Analyzing the misleading information on Covid-19 using MBCFWS4

A. Anbu

doi:10.31893/multiscience.2023021

Keywords:

corona virus

misinformation

memory-based CF

respondent’s perception

similarity measure

ten factors

Abstract

The COVID-19 pandemic had a major effect on almost every area of human lives in the majority of the world's countries. Misinformation spreads quickly in the initial phases of the COVID-19 pandemic in different forms, such as fear, distortion, contraindication assumption, and others. False and misleading advertisements harm millions of individuals. In recent research, there are more advanced techniques have been used to address the misinformation about the COVID-19 pandemic. But they are self-reported and probabilistic data during the lockdown period. So, it was difficult to find the respondents' perceptions based on sharing the COVID-19 misinformation. This proposed work analyzed and filtered some optimized factors to analyze the misinformation such as fake reports, fake remedies, conspiracy, susceptibility, vaccine rumors, social media, vitamin D prevents corona, political corona, socio-Economic, and more side effects after getting vaccinated. Collaborative filtering (CF) is the most efficient recommender system, and it is extensively utilized by a broad range of research institutions and enterprises, as well as being used in practice. It consists of two types of CF namely Memory-based CF and Model-based CF. In this work, Memory-based CF recommendation algorithm is combined with a similarity measure called MBCFWS4 to analyze the similarity measure between the factors to conclude the most impact factor. The Primary and secondary dataset helps to identify the respondents' perception based on the COVID-19 misinformation. The efficiency comparison of the proposed work is measured in terms of Precision, Recall & F-measure and found that this analysis using MBCFWS4 is outperforming well than the others as MBCFWS4 predicted accurately and revealed the conclusion based on the COVID-19 misinformation.
References
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Anbu, A. (2023). Analyzing the misleading information on Covid-19 using MBCFWS4. Multidisciplinary Science Journal, 5(2), 2023021. https://doi.org/10.31893/multiscience.2023021

[1] Pak A, Adegboye OA, Adekunle AI, Rahman KM, McBryde ES, Eisen DP (2020) Economic consequences of the COVID-19 outbreak: The need for epidemic preparedness. Frontiers in Public Health 8. DOI: 10.3389/fpubh.2020.00241

[2] Roozenbeek J, Schneider CR, Dryhurst S, Kerr J, Freeman AL, Recchia G, Van der Bles AM, Van der Linden S (2020) Susceptibility to misinformation about COVID-19 around the world. Royal Society Open Science 7:201199. DOI: 10.1098/rsos.201199

[3] Elhadad MK, Li KF, Gebali F (2020) Detecting misleading information on COVID-19. IEEE Access 8:165201. DOI: 10.1109/access.2020.3022867

[4] Al-Zaman MS (2021) A thematic analysis of misinformation in India during the COVID-19 pandemic. International Information and Library Review 1. DOI: 10.1080/10572317.2021.1908063

[5] Ejegwa PA (2020) New similarity measures for Pythagorean fuzzy sets with applications. International Journal of Fuzzy Computation and Modelling 3:75. DOI: 10.1504/ijfcm.2020.106105

[6] Peng X (2018) New similarity measure and distance measure for Pythagorean fuzzy set. Complex and Intelligent Systems 5:101. DOI: 10.1007/s40747-018-0084-x

[7] Chen R, Hua Q, Chang Y, Wang B, Zhang L, Kong X (2018) A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks. IEEE Access 6:64301. DOI: 10.1109/access.2018.2877208

[8] Hatmal MM, Al-Hatamleh MA, Olaimat AN, Hatmal M, Alhaj-Qasem DM, Olaimat T, Mohamud R (2021) Side effects and perceptions following COVID-19 vaccination in Jordan: A randomized, cross-sectional study implementing machine learning for predicting severity of side effects. Vaccines 9:556. DOI: 10.3390/vaccines9060556

[9] Roy S, Ghosh P (2020) Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking. PLOS ONE 15:e0241165. DOI: 10.1371/journal.pone.0241165

[10] Wang J, Wang Z (2020) Strengths, weaknesses, opportunities and threats (SWOT) analysis of China’s prevention and control strategy for the COVID-19 epidemic. International Journal of Environmental Research and Public Health 17:2235. DOI: 10.3390/ijerph17072235

[11] Tariq Soomro Z, Waseem Ilyas SH, Yaqub U (2020) Sentiment, count and cases: Analysis of Twitter discussions during COVID-19 pandemic. 2020 7th International Conference on Behavioural and Social Computing (BESC). DOI: 10.1109/besc51023.2020.9348291

[12] Junlan Z, Chengke Y (2020) Data-analysis-based discussion on COVID-19 pandemic shocks to the economy and policy responses: Cases in the United Kingdom. 2020 Management Science Informatization and Economic Innovation Development Conference (MSIEID). DOI: 10.1109/msieid52046.2020.00109

[13] Ma X, Wang J, Qin H, Wang Y (2020) Public opinion analysis about the coronavirus COVID-19 based on micro-blog data. 2020 International Conference on Information Science and Education (ICISE-IE). DOI: 10.1109/icise51755.2020.00104

[14] Vrindavanam J, Srinath R, Shankar HH, Nagesh G (2021) Machine learning based COVID-19 cough classification models – A comparative analysis. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). DOI: 10.1109/iccmc51019.2021.9418358

[15] Alhijawi B, Al-Naymat G, Obeid N, Awajan A (2021) Novel predictive model to improve the accuracy of collaborative filtering recommender systems. Information Systems 96:101670. DOI: 10.1016/j.is.2020.101670

[16] Ghazarian S, Nematbakhsh MA (2015) Enhancing memory-based collaborative filtering for group recommender systems. Expert Systems with Applications 42:3801. DOI: 10.1016/j.eswa.2014.11.042

[17] Bhalse N, Thakur R (2021) Algorithm for movie recommendation system using collaborative filtering. Materials Today: Proceedings. DOI: 10.1016/j.matpr.2021.01.235

[18] Nielsen RK, Schulz A, Fletcher R (2021) An ongoing infodemic: How people in eight countries access news and information about Coronavirus a year into the pandemic. Availale in: https://reutersinstitute.politics.ox.ac.uk/ongoing-infodemic-how-people-eight-countries-access-news-and-information-about-coronavirus-year Accessed on: November 4, 2021.

[19] Ilie PC, Stefanescu S, Smith L (2020) The role of vitamin D in the prevention of coronavirus disease 2019 infection and mortality. Aging: Clinical and Experimental Research 32:1195.

[20] Pummerer L, Böhm R, Lilleholt L, Winter K, Zettler I, Sassenberg K (2021) Dataset for: Conspiracy theories and their societal effects during the COVID-19 pandemic [Data set]. PsychArchives. Availale in: https://www.psycharchives.org/handle/20.500.12034/4095 Accessed on: November 4, 2021.

[21] Augustine EP (2020) New similarity measures for Pythagorean fuzzy sets with applications. International Journal of Fuzzy Computation and Modelling 3:75. DOI: 10.1504/ijfcm.2020.10027701

[22] Zarzour H, Jararweh Y, Al-Sharif ZA (2020) An effective model-based trust collaborative filtering for explainable recommendations. 2020 11th International Conference on Information and Communication Systems (ICICS). DOI: 10.1109/icics49469.2020.239555

Abstract

References

How to cite