Machine Learning Approaches for Combating Fake News on social media: Approaches, Advancement, Taxonomy and Future Directions

Monday Simon, Nandom Sumayyah Sophie, Gani Timothy Abe, Ashraf Ishaq, Laurence Emmanuel, Samuel Amachundi Adda, Victoria Sabo Zaku

Abstract


Widespread of Social Network Sites (SNS) and the simplicity of used has gradually changed the generation and spread of information nowadays. However, cheap access to News does not equal an increased level of people’s awareness. In contrast to the traditional media channels, social networks also bring about faster and wider dissemination of intentionally false information (fake news). Viral spread of fake news has severe consequences on the behavior’s, attitudes and beliefs of the people, and ultimately can seriously harm the democratic processes. Reducing fake news negative impact through early detection and control of extensive spread presents the main challenge facing researchers nowadays. In this survey paper, we extensively analyze a wide range of different solutions for the early detection of fake news in the existing literature. More precisely, we examine Machine Learning (ML) models for the identification and classification of fake news, fake news detection on SNS. Finally, we present some open research challenges. 


Full Text:

PDF

References


Adiba, F. I., Islam, T., Kaiser, M. S., Mahmud, M., & Rahman, M. A. (2020). Effect of corpora on classification of fake news using naive Bayes classifier. International Journal of Automation, Artificial Intelligence and Machine Learning, 1(1), 80-92.

Agarwal, A., Mittal, M., Pathak, A., & Goyal, L. M. (2020). Fake news detection using a blend of neural networks: an application of deep learning. SN Computer Science, 1(3), 1-9.

Ahmed, A. A. A., Aljabouh, A., Donepudi, P. K., & Choi, M. S. (2021). Detecting Fake News using Machine Learning: A Systematic Literature Review. arXiv preprint arXiv:2102.04458.

Asano, E. (2017). How much time do people spend on social media? Social Media Today, 4.

Aslam, S. (2018). Twitter by the numbers: Stats, demographics & fun facts. Omnicoreagency. com.

Atodiresei, C.-S., Tănăselea, A., & Iftene, A. (2018). Identifying fake news and fake users on Twitter. Procedia Computer Science, 126, 451-461.

Balmas, M. (2014). When fake news becomes real: Combined exposure to multiple news sources and political attitudes of inefficacy, alienation, and cynicism. Communication research, 41(3), 430-454.

Bashar, A. (2019). Survey on evolving deep learning neural network architectures. Journal of Artificial Intelligence, 1(02), 73-82.

Bessi, A., Coletto, M., Davidescu, G. A., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015). Science vs conspiracy: Collective narratives in the age of misinformation. PloS one, 10(2), e0118093.

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.

Constantinides, P., Henfridsson, O., & Parker, G. G. (2018). Introduction—platforms and infrastructures in the digital age: INFORMS.

Dong, J. Q., & Yang, C.-H. (2020). Business value of big data analytics: A systems-theoretic approach and empirical test. Information & Management, 57(1), 103124.

Figueira, Á., & Oliveira, L. (2017). The current state of fake news: challenges and opportunities. Procedia Computer Science, 121, 817-825.

Fong, R. C., & Vedaldi, A. (2017). Interpretable explanations of black boxes by meaningful perturbation. Paper presented at the Proceedings of the IEEE International Conference on Computer Vision.

Garcia, D., Mavrodiev, P., Casati, D., & Schweitzer, F. (2017). Understanding popularity, reputation, and social influence in the twitter society. Policy & Internet, 9(3), 343-364.

Ghani, N. A., Hamid, S., Hashem, I. A. T., & Ahmed, E. (2019). Social media big data analytics: A survey. Computers in Human Behavior, 101, 417-428.

Graves, A., & Jaitly, N. (2014). Towards end-to-end speech recognition with recurrent neural networks. Paper presented at the International conference on machine learning.

Graves, A., Mohamed, A.-r., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. Paper presented at the 2013 IEEE international conference on acoustics, speech and signal processing.

Hasani-Mavriqi, I., Kowald, D., Helic, D., & Lex, E. (2018). Consensus dynamics in online collaboration systems. Computational social networks, 5(1), 1-24.

Hinton, G. E. (2012). A practical guide to training restricted Boltzmann machines Neural networks: Tricks of the trade (pp. 599-619): Springer.

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.

Kaur, S., Kumar, P., & Kumaraguru, P. (2020). Automating fake news detection system using multi-level voting model. Soft Computing, 24(12), 9049-9069.

Krešňáková, V. M., Sarnovský, M., & Butka, P. (2019). Deep learning methods for Fake News detection. Paper presented at the 2019 IEEE 19th International Symposium on Computational Intelligence and Informatics and 7th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Sciences and Robotics (CINTI-MACRo).

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.

Kucharski, A. (2016). Study epidemiology of fake news. Nature, 540(7634), 525-525.

Kwekha-Rashid, A. S., Abduljabbar, H. N., & Alhayani, B. (2021). Coronavirus disease (COVID-19) cases analysis using machine-learning applications. Applied Nanoscience, 1-13.

Makarim, N. H., Dimyati, D., & Kurniullah, A. Z. (2020). The use of Instagram account in constructing the concept of beauty: a case on “unpad geulis”. aspiration Journal, 1(1), 73-98.

Manogaran, G., Vijayakumar, V., Varatharajan, R., Kumar, P. M., Sundarasekar, R., & Hsu, C.-H. (2018). Machine learning based big data processing framework for cancer diagnosis using hidden Markov model and GM clustering. Wireless personal communications, 102(3), 2099-2116.

Masood, R., & Aker, A. (2018). The Fake News Challenge: Stance Detection using Traditional Machine Learning Approaches. Paper presented at the KMIS.

Medina, R. Z., & Diaz, J. C. L. (2016). Social media use in crisis communication management: an opportunity for local communities? Social media and local governments (pp. 321-335): Springer.

Memon, A. M., Sharma, S. G., Mohite, S. S., & Jain, S. (2018). The role of online social networking on deliberate self-harm and suicidality in adolescents: A systematized review of literature. Indian journal of psychiatry, 60(4), 384.

Mitra, T., & Gilbert, E. (2015). Credbank: A large-scale social media corpus with associated credibility annotations. Paper presented at the Proceedings of the International AAAI Conference on Web and Social Media.

Mustapha, A., Mostafa, S. A., Hassan, M. H., Jubair, M. A., Khaleefah, S. H., & Hassan, M. H. (2020). Machine Learning Supervised Analysis for Enhancing Incident Management Process. International Journal, 8(1.1).

Nasir, J. A., Khan, O. S., & Varlamis, I. (2021). Fake news detection: A hybrid CNN-RNN based deep learning approach. International Journal of Information Management Data Insights, 1(1), 100007.

Shu, K., Wang, S., & Liu, H. (2019). Beyond news contents: The role of social context for fake news detection. Paper presented at the Proceedings of the twelfth ACM international conference on web search and data mining.

Stock, K., Pouchet, L.-N., & Sadayappan, P. (2012). Using machine learning to improve automatic vectorization. ACM Transactions on Architecture and Code Optimization (TACO), 8(4), 1-23.

Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. arXiv preprint arXiv:1409.3215.

Syarif, A. R., & Gata, W. (2017). Intrusion detection system using hybrid binary PSO and K-nearest neighborhood algorithm. Paper presented at the 2017 11th International Conference on Information & Communication Technology and System (ICTS).

Tretyakov, K. (2004). Machine learning techniques in spam filtering. Paper presented at the Data Mining Problem-oriented Seminar, MTAT.

Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., & Krause, A. (2018). Fake news detection in social networks via crowd signals. Paper presented at the Companion Proceedings of the The Web Conference 2018.

Vosoughi, S., Mohsenvand, M. N., & Roy, D. (2017). Rumor gauge: Predicting the veracity of rumors on Twitter. ACM transactions on knowledge discovery from data (TKDD), 11(4), 1-36.

Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151.

Wang, W. Y. (2017). " liar, liar pants on fire": A new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648.

Wang, Y., Zhang, Z., Zhang, L., Luo, Z., Shen, J., Lin, H., . . . Wang, X. (2018). Visible-light driven overall conversion of CO2 and H2O to CH4 and O2 on 3D-SiC@ 2D-MoS2 heterostructure. Journal of the American Chemical Society, 140(44), 14595-14598.

Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2018). Detection and resolution of rumours in social media: A survey. ACM Computing Surveys (CSUR), 51(2), 1-36.

Zubiaga, A., Kochkina, E., Liakata, M., Procter, R., & Lukasik, M. (2016). Stance classification in rumours as a sequential task exploiting the tree structure of social media conversations. arXiv preprint arXiv:1609.09028.


Refbacks

  • There are currently no refbacks.