Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm
Source abstract
Abstract
The spread of the Coronavirus pandemic has been accompanied by an infodemic. The false information that is embedded in the infodemic affects people’s ability to have access to safety information and follow proper procedures to mitigate the risks. This research aims to target the falsehood part of the infodemic, which prominently proliferates in news articles and false medical publications. Here, we present NeoNet, a novel supervised machine learning algorithm that analyzes the content of a document (news article, a medical publication) and assigns a label to it. The algorithm was trained by Term Frequency Inverse Document Frequency (TF-IDF) bigram features, which contribute a network training model. The algorithm was tested on two different real-world datasets from the CBC news network and COVID-19 publications. In five different fold comparisons, the algorithm predicted a label of an article with a precision of 97–99%. When compared with prominent algorithms such as Neural Networks, SVM, and Random Forests NeoNet surpassed them. The analysis highlighted the promise of NeoNet in detecting disputed online contents, which may contribute negatively to the COVID-19 pandemic.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Misinformation and Its Impacts · Topic Modeling · Spam and Phishing Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Computer and Information Systems, Islamic University of Madinah, Medina 42351, Saudi Arabia
School of Cybersecurity, Data Science and Computing, Norwich University, Northfield, VT 05663, USA
Mininglamp Academy of Sciences, Mininglamp Technology, Beijing 100864, China
Funding
National Security Agency · IU of Madinah
References (60)
Show source-provided references
1. Misinformation of COVID-19 on the Internet: Infodemiology Study10.2196/18444 ↗
2. Cross-Country Comparison of Public Awareness, Rumors, and Behavioral Responses to the COVID-19 Epidemic: Infodemiology Study10.2196/21143 ↗
3. Evaluation of Korean-Language COVID-19–Related Medical Information on YouTube: Cross-Sectional Infodemiology Study10.2196/20775 ↗
4. Global Infodemiology of COVID-19: Analysis of Google Web Searches and Instagram Hashtags10.2196/20673 ↗
5. Anticoagulant treatment is associated with decreased mortality in severe coronavirus disease 2019 patients with coagulopathy10.1111/jth.14817 ↗
6. Framework for Managing the COVID-19 Infodemic: Methods and Results of an Online, Crowdsourced WHO Technical Consultation10.2196/19659 ↗
7. The “Infodemic” of Journal Publication Associated with the Novel Coronavirus Disease10.2106/JBJS.20.00610 ↗
8. Okan, O., Bollweg, T.M., Berens, E.M., Hurrelmann, K., Bauer, U., and Schaeffer, D. (2020). Coronavirus-related health literacy: A cross-sectional study in adults during the COVID-19 infodemic in Germany. Int. J. Environ. Res. Public. Health, 17.10.2139/ssrn.3631299 ↗
9. Public Health in the Information Age: Recognizing the Infosphere as a Social Determinant of Health10.2196/19311 ↗
10. Public Emotions and Rumors Spread During the COVID-19 Epidemic in China: Web-Based Correlation Study10.2196/21933 ↗
11. A geospatial infodemic: Mapping Twitter conspiracy theories of COVID-1910.1177/2043820620935683 ↗
12. COVID-19–Related Infodemic and Its Impact on Public Health: A Global Social Media Analysis10.4269/ajtmh.20-0812 ↗
13. Infodemic and the spread of fake news in the COVID-19-era10.1097/MEJ.0000000000000713 ↗
14. Matthews, S. (2021, August 04). Government Orders Review into Vitamin D Role in Covid-19. Available online: https://www.dailymail.co.uk/news/article-8432321/Government-orders-review-vitamin-D-role-Covid-19.html.
15. Who do you trust? The digital destruction of shared situational awareness and the COVID-19 infodemic10.1016/j.ijinfomgt.2020.102201 ↗
16. How to Fight an Infodemic: The Four Pillars of Infodemic Management10.2196/21820 ↗
17. Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics10.1038/s41562-020-00994-6 ↗
18. (2020, December 25). Twitter to Start Removing COVID-19 Vaccine Misinformation. Available online: https://apnews.com/article/misinformation-immunizations-coronavirus-pandemic-085cc1b49a5d488026f2e59d8f32d590.
19. Rojas, I., Joya, G., and Catala, A. (2019, January 12–14). Semantic Fake News Detection: A Machine Learning Perspective. Proceedings of the Advances in Computational Intelligence, Gran Canaria, Spain.
20. A Machine Learning Approach to Coreference Resolution of Noun Phrases10.1162/089120101753342653 ↗
21. Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram10.2196/20794 ↗
22. Liu, F., Liu, F., and Liu, Y. (2008, January 15–19). Automatic keyword extraction for the meeting corpus using supervised approach and bigram expansion. Proceedings of the 2008 IEEE Spoken Language Technology Workshop, Goa, India.10.1109/SLT.2008.4777870 ↗
23. (2020, December 25). Relationships between Words: N-Grams and Correlations—Text Mining with R [Book]. Available online: https://www.oreilly.com/library/view/text-mining-with/9781491981641/ch04.html.
24. Qiang, G. (2010, January 7–10). An Effective Algorithm for Improving the Performance of Naive Bayes for Text Classification. Proceedings of the 2010 Second International Conference on Computer Research and Development, Kuala Lumpur, Malaysia.10.1109/ICCRD.2010.160 ↗
25. The support vector machine under test10.1016/S0925-2312(03)00431-4 ↗
26. Suthaharan, S. (2016). Support Vector Machine. Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, Springer.10.1007/978-1-4899-7641-3 ↗
27. (2020, December 25). What Is a Support Vector Machine?|Nature Biotechnology. Available online: https://www.nature.com/articles/nbt1206-1565.
28. Aphiwongsophon, S., and Chongstitvatana, P. (2018, January 18–21). Detecting Fake News with Machine Learning Method. Proceedings of the 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Chiang Rai, Thailand.10.1109/ECTICon.2018.8620051 ↗
29. Traore, I., Woungang, I., and Awad, A. (2017, January 25–27). Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. Proceedings of the Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, Vancouver, BC, Canada.
30. Automatic deception detection: Methods for finding fake news10.1002/pra2.2015.145052010082 ↗
31. Dunning, T., and Friedman, E. (2014). Practical Machine Learning: A New Look at Anomaly Detection, O’Reilly Media, Inc.
32. Inoue, J., Yamagata, Y., Chen, Y., Poskitt, C.M., and Sun, J. (2017, January 18–21). Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning. Proceedings of the 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, LA, USA.10.1109/ICDMW.2017.149 ↗
33. Kang, D.-K., Fuller, D., and Honavar, V. (2005, January 15–17). Learning classifiers for misuse and anomaly detection using a bag of system calls representation. Proceedings of the Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop, West Point, NY, USA.
34. Liu, D., Zhao, Y., Xu, H., Sun, Y., Pei, D., Luo, J., Jing, X., and Feng, M. (2015, January 28–30). Opprentice: Towards Practical and Automatic Anomaly Detection Through Machine Learning. Proceedings of the 2015 Internet Measurement Conference, Tokyo, Japan.10.1145/2815675.2815679 ↗
35. Omar, S., Ngadi, M., Jebur, H., and Benqdara, S. (2013). Machine Learning Techniques for Anomaly Detection: An Overview. Int. J. Comput. Appl., 79.10.5120/13715-1478 ↗
36. Pecht, M.G., and Kang, M. (2019). Machine Learning: Anomaly Detection. Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things, IEEE.10.1002/9781119515326 ↗
37. A hybrid machine learning approach to network anomaly detection10.1016/j.ins.2007.03.025 ↗
38. Shon, T., Kim, Y., Lee, C., and Moon, J. (2005, January 15–17). A machine learning framework for network anomaly detection using SVM and GA. Proceedings of the Sixth Annual IEEE SMC Information Assurance Workshop, West Point, NY, USA.
39. (2021, July 10). PatientsLikeMe. Available online: https://www.patientslikeme.com/.
40. (2021, July 10). Medical News, Opinion, Health Information, Journal and Conference Briefings, Industry Alerts on Doctors Lounge. Available online: https://www.doctorslounge.com/.
41. Measuring climate change on Twitter using Google’s algorithm: Perception and events10.1108/IJWIS-08-2015-0025 ↗
42. (2020, December 25). COVID-19 Open Research Dataset Challenge (CORD-19). Available online: https://kaggle.com/allen-institute-for-ai/CORD-19-research-challenge.
43. (2020, December 25). Pandas—Python Data Analysis Library. Available online: https://pandas.pydata.org/.
44. (2021, July 08). TextBlob—Google Search. Available online: https://www.google.com/search?q=TextBlob&oq=TextBlob&aqs=chrome..69i57j35i39j69i59j0l5.2340j0j4&sourceid=chrome&ie=UTF-8.
45. Ramos, J. (2021, July 29). Using TF-IDF to Determine Word Relevance in Document Queries. Available online: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.121.1424&rep=rep1&type=pdf.
46. Bekkerman, R., and Allan, J. (2004). Using Bigrams in Text Categorization, Center of Intelligent Information Retrieval, UMass Amherst.
47. Hachaj, T., and Ogiela, M.R. (2018, January 13–15). What Can Be Learned from Bigrams Analysis of Messages in Social Network?. Proceedings of the 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Beijing, China.10.1109/CISP-BMEI.2018.8633108 ↗
48. The use of bigrams to enhance text categorization10.1016/S0306-4573(01)00045-0 ↗
49. Prim’s algorithm for solving minimum spanning tree problem in fuzzy environment
50. Design and Implementation of GPU-Based Prim’s Algorithm
51. CBC News (2021, August 04). The Latest on the Coronavirus Outbreak for May 1|CBC News. Available online: https://www.cbc.ca/news/the-latest-on-the-coronavirus-outbreak-for-may-1-1.5552899.
52. Al-Maolegi, M., and Arkok, B. (2014). An Improved Apriori Algorithm for Association Rules. arXiv Prepr.10.5121/ijnlc.2014.3103 ↗
53. Li, N., Zeng, L., He, Q., and Shi, Z. (2012, January 8–10). Parallel Implementation of Apriori Algorithm Based on MapReduce. Proceedings of the 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Kyoto, Japan.10.1109/SNPD.2012.31 ↗
54. Kambayashi, Y., Winiwarter, W., and Arikawa, M. (2001, January 5–7). Enhancing the Apriori Algorithm for Frequent Set Counting. Proceedings of the Data Warehousing and Knowledge Discovery, Munich, Germany.10.1007/3-540-44801-2 ↗
55. Improving Efficiency of Apriori Algorithm Using Transaction Reduction
56. Sammut, C., and Webb, G.I. (2010). Apriori Algorithm. Encyclopedia of Machine Learning, Springer.10.1007/978-0-387-30164-8 ↗
57. Ye, Y., and Chiang, C.C. (2006, January 9–11). A Parallel Apriori Algorithm for Frequent Itemsets Mining. Proceedings of the Fourth International Conference on Software Engineering Research, Management and Applications (SERA’06), Seattle, WA, USA.
58. Alonso-Reina, A., Sepúlveda-Torres, R., Saquete, E., Palomar, M., and Team GPLSI (2019, January 3). Approach for automated fact checking. Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER), Hong Kong, China.10.18653/v1/D19-6617 ↗
59. Orange: Data Mining Toolbox in Python
60. (2020, December 25). “Alarmingly High” Proportion of British People Are Vitamin D Deficient|Daily Mail Online. Available online: https://www.dailymail.co.uk/sciencetech/article-9068299/Alarmingly-high-proportion-British-people-vitamin-D-deficient.html.