA 93106 USA, 2017.
[46] H. Buckner, Taylor, A Theory of Rumor Transmission, Public Opinion Quarterly, Vol. 29, No. 1, 1965, pp. 54-70.
[47] M. A. Ekstrand, R. Das, F. Burke, Diaz (To Appear), Fairness and Discrimination in Information Access Systems, Foundations and Trends in Information Retrieval, 2018, https://doi.org/10.1561/1500000079.
[48] V. L. Rubin, Disinformation and Misinformation Triangle: A Conceptual Model for Fake News Epidemic, Causal Factors and Interventions, Journal of Documentation, 2019.
[49] Rubin et al., Coherence, Disorganization, and Fragmentation in Traumatic Memory Reconsidered: A Response to, Journal of Abnormal Psychology, 2016, pp. 1011-1017.
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