Abstract
Fake news and misinformation have become a serious predicament, especially, in recent times as it has become easier to spread and harder to recognize. Not only has it created an environment of distrust around the world and misled people, but also incited violence and resulted in people losing their lives. State of the art fake news detection includes the use of verifying news from a trustable dataset, BERT filtering or using cues from Lexical Structure, Simplicity and Emotion and more. In addition, several frameworks have also been proposed to deal with this issue e.g., SpotFake and FR-detect to name a few. However, these frameworks strongly rely on the users to verify the news, modification of information, categorization of the news, and reliability of the dataset (if a dataset is used). This paper proposes ‘HOAXIMETER’, a framework to detect fake news on the World Wide Web covering the weakness of the aforementioned existing ones. It does so by putting forward an in-depth study and literature review on the existing frameworks by analyzing and evaluating them in the context of how well they work with each step, thereby highlighting their strengths and weaknesses. Ultimately, ‘HOAXIMETER’ is proposed which is meant to be the most effective fake news detection framework free from the issues of the existing one.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Choy M, Chong M (nd) Seeing through misinformation: a framework for identifying fake online news. https://www.arxiv.org/pdf/1804.03508.pdf
Bedi A, Pandey N, Khatri SK (2019) A framework to identify and secure the issues of fake news and rumours in social networking. IEEE Xplore. https://doi.org/10.1109/PEEIC47157.2019.8976800
Liu H, Wang L, Han X, Zhang W, He X (2020) Detecting fake news on social media: a multi-source scoring framework.. IEEE Xplore. https://doi.org/10.1109/ICCCBDA49378.2020.9095586. Accessed 28 Mar 2022
Singhal S, Shah RR, Chakraborty T, Kumaraguru P, Satoh S (2019) SpotFake: a multi-modal framework for fake news detection. IEEE Xplore. https://doi.org/10.1109/BigMM.2019.00-44. Accessed 28 Mar 2022
Jarrahi A, Safari L (nd) FR-detect: a multi-modal framework for early fake news detection on social media using publishers features. https://www.arxiv.org/pdf/2109.04835.pdf. Accessed 29 Mar 2022
Parikh SB, Khedia SR, Atrey PK (2019) A framework to detect fake tweet images on social media. In: 2019 IEEE fifth international conference on multimedia big data (BigMM). https://doi.org/10.1109/bigmm.2019.00-37. Accessed 29 Mar 2022
Abdelminaam DS, Ismail FH, Taha M, Taha A, Houssein EH, Nabil A (2021) CoAID-DEEP: an optimized intelligent framework for automated detecting COVID-19 misleading information on twitter. IEEE Access 9:27840–27867. https://doi.org/10.1109/ACCESS.2021.3058066. Accessed 29 Mar 2022
Hassan N, Arslan F, Li C, Tremayne M (2017) Toward automated fact-checking. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. https://doi.org/10.1145/3097983.3098131. Accessed 16 May 2022
de Beer D, Matthee M (2020) Approaches to identify fake news: a systematic literature review. Integr Sci Dig Age 136:13–22. https://doi.org/10.1007/978-3-030-49264-9_2. Accessed 16 May 2022
Diakopoulos N (2019) Automating the news: how algorithms are rewriting the media. Harvard University Press, Cambridge, Massachusetts
http://www.skillsire.com (nd) How do you perform unsupervised classification on fake news? https://www.skillsire.com/read-blog/347_how-do-you-perform-unsupervised-classification-on-fake-news.html. Accessed 16 May 2022
Iubel N (nd) Introducing bedfellows. https://www.source.opennews.org/articles/introducing-bedfellows/. Accessed 16 May 2022
Abhishek K, Sahoo S (2020) Role of text similarity in fake news classification regarding COVID-19. Research gate. https://www.researchgate.net/publication/348266889_Role_of_Text_Similarity_in_Fake_News_Classification_regarding_COVID-19. Accessed 16 May 2022
Beri A (2020) Stemming vs lemmatization. https://www.towardsdatascience.com/stemming-vs-lemmatization-2daddabcb221. Accessed 16 May 2022
Gupta L (2021) Differences between Word2Vec and BERT. The Startup. https://www.medium.com/swlh/differences-between-word2vec-and-bert-c08a3326b5d1#:~:text=Word2Vec%20will%20generate%20the%20same. Accessed 17 May 2022
toolbox.google.com (nd) Fact check tools. https://www.toolbox.google.com/factcheck/explorer. Accessed 17 May 2022
products.wolframalpha.com (nd) Wolfram|Alpha APIs: computational knowledge integration. https://www.products.wolframalpha.com/api. Accessed 17 Jun 2022
Olah C (2015) Understanding LSTM networks—colah’s blog. http://colah.github.io/posts/2015-08-Understanding-LSTMs/. Accessed 17 May 2022
Phi M (2019) Illustrated guide to LSTM’s and GRU’s: a step by step explanation. https://www.towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21. Accessed 17 May 2022
www.kaggle.com (nd) Fake and real news dataset. https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset?select=True.csv. Accessed 17 Jun 2022
Abrahamson A (2020) Detecting fake news with deep learning. https://www.towardsdatascience.com/detecting-fake-news-with-deep-learning-7505874d6ac5. Accessed 16 Jun 2022
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chowdhary, I.Z., Chaudhry, U.B. (2023). ‘HOAXIMETER’—An Effective Framework for Fake News Detection on the World Wide Web. In: Jahankhani, H. (eds) Cybersecurity in the Age of Smart Societies. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-031-20160-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-20160-8_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20159-2
Online ISBN: 978-3-031-20160-8
eBook Packages: Computer ScienceComputer Science (R0)