News Science Quarterly (NS)

News Science Quarterly (NS)

New Strategies of Fake News Detection

Document Type : Original Article

Authors
Nastaran Zanjani 1
Fatemeh Pouramini 2
Zahra Askarinejadamiri 3
1 Corresponding author, Assistant professor, computer engineering department, Refah University College, Tehran, Iran. E-mail: zanjani@refah.ac.ir
2 Bachelor of computer engineering, computer engineering department, Refah University College, Tehran, Iran. E-mail: f.pour67@gmail.com
3 Assistant professor, computer engineering department, Refah University College, Tehran, Iran. E-mail: askarinejad@refah.ac.ir
10.22034/lrsi.2024.481772.1261
Abstract
Objective: This paper aims to provide a detailed overview of the most recent methods and strategies used for detecting fake news, especially in the context of rapid advancements in artificial intelligence and machine learning. With the widespread reach of fake news across social media and other digital platforms, this review focuses on identifying and evaluating effective approaches that can help tackle this growing problem.
Methods: Given the importance of detecting fake news, this paper reviews and compares various approaches utilized in this field. To this end, by studying articles published in online libraries and document repositories such as IEEE, Scopus, Elsevier, and others, we first explore different methods for detecting fake news. Then, we compare the various approaches of human-based detection with those of automated detection.
Results: The review shows that while conventional techniques like feature extraction and rule-based systems offer a good starting point, they often fall short when dealing with the complexity of modern disinformation. Deep learning models trained on large datasets have demonstrated promising results in detecting fake news, yet they still struggle with the subtlety of human-generated content and real-time applications. This highlights the need for more comprehensive solutions that can address these challenges.
Conclusions: The findings suggest that an integrated approach—one that combines language analysis, machine learning, and network-based methods—is essential for building effective fake news detection systems. As the field progresses, future research should focus on improving hybrid models, refining data quality, and incorporating user-centric insights to combat the spread of disinformation better. Combining large language models (LLMs) with context-aware systems offers a promising path for achieving higher precision in detecting both machine-generated and human-created fake news.
Keywords
Social Networks
Fake News
Artificial Intelligence
References
 Afroz, S., Brennan, M., & Greenstadt, R. (2012). Detecting hoaxes, frauds, and deception in writing style online. 2012 IEEE Symposium on security and Privacy. DOI: 10.1109/SP.2012.34
Ahmed, H. (2017). Detecting opinion spam and fake news using n-gram analysis and semantic similarity
Ansari, V., Moomenzadeh, H., & Arfaeinia, H. (1403). Fake News Detection Using Deep Neural Network CNN, 7th International Conference on Electrical Engineering, Computer Science, Mechanics, and Artificial Intelligence, (in Persian) https://civilica.com/doc/2046572.
Banerjee, R., Feng, S., Kang, J. S., & Choi, Y. (2014). Keystroke patterns as prosody in digital writings: A case study with deceptive reviews and essays. Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP(
Bigne, E., Andreu, L., Hernandez, B., & Ruiz, C. (2018). The impact of social media and offline influences on consumer behavior. An analysis of the low-cost airline industry. Current Issues in Tourism, 21(9), 1014-1032. https://doi.org/10.1080/13683500.2015.1126236
Castillo, C., Mendoza, M., & Poblete, B. (2011). Information credibility on Twitter. Proceedings of the 20th International Conference on World Wide Web.https://doi.org/10.1145/1963405.1963500
Chu, Z., Gianvecchio, S., Wang, H., & Jajodia, S. (2012). Detecting automation of Twitter accounts: Are you a human, bot, or cyborg? IEEE Transactions on dependable and secure computing, 9(6), 811-824. DOI: 10.1109/TDSC.2012.75
Di, R., Wang, H., Fang, Y., & Zhou, Y. (2018). Fake comment detection based on time series and density peaks clustering. Algorithms and Architectures for Parallel Processing: ICA3PP 2018 International Workshops, Guangzhou, China, November 15-17, 2018, Proceedings 18
Farajtabar, M., Yang, J., Ye, X., Xu, H., Trivedi, R., Khalil, E., Li, S., Song, L., & Zha, H. (2017). Fake news mitigation via point process based intervention. International conference on machine learning
Farokhian, M., Rafe, V., & Veisi, H. (2022). Fake news detection using parallel BERT deep neural networks. arXiv preprint arXiv:2204.04793.
Farzi, S. (2021). Utilizing Conditional Generative Adversarial Networks for Data Generation to Improve the Classification of Users Spreading Fake News, Iranian Journal of Information and Communication Technology, Volume: 13, Issue: 47. (in Persian). https://civilica.com/doc/1858925
Horne, B., & Adali, S. (2017). This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. Proceedings of the international AAAI conference on web and social media . https://doi.org/10.1609/icwsm.v11i1.14976
Izi, J., & Hassanpour, H. (2023). Detection and Classification of Fake News Using Natural Language Processing and Deep Learning, Sixth National Conference on New Technologies in Electrical Engineering and Computer (in Persian) Science, https://civilica.com/doc/1876620.
Izi, J., Rezaei, P. (1402). Improving Detection in Fake News Classification on Social Media Using K-Nearest Neighbors Algorithm, 9th International Conference on Interdisciplinary Research in Electrical Engineering, Computer Science, Mechanics, and Mechatronics in Iran and the Islamic World, (in Persian), https://civilica.com/doc/1994927.
Kaliyar, R. K., Goswami, A., & Narang, P. (2021). FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. Multimedia Tools and Applications, 80(8), 11765-11788.
Kuntur, S., Wróblewska, A., Paprzycki, M., & Ganzha, M. (2024). Fake News Detection: It's All in the Data! arXiv preprint arXiv:2407.02122.
Mukherjee, A., Liu, B., & Glance, N. (2012). Spotting fake reviewer groups in consumer reviews. Proceedings of the 21st International Conference on World Wide Web.https://doi.org/10.1145/2187836.2187863
Oshikawa, R., Qian, J., & Wang, W. Y. (2018). A survey on natural language processing for fake news detection. arXiv preprint arXiv:1811.00770.
Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003). Psychological aspects of natural language use: Our words, our selves. Annual review of psychology, 54(1), 547-577. https://doi.org/10.1146/annurev.psych.54.101601.145041
Russell, M. A. (2013). Mining the social web: data mining Facebook, Twitter, LinkedIn, Google+, GitHub, and more. " O'Reilly Media, Inc.".
Shafiei-Shara, Z., Ali-Asgari-Renani, F.,  & Mohammadi, S. (1403). Fake News Detection Using Artificial Intelligence Algorithms. 8th National Conference on Applied Research in Electrical Engineering, Mechanics, and Mechatronics, Tehran, (in Persian)  https://civilica.com/doc/2024063.
Shao, C., Ciampaglia, G. L., Flammini, A., & Menczer, F. (2016). Hoaxy: A platform for tracking online misinformation. Proceedings of the 25th International Conference Companion on World Wide Web. https://doi.org/10.1145/2872518.2890098
Shu, K., Bernard, H. R., & Liu, H. (2019). Studying fake news via network analysis: detection and mitigation. Emerging research challenges and opportunities in computational social network analysis and mining, 43-65.
Shushkevich, E., Alexandrov, M., & Cardiff, J. (2023). Improving multiclass classification of fake news using Bert-based models and CHATGPT-augmented data. Inventions, 8(5), 112. https://doi.org/10.3390/inventions8050112
Su, J., Cardie, C., & Nakov, P. (2023). Adapting fake news detection to the era of large language models. arXiv preprint arXiv:2311.04917.
Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., & Krause, A. (2017). Detecting fake news in social networks via crowdsourcing. arXiv preprint arXiv:1711.09025.
Vazifeh Aban, H., & Hasani Ahangar, M. R.; (1402). Using Majority Voting Technique in Classifying Fake News from Real News with Decision Tree, Logistic Regression, and K-Nearest Neighbors Algorithms, 6th National Conference on New Technologies in Electrical Engineering, Computer Science, and Mechanics of Iran, Tehran, (in Persian) https://civilica.com/doc/1744364.
Vicario, M. D., Quattrociocchi, W., Scala, A., & Zollo, F. (2019). Polarization and fake news: Early warning of potential misinformation targets. ACM Transactions on the Web (TWEB), 13(2), 1-22. https://doi.org/10.1145/3316809
Vyas, P., Liu, J., & Xu, S. (2024). Real-Time Fake News Detection on the X (Twitter): An Online Machine Learning Approach.
Wang, Y., & Li, B. (2015). Sentiment analysis for social media images. 2015 IEEE International Conference on Data Mining Workshop (ICDMW). DOI: 10.1109/ICDMW.2015.142
Yang, F., Liu, Y., Yu, X., & Yang, M. (2012). Automatic detection of rumor on sina weibo. Proceedings of the ACM SIGKDD workshop on mining data semantics. https://doi.org/10.1145/2350190.2350203
Zhang, X., & Ghorbani, A. A. (2020). An overview of online fake news: Characterization, detection, and discussion. Information Processing & Management, 57(2), 102025.
Zhao, Z., Resnick, P., & Mei, Q. (2015). Enquiring minds: Early detection of rumors in social media from enquiry posts. Proceedings of the 24th International Conference on world wide web.
Zhou, X., & Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys (CSUR), 53(5), 1-40. https://doi.org/10.1145/3395046
Zhu, Y., Li, Y., Wang, J., Gao, M., & Wei, J. (2024). FaKnow: A Unified Library for Fake News Detection. arXiv preprint arXiv:2401.16441.