The week's pick
Random Articles
Reseach Article
COVFake: A Word Embedding Coupled with LSTM Approach for COVID Related Fake News Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 174 - Number 10 |
| Year of Publication: 2021 |
| Authors: Muhammad Usama Islam, Md. Mobarak Hossain, Mohammod Abul Kashem |
10.5120/ijca2021920977
|
Muhammad Usama Islam, Md. Mobarak Hossain, Mohammod Abul Kashem . COVFake: A Word Embedding Coupled with LSTM Approach for COVID Related Fake News Detection. International Journal of Computer Applications. 174, 10 ( Jan 2021), 1-5. DOI=10.5120/ijca2021920977
Abstract
Coronavirus (COVID) took a substantial toll on human life with its unprecedented arrival in human sphere. An unforeseen circumstance which lead to various types of guidelines of procedures directed from the monitoring bodies including face-mask guideline, hand-wash guidelines and so forth. However, with the advent of this disease, misinformation became a causal factor to this scenario albeit claiming millions of life in the process. A threatening disease coupled with misinformation has created a disastrous scenario in human life. Our approach, exploits the power of natural language processing, specifically word embedding and Long short term memory (LSTM) to detect the COVID related fake news. Our model performs with a promising accuracy of 96% which concludes our effort of contribution to this massive outbreak from a linguistic standpoint.
References
- Iftikhar Ahmad, Muhammad Yousaf, Suhail Yousaf, and Muhammad Ovais Ahmad. Fake news detection using machine learning ensemble methods. Complexity, 2020, 2020.
- Hadeer Ahmed, Issa Traore, and Sherif Saad. Detection of online fake news using n-gram analysis and machine learning techniques. In International conference on intelligent, secure, and dependable systems in distributed and cloud environments, pages 127–138. Springer, 2017.
- Hunt Allcott and Matthew Gentzkow. Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2):211–36, 2017.
- Oberiri Destiny Apuke and Bahiyah Omar. Fake news and covid-19: modelling the predictors of fake news sharing among social media users. Telematics and Informatics, page 101475, 2020.
- Marina Azzimonti and Marcos Fernandes. Social media networks, fake news, and polarization. Technical report, National Bureau of Economic Research, 2018.
- Sumit Banik. Covid fake news dataset, November 2020.
- Sahil Chopra, Saachi Jain, and John Merriman Sholar. Towards automatic identification of fake news: Headline-article stance detection with lstm attention models. In Stanford CS224d Deep Learning for NLP final project, 2017.
- Sahar Ghannay, Benoit Favre, Yannick Esteve, and Nathalie Camelin. Word embedding evaluation and combination. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 300–305, 2016.
- Shalini Ghosh, Oriol Vinyals, Brian Strope, Scott Roy, Tom Dean, and Larry Heck. Contextual lstm (clstm) models for large scale nlp tasks. arXiv preprint arXiv:1602.06291, 2016.
- Yoav Goldberg and Omer Levy. word2vec explained: deriving mikolov et al.’s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722, 2014.
- Mykhailo Granik and Volodymyr Mesyura. Fake news detection using naive bayes classifier. In 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), pages 900–903. IEEE, 2017.
- Wahab Khan, Ali Daud, Jamal A Nasir, and Tehmina Amjad. A survey on the state-of-the-art machine learning models in the context of nlp. Kuwait journal of Science, 43(4), 2016.
- Anuradha Khattar, Priti Rai Jain, and SMK Quadri. Effects of the disastrous pandemic covid 19 on learning styles, activities and mental health of young indian students-a machine learning approach. In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), pages 1190–1195. IEEE, 2020.
- Phong Le and Willem Zuidema. Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive lstms. arXiv preprint arXiv:1603.00423, 2016.
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.
- Salman Bin Naeem, Rubina Bhatti, and Aqsa Khan. An exploration of how fake news is taking over social media and putting public health at risk. Health Information & Libraries Journal, 2020.
- Usha M Rodrigues and Jian Xu. ¡? covid19?¿ regulation of covid-19 fake news infodemic in china and india. Media International Australia, 177(1):125–131, 2020.
- Max Roser, Hannah Ritchie, Esteban Ortiz-Ospina, and Joe Hasell. Coronavirus pandemic (covid-19). OurWorld in Data, 2020.
- Natali Ruchansky, Sungyong Seo, and Yan Liu. Csi: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 797–806, 2017.
- Jacques Savoy. A stemming procedure and stopword list for general french corpora. Journal of the American Society for Information Science, 50(10):944–952, 1999.
- Kazuki Shimizu. 2019-ncov, fake news, and racism. The lancet, 395(10225):685–686, 2020.
- Richard Socher, Yoshua Bengio, and Christopher D Manning. Deep learning for nlp (without magic). In Tutorial Abstracts of ACL 2012, pages 5–5. 2012.
- Edson C Tandoc Jr, ZhengWei Lim, and Richard Ling. Defining fake news a typology of scholarly definitions. Digital journalism, 6(2):137–153, 2018.
- Muhammad Umer, Zainab Imtiaz, Saleem Ullah, Arif Mehmood, Gyu Sang Choi, and Byung-Won On. Fake news stance detection using deep learning architecture (cnn-lstm). IEEE Access, 8:156695–156706, 2020.
- Sander van der Linden, Jon Roozenbeek, and Josh Compton. Inoculating against fake news about covid-19. Frontiers in Psychology, 11:2928, 2020.
- Michela Del Vicario, Walter Quattrociocchi, Antonio Scala, and Fabiana Zollo. Polarization and fake news: Early warning of potential misinformation targets. ACM Transactions on the Web (TWEB), 13(2):1–22, 2019.
- William Yang Wang. ” liar, liar pants on fire”: A new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648, 2017.
- Liang Wu and Huan Liu. Tracing fake-news footprints: Characterizing social media messages by how they propagate. In Proceedings of the eleventh ACM international conference on Web Search and Data Mining, pages 637–645, 2018.
- Jia Xue, Junxiang Chen, Ran Hu, Chen Chen, ChengDa Zheng, and Tingshao Zhu. Twitter discussions and concerns about covid-19 pandemic: Twitter data analysis using a machine learning approach. arXiv preprint arXiv:2005.12830, 2020.
- SM Zobaed, Mohsen Amini Salehi, Albert Zomaya, and Sherif Sakr. Big data in the cloud., 2019.
Index Terms
Keywords