Text Analytics Solutions for the Control of Fake News: Materials and Methods
Abstract
Full Text:
PDFReferences
Maciej, S. (2018). FakeNewsCorpus: A dataset of millions of news articles scraped from a curated list of data sources. Retrieved from github:
Zhou, X., Zafarani, R., Shu, K., & Liu, H. (2019). Fake news: Fundamental theories, detection strategies and challenges. Proceedings of the 12th ACM International Conference on Web Search and Data Mining (pp. 836-837). Association for Computing Machinery, Inc.
Donepudi, P. K. (2019). Automation and Machine Learning in Transforming the Financial Industry. Asian Business Review, 129-138.
Alzubi, Jafar., Nayyar, Anand., & Kumar, Akshi (2018). Machine learning from theory to algorithms: an overview. Journal of Physics Conference Series.
Aniyath, A. (2019). A Survey on Fake Newa Detection by the Data Mining Perspective. International Journal of Information and Computer Science, 9-28.
Della, V. M., Tacchini, E., Moret, S., Ballarin, G., DiPierro, M., & De Alfaro, L. (2018). Automatic online fake news detection combining content and social signals. Proceedings of the 22st Conference of Open Innovations Association FRUCT (pp. 272–279). IEEE.
Kurasinski, L., & Mihailescu, C. (2020). Machine Learning explainability in text classification for Fake News detection. 19th IEEE International Conference on Machine Learning (pp. 775-781). IEEE.
Maniz, S. (2018). Detecting FAke News with Sentiment Analysis and Network Metadata. Earlham Historical Journal.
Reddy, H., Raj, N., Manali, G., & Basava, A. (2020). Tex-mining-based Fake News Deetection Using Ensemble Methods. International Journal of Automation and Computing, 210-221.
Khanam, Z., Alwasel, B. N., Sirafi, H., & Rashid, M. (2021). Fake News Detection Using Machine Learning Approaches. IOP Conference Series: Materials Science and Engineering. IOP.
Iftikhar, A., Muhammad Yousaf, Sukail, Y., & Muhammad, O. A. (2020). Fake NEws Detection Using Machine Learning Ensemble Methods. Complexity, 11 pages.
Dong-Ho, L., Yu-Ri, K., Hyeong-Jun, K., & Yu-Jun, Y. (2019). Feke News detection using Deep Learning . Journal of Information Processing Systems, 1119-1130.
Shalini, P., Sankeerthi, P., Subba, R. N., & Dinesh Acharya. (2022). Fake News Detection from Online media using Machine Learning Classifiers. 1st international Conference on Artificial Intelligence, Computational Electronics and Communication System (pp. 28-30). Manipal India: Journal of Physics: Conference Series.
Ali, H. H., & Heba, Y. A. (2022). Fake News Detection Based on the Machine Learning Model. Design Engineering, 1373-1378.
Haumahu, J. P., Silvester, D. H., & Yaddarabullah, Y. (2020). Fake news classification for Indonesian news using Extreme Gradient Boosting (XGBoost). The 5th Annual Applied Science and Engineering Conference. IOP Publishing .
Pritika, B., Preeti, S., & Raj, K. (2019). Fake News Detection using Bi-directional LSTM-Recurrent Neural Network. International Conference on recent trends in advanced computing 2019, ICRTAC 2019. India: Procedia Computer Science.
Zhibin, W., & Huatai, X. (2021). Performance comparison of different machine learning model in detecting fake news. Sweden: Open Access.
Kai, N., Sharon, L., & William, Y. W. (2020). Fakeddit. Retrieved from https://fakeddit.netlify.app/
William, Y. W. (2017). "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection. arXiv.
Rowan, Z., Ari, H., Hannah, R., Yonatan, B., Ali, F., Franziska, R., & Yejin, C. (2020). Defending Against Neural Fake News. arXiv.
Kai, S., Deepak, M., Suhang, W., Dongwon, L., & Huan, L. (2018). FakeNewsNet: A data respository with news content, social context and dynamic information for studying fake news on social media . Journal of computer science.
Dean, P., & Delip, R. (2017, June 15). Fake News Challenge Stage 1 (FNC-1): Stance Detection. Retrieved from Fake News Challenge: httpp://www.fakenewschallenge.org/
Nguyen, V., & Kyumin, L. (2020). Where Are Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News. arXiv.
Eugenio, T., Gabriele, B., Marco, L. D., Stefano, M., & Luca, d. A. (2017). Some Like it Hoax: Automated Fake News Detection in Social Networks. arXiv.
Jeppe, N., Benjamin, D. H., & Sibel, A. (2019). NELA-GT 2018: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles. arXiv.
Mauricio, G., Benjamin, D. H., & Sibel, A. (2019). NELA-GT-2019: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles. arXiv.
Maurico, G., Benjamin D, H., & Sibel, A. (2020). NELA-GT-2020: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles. arXiv.
Yingtong, D., Kai, S., Congying, X., Philip, S. Y., & Lichao, S. (2021). User Preference Aware Fake News Detection. arXiv.
Qiong, N., Juan, C., Yongchun, Z., Yanyan, W., & Jintao, L. (2022). MDFEND: Multi-domain Fake News Detection. arXiv.
Parth, P., Shivam, S., Srinivas, P., Vineeth, G., Gitanjali, K., Md, S. A., . . . Tanmoy, C. (2020). Fighting an Infodemic: Covid-19 Fake News Dataset. arXiv.
Yichuan, L., Bohan, J., Kia, S., & Huan, L. (2020). MM-COVID: A Multilingual and Multimodal Data Respository for Combating COVID-19 Disinformation. arXiv.
Mohamed, S. H., & Hassina, A. (2021). AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset. arXiv.
Zobaer, H., Ashraful, R., Saiful, I., & Sudipta, K. (2020). BanFakeNews: A Dataset for Detecting Fake News in Bangla. arXiv.
Jan, C. B., Julianne, A. T., & Charibeth, C. (2020). Localization of Fake News Detection via Multitask Transfer Learning. arXiv.
Refbacks
- There are currently no refbacks.
Abava Кибербезопасность IT Congress 2024
ISSN: 2307-8162