Development of Fake News Model Using Machine Learning through Natural Language Processing
Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.
[1] N. J. Conroy, V. L. Rubin, and Y. Chen, “Automatic deception detection: Methods for finding fake news,” Proceedings of the Association for Information Science and Technology, vol. 52, no. 1, pp. 1–4, 2015.
[2] S. Feng, R. Banerjee, and Y. Choi, “Syntactic stylometry for deception detection,” in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, Association for Computational Linguistics, 2012, pp. 171–175.
[3] Tandoc, E. C., & Lim, Z. W., & Ling, R.(2018). Defining “fake news.” Digital Journalism, 6, 137-153.
[4] Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., & Krause, A. (2018). Fake News Detection in Social Networks via Crowd Signals, 517–524. https://doi.org/10.1145/3184558.3188722
[5] Lorent, S. (2019). Master thesis: Fake news detection using machine learning.
[6] Jain, A., & Kasbe, A. (2018, February). Fake news detection. In 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1-5). IEEE.
[7] Rubin, V., Conroy, N., Chen, Y., & Cornwell, S. (2016). Fake news or truth? Using satirical cues to detect potentially misleading news. In Proceedings of the Second Workshop on Computational Approaches to Deception Detection (pp. 7-17).
[8] Michal Lukasik, Trevor Cohn, and Kalina Bontcheva. 2015a. Classifying tweet level judgements of rumours in social media. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP‟15). 2590–2595. Michal
[9] Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva, Maria Liakata, and Rob Procter. Detection and resolution of rumours in social media: A survey. arXiv preprint arXiv:1704.00656, 2017
[10] Zhou, X., Cao, J., Jin, Z., Xie, F., Su, Y., Chu, D. ... & Zhang, J. (2015, May). Real-Time News Cer tification System on Sina Weibo. In Proceedings of the 24th International Conference on World Wide Web (pp. 983-988). ACM.
[11] Rubin, V. L., Chen, Y., & Conroy, N. J. (2015, November). Deception detection for news: three types of fakes. In Proceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community (p. 83). American Society for Information Science.
[12] Ruchansky, N., Seo, S., & Liu, Y. (2017, November). Csi: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 797-806). ACM.
[13] Janze, C., & Risius, M. (2017). Automatic Detection of Fake News on Social Media Platforms.
[14] Hiramath, C. K., & Deshpande, G. C. (2019, July). Fake News Detection Using Deep Learning Techniques. In 2019 1st International Conference on Advances in Information Technology (ICAIT) (pp. 411-415). IEEE.
[15] Bourgonje, P., Schneider, J. M., & Rehm, G. (2017). From clickbait to fake news detection: an approach based on detecting the stance of headlines to articles. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism (pp. 84-89).
[16] Bajaj, S. (n.d.). The Pope Has a New Baby! Fake News Detection Using Deep Learning. Retrieved from https://web.stanford.edu/class/cs224n/reports/2710385.pdf.
[17] Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011, June). Finding deceptive opinion spam by any stretch of the imagination. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies-volume 1 (pp. 309-319). Association for Computational Linguistics.
[18] Pratiwi, I. Y. R., Asmara, R. A., & Rahutomo, F. (2017, October). Study of hoax news detection using naïve Bayes classifier in Indonesian language. In 2017 11th International Conference on Information & Communication Technology and System (ICTS) (pp. 73-78). IEEE..
[19] Davuth, N., & Kim, S. R. (2013). Classification of malicious domain names using support vector machine and bi-gram method. International Journal of Security and Its Applications7(51-58)
[20] Banerjee, S., Chua, A. Y., & Kim, J. J. (2015, January). Using supervised learning to classify authentic and fake online reviews. In Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication (pp. 1-7).
[21] Torunoğlu, D., Çakirman, E., Ganiz, M. C., Akyokuş, S., & Gürbüz, M. Z. (2011, June). Analysis of preprocessing methods on classification of Turkish texts. In 2011 International Symposium on Innovations in Intelligent Systems and Applications (pp. 112-117). IEEE.
[22] Raulji, J. K., & Saini, J. R. (2016). Stop-word removal algorithm and its implementation for Sanskrit language. International Journal of Computer Applications, 150(2), 15-17.
[23] Vijayaraghavan, S., Wang, Y., Guo, Z., Voong, J., Xu, W., Nasseri, A. ... & Wadhwa, E. (2018). Fake News Detection with Different Models. arXiv preprint arXiv:2003.04978.
[24] Gilda, S. (2017, December). Evaluating machine learning algorithms for fake news detection. In 2017 IEEE 15th Student Conference on Research and Development (SCOReD) (pp. 110-115). IEEE.
[25] Nørregaard, J., Horne, B. D., & Adalı, S. (2019, July). NELA-GT-2018: A large multi-labelled news dataset for the study of misinformation in news articles. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 13, No. 01, pp. 630-638).
[26] Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F. … Zittrain, J. L. (2018a). The science of fake news. Science, 359(6380), 1094–1096. https://doi.org/10.1126/science.aao2998
[27] Kostakos, P., Nykanen, M., Martinviita, M., Pandya, A., & Oussalah, M. (2018, August). Meta-terrorism: identifying linguistic patterns in public discourse after an attack. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 1079-1083). IEEE.
[28] Samonte, M. J. C. (2018). Polarity analysis of editorial articles towards fake news detection. ACM International Conference Proceeding Series, 108–112. https://doi.org/10.1145/3230348.3230354.
[29] Gencheva, P. et al. (2017) ‘A context-aware approach for detecting worth-checking claims in political debates’, in International Conference Recent Advances in Natural Language Processing, RANLP. doi: 10.26615/978-954-452-049-6-037.
[30] Patwari, A., Goldwasser, D. and Bagchi, S. (2017) ‘TATHYA: A Multi-Classifier System for Detecting Check-Worthy Statements in Political Debates’, in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. doi: 10.1145/3132847.3133150.
[31] Gruppi, M., Horne, B. D., & Adalı, S. (2020). NELA-GT-2019: A Large Multi-Labelled News Dataset for the Study of Misinformation in News Articles. arXiv preprint arXiv:2003.08444.
[32] Jindal, N., & Liu, B. (2008, February). Opinion spam and analysis. In Proceedings of the 2008 international conference on web search and data mining (pp. 219-230).
[33] Pathak, A., & Srihari, R. K. (2019, July). BREAKING! Presenting Fake News Corpus for Automated Fact Checking. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (pp. 357-362).
[1] N. J. Conroy, V. L. Rubin, and Y. Chen, “Automatic deception detection: Methods for finding fake news,” Proceedings of the Association for Information Science and Technology, vol. 52, no. 1, pp. 1–4, 2015.
[2] S. Feng, R. Banerjee, and Y. Choi, “Syntactic stylometry for deception detection,” in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, Association for Computational Linguistics, 2012, pp. 171–175.
[3] Tandoc, E. C., & Lim, Z. W., & Ling, R.(2018). Defining “fake news.” Digital Journalism, 6, 137-153.
[4] Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., & Krause, A. (2018). Fake News Detection in Social Networks via Crowd Signals, 517–524. https://doi.org/10.1145/3184558.3188722
[5] Lorent, S. (2019). Master thesis: Fake news detection using machine learning.
[6] Jain, A., & Kasbe, A. (2018, February). Fake news detection. In 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1-5). IEEE.
[7] Rubin, V., Conroy, N., Chen, Y., & Cornwell, S. (2016). Fake news or truth? Using satirical cues to detect potentially misleading news. In Proceedings of the Second Workshop on Computational Approaches to Deception Detection (pp. 7-17).
[8] Michal Lukasik, Trevor Cohn, and Kalina Bontcheva. 2015a. Classifying tweet level judgements of rumours in social media. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP‟15). 2590–2595. Michal
[9] Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva, Maria Liakata, and Rob Procter. Detection and resolution of rumours in social media: A survey. arXiv preprint arXiv:1704.00656, 2017
[10] Zhou, X., Cao, J., Jin, Z., Xie, F., Su, Y., Chu, D. ... & Zhang, J. (2015, May). Real-Time News Cer tification System on Sina Weibo. In Proceedings of the 24th International Conference on World Wide Web (pp. 983-988). ACM.
[11] Rubin, V. L., Chen, Y., & Conroy, N. J. (2015, November). Deception detection for news: three types of fakes. In Proceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community (p. 83). American Society for Information Science.
[12] Ruchansky, N., Seo, S., & Liu, Y. (2017, November). Csi: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 797-806). ACM.
[13] Janze, C., & Risius, M. (2017). Automatic Detection of Fake News on Social Media Platforms.
[14] Hiramath, C. K., & Deshpande, G. C. (2019, July). Fake News Detection Using Deep Learning Techniques. In 2019 1st International Conference on Advances in Information Technology (ICAIT) (pp. 411-415). IEEE.
[15] Bourgonje, P., Schneider, J. M., & Rehm, G. (2017). From clickbait to fake news detection: an approach based on detecting the stance of headlines to articles. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism (pp. 84-89).
[16] Bajaj, S. (n.d.). The Pope Has a New Baby! Fake News Detection Using Deep Learning. Retrieved from https://web.stanford.edu/class/cs224n/reports/2710385.pdf.
[17] Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011, June). Finding deceptive opinion spam by any stretch of the imagination. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies-volume 1 (pp. 309-319). Association for Computational Linguistics.
[18] Pratiwi, I. Y. R., Asmara, R. A., & Rahutomo, F. (2017, October). Study of hoax news detection using naïve Bayes classifier in Indonesian language. In 2017 11th International Conference on Information & Communication Technology and System (ICTS) (pp. 73-78). IEEE..
[19] Davuth, N., & Kim, S. R. (2013). Classification of malicious domain names using support vector machine and bi-gram method. International Journal of Security and Its Applications7(51-58)
[20] Banerjee, S., Chua, A. Y., & Kim, J. J. (2015, January). Using supervised learning to classify authentic and fake online reviews. In Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication (pp. 1-7).
[21] Torunoğlu, D., Çakirman, E., Ganiz, M. C., Akyokuş, S., & Gürbüz, M. Z. (2011, June). Analysis of preprocessing methods on classification of Turkish texts. In 2011 International Symposium on Innovations in Intelligent Systems and Applications (pp. 112-117). IEEE.
[22] Raulji, J. K., & Saini, J. R. (2016). Stop-word removal algorithm and its implementation for Sanskrit language. International Journal of Computer Applications, 150(2), 15-17.
[23] Vijayaraghavan, S., Wang, Y., Guo, Z., Voong, J., Xu, W., Nasseri, A. ... & Wadhwa, E. (2018). Fake News Detection with Different Models. arXiv preprint arXiv:2003.04978.
[24] Gilda, S. (2017, December). Evaluating machine learning algorithms for fake news detection. In 2017 IEEE 15th Student Conference on Research and Development (SCOReD) (pp. 110-115). IEEE.
[25] Nørregaard, J., Horne, B. D., & Adalı, S. (2019, July). NELA-GT-2018: A large multi-labelled news dataset for the study of misinformation in news articles. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 13, No. 01, pp. 630-638).
[26] Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F. … Zittrain, J. L. (2018a). The science of fake news. Science, 359(6380), 1094–1096. https://doi.org/10.1126/science.aao2998
[27] Kostakos, P., Nykanen, M., Martinviita, M., Pandya, A., & Oussalah, M. (2018, August). Meta-terrorism: identifying linguistic patterns in public discourse after an attack. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 1079-1083). IEEE.
[28] Samonte, M. J. C. (2018). Polarity analysis of editorial articles towards fake news detection. ACM International Conference Proceeding Series, 108–112. https://doi.org/10.1145/3230348.3230354.
[29] Gencheva, P. et al. (2017) ‘A context-aware approach for detecting worth-checking claims in political debates’, in International Conference Recent Advances in Natural Language Processing, RANLP. doi: 10.26615/978-954-452-049-6-037.
[30] Patwari, A., Goldwasser, D. and Bagchi, S. (2017) ‘TATHYA: A Multi-Classifier System for Detecting Check-Worthy Statements in Political Debates’, in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. doi: 10.1145/3132847.3133150.
[31] Gruppi, M., Horne, B. D., & Adalı, S. (2020). NELA-GT-2019: A Large Multi-Labelled News Dataset for the Study of Misinformation in News Articles. arXiv preprint arXiv:2003.08444.
[32] Jindal, N., & Liu, B. (2008, February). Opinion spam and analysis. In Proceedings of the 2008 international conference on web search and data mining (pp. 219-230).
[33] Pathak, A., & Srihari, R. K. (2019, July). BREAKING! Presenting Fake News Corpus for Automated Fact Checking. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (pp. 357-362).
@article{"International Journal of Information, Control and Computer Sciences:80032", author = "Sajjad Ahmed and Knut Hinkelmann and Flavio Corradini", title = "Development of Fake News Model Using Machine Learning through Natural Language Processing", abstract = "Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.
", keywords = "Fake news detection, types of fake news, machine learning, natural language processing, classification techniques. ", volume = "14", number = "12", pages = "454-7", }
