Abstract
Fake news spreads quickly on online social media and adversely impacts political, social, religious, and economic stability. This necessitates an efficient fake news detector which is now feasible due to advances in natural language processing and artificial intelligence. However, existing fake news detection (FND) systems are built on tokenization, embedding, and structure-based feature extraction, and fail drastically in real life because of the difference in vocabulary and its distribution across various domains. This article evaluates the effectiveness of various categories of traditional features in cross-domain FND and proposes a new method. Our proposed method shows significant improvement over recent methods in the literature for cross-domain fake news detection in terms of widely used performance metrics.
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
Altay, S., Hacquin, A.S., Mercier, H.: Why do so few people share fake news? it hurts their reputation. New Media Soc. 24(6), 1461444820969893 (2020)
Parsons, D.D.: The impact of fake news on company value: evidence from tesla and galena biopharma (2020)
Braun, J.A., Eklund, J.L.: Fake news, real money: Ad tech platforms, profit-driven hoaxes, and the business of Journalism. Digit. J. 7(1), 1–21 (2019)
A brief history of fake news. https://www.cits.ucsb.edu/fake-news/brief-history
Brown, E.: Online fake news is costing us \(78\) billion globally each year (2019). https://www.zdnet.com/article/online-fake-news-costing-us-78-billion-globally-each-year/
Kshetri, N., Voas, J.: The economics of “fake news". IT Profess. 19(6), 8–12 (2017)
Park, S., Fisher, C., LEE, J., Mcguinness, K.: COVID-19: Australian news and misinformation. News Media Research Centre, University of Canberra (2020)
Kumar, A., Bhatia, M., Sangwan, S.R.: Rumour detection using deep learning and filter-wrapper feature selection in benchmark twitter dataset. Multimedia Tools Appl. 80, 1–18 (2021)
Verma, P.K., Agrawal, P., Amorim, I., Prodan, R.: Welfake: word embedding over linguistic features for fake news detection. IEEE Trans. Comput. Soc. Syst. 8(4), 881–893 (2021)
Balwant, M.K.: Bidirectional LSTM based on POS tags and CNN architecture for fake news detection. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–6. IEEE (2019)
Pérez-Rosas, V., Kleinberg, B., Lefevre, A., Mihalcea, R.: Automatic detection of fake news. arXiv preprint arXiv:1708.07104 (2017)
Janicka, M., Pszona, M., Wawer, A.: Cross-domain failures of fake news detection. Computación y Sistemas 23(3), 1089–1097 (2019)
Gautam, A., Jerripothula, K.R.: SGG: spinbot, grammarly and glove based fake news detection. In: 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), pp. 174–182. IEEE (2020)
Saikh, T., De, A., Ekbal, A., Bhattacharyya, P.: A deep learning approach for automatic detection of fake news. arXiv preprint arXiv:2005.04938 (2020)
Goel, P., Singhal, S., Aggarwal, S., Jain, M.: Multi domain fake news analysis using transfer learning. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1230–1237. IEEE (2021)
Silva, A., Luo, L., Karunasekera, S., Leckie, C.: Embracing domain differences in fake news: cross-domain fake news detection using multi-modal data. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 557–565 (2021)
Etymology- how words change over time. https://www.virtuescience.com/etymology-how-words-change-over-time.html
Ghori, K.M., Imran, M., Nawaz, A., Abbasi, R.A., Ullah, A., Szathmary, L.: Performance analysis of machine learning classifiers for non-technical loss detection. J. Ambient Intell. Human. Comput. 8, 16033–16048 (2020)
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 Singapore Pte Ltd.
About this paper
Cite this paper
Ferdush, J., Kamruzzaman, J., Karmakar, G., Gondal, I., Das, R. (2023). Identification of Fake News: A Semantic Driven Technique for Transfer Domain. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1793. Springer, Singapore. https://doi.org/10.1007/978-981-99-1645-0_47
Download citation
DOI: https://doi.org/10.1007/978-981-99-1645-0_47
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1644-3
Online ISBN: 978-981-99-1645-0
eBook Packages: Computer ScienceComputer Science (R0)