Fake news detection using recurrent neural network based on bidirectional LSTM and GloVe
Tóm tắt
In the world of technology, the electronic and technical development of the fields of communication and the internet has increased, which has caused a renaissance in the virtual world. This development has greatly impacted virtual communities for the ease and speed of communication and information transfer through social media platforms, making these platforms likable and easy to use. The social network faces major challenges due to its extensive use. As a result, many people have become involved in cybercrimes. There are accounts on the internet that are malicious. Platforms for social networking online, such as Facebook and Twitter, allow all users to freely generate and consume massive volumes of material regardless of their traits. While individuals and businesses utilize this information to gain a competitive edge, spam or phony users create important data. According to estimates, 1 in 200 posts on social media contain spam, and 1 in 21 tweets contain spam. The problem was centered around the accuracy of detecting false news and correcting it or preventing its dissemination before it spread in the network. A new method is given based on improving the false news detection system; the level of improvement was significant in the preprocessing stage where Glove is used, which is an unsupervised learning algorithm developed by researchers at Stanford University aiming to generate word embeddings by aggregating global word co-occurrence matrices from a given corpus. The basic idea behind the GloVe word embedding is to derive the relationship between the words from statistics. The proposed method contains deep learning algorithms of convolutional neural network (CNN), deep neural network (DNN), and long short-term memory (LSTM). The RNN with GloVe in the preprocessing stage using the Curpos fake news dataset to enhance the system, due to the sequential processes and classification, has the highest accuracy of 98.974%.
Tài liệu tham khảo
Abozinadah EA (2016) Improved micro-blog classification for detecting abusive Arabic Twitter accounts. Int J Data Mining Knowl Manage Process (IJDKP). https://doi.org/10.5121/ijdkp.2016.6602
Alabbas W, Al-Khateeb HM, Mansour A, Epiphaniou G, Frommholz I (2017) Classification of colloquial English tweets in real-time to detect high-risk floods. Int Conf Soc Media Wearable Web Anal Soc Media 2017:1–8
Albadi N, Kurdi M, Mishra S (2018). Are they our brothers? analysis and detection of religious hate speech in the English twittersphere. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 69–76). IEEE.
Alkhatib M, Shaalan K (2017). Natural language processing for Arabic metaphors: a conceptual approach. In: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016 2 (pp. 170-181). Springer International Publishing.https://doi.org/10.1007/978-3-319-48308-5.
Alomari KM, ElSherif HM, Shaalan K (2017). Arabic tweets sentimental analysis using machine learning. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 602-610). Cham: Springer International Publishing.https://doi.org/10.1007/978-3-319-60042-0.
Augenstein I, Ruder S, Søgaard A (2018). Multitask learning of pairwise sequence classification tasks over disparate label spaces. arXiv preprint arXiv:1802.09913.
Aurpa TT, Sadik R, Ahmed MS (2022) Abusive Bangla comments detection on Facebook using transformer-based deep learning models. Soc Netw Anal Min 12(1):24
Bahad P, Saxena P, Kamal R (2019) Fake news detection using bi-directional LSTM-recurrent neural network. Proc Comput Sci 165(2019):74–82. https://doi.org/10.1016/j.procs.2020.01.072
Bin Hathlian NF, Hafez AM (2017) Subjective text mining for English social media. Int J Semant Web Inf Syst 13(2):1–13. https://doi.org/10.4018/IJSWIS.2017040101
Bohra A, Vijay D, Singh V, Akhtar SS, Shrivastava M (2018). A dataset of hindi-english code-mixed social media text for hate speech detection. In: Proceedings of the second workshop on computational modeling ofpeople’s opinions, personality, and emotions in social media (pp. 36–41).
Boudchiche M, Mazroui A, Bebah MOAO, Lakhouaja A, Boudlal A (2017) AlKhalil morpho sys 2: a robust Arabic morpho-syntactic analyzer. J King Saud Univ-Comput Inf Sci 29(2):141–146. https://doi.org/10.1016/j.jksuci.2016.05.002
Cuadrado RRJJ, Monzón MD, Users L (2016) Analysis of the impact of file formats for open data analytics efficiency: a case study with R. GSTF J Comput. 5(1):40–44. https://doi.org/10.5176/2251-3043
Dhiman P, Kaur A, Iwendi C, Mohan SK (2023) A scientometric analysis of deep learning approaches for detecting fake news. Electronics 12(4):948
Di Capua M, Di Nardo E, Petrosino A (2016). Unsupervised cyber bullying detection in social networks. In: 2016 23rd International conference on pattern recognition (ICPR) (pp. 432–437). IEEE.
Galal M, Madbouly MM, El-Zoghby A (2019) Classifying English text using deep learning. J Theor Appl Inf Technol 97(23):3412–3422
Galán-García P, Puerta JGDL, Gómez CL, Santos I, Bringas PG (2016) Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying. Logic J IGPL 24(1):42–53
Galery T, Charitos E, Tian Y (2018). Aggression identification and multi lingual word embeddings. In: Proceedings of the first workshop on trolling, aggression and cyberbullying (TRAC-2018) (pp. 74-79).
Shaalan K, Siddiqui S, Alkhatib M, Abdel Monem A (2019). Challenges in arabic natural language processing. In: Computational linguistics, speech and image processing for arabic language (pp. 59-83).https://doi.org/10.1142/9789813229396_0003.
Suyanto S (2020). Synonyms-based augmentation to improve fake news detection using bidirectional LSTM. In: 2020 8th International Conference on Information and Communication Technology (ICoICT) (pp. 1-5). IEEE.https://doi.org/10.1109/ICoICT49345.2020.9166230
Guellil I, Saâdane H, Azouaou F, Gueni B, Nouvel D (2021) Arabic natural language processing: an overview. J King Saud Univ-Comput Inf Sci 33(5):497–507. https://doi.org/10.1016/j.jksuci.2019.02.006
Habash N (2007). English morphological representations for machine translation. In: English computational morphology (pp. 263–285). Springer, Dordrecht.
Haidar B, Chamoun M, Serhrouchni A (2018). English cyberbullying detection: using deep learning. In: 2018 7th International Conference on Computer and Communication Engineering (ICCCE) (pp. 284–289). IEEE.
Hidalgo JMG, Díaz AAC (2012) Combining Predation Heuristics and Chat-Like Features in Sexual Predator Identification. In: CLEF (Online Working Notes/Labs/Workshop).
Joachims T (1998, April). Text categorization with support vector machines: learning with many relevant features. In: European conference on machine learning (pp. 137–142). Springer, Berlin, Heidelberg.
Kanan T, Sadaqa O, Almhirat A, Kanan E (2019). Arabic light stemming: a comparative study between p-stemmer, khoja stemmer, and light10 stemmer. In: 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS) (pp. 511-515). IEEE.https://doi.org/10.1109/SNAMS.2019.8931842.
Kanan T, Fox EA (2016) Automated English Text Classification with P-Stemmer. Mach Learn Tailored News Article Taxonomy 114:0106
Kanan T, Sadaqa O, Aldajeh A, Alshwabka H, AlZu’bi S, Elbes M, Alia MA (2019, April). A review of natural language processing and machine learning tools used to analyze arabic social media. In: 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp. 622-628). IEEE.https://doi.org/10.1109/JEEIT.2019.8717369.
Kang IS, Kim CK, Kang SJ, Na SH (2012). IR-based k-nearest neighbor approach for identifying abnormal chat users. In: CLEF (Online Working Notes/Labs/Workshop).
Kim KH, Jeong CS (2019). Fake news detection system using article abstraction. In: JCSSE 2019–16th International Joint Conference on Computer Science and Software Engineering: knowledge Evolution Towards Singularity of Man-Machine Intelligence, 209–212. https://doi.org/10.1109/JCSSE.2019.8864154
Klubička F, Fernandez R (2018). Examining a hate speech corpus for hate speech detection and popularity prediction. arXiv preprint arXiv:1805.04661.
Kohavi R, Quinlan JR (2002). Data mining tasks and methods: classification: decision-tree discovery. In: Handbook of data mining and knowledge discovery (pp. 267–276).
Kratzke N (2017) The# btw17 Twitter dataset–recorded tweets of the federal election campaigns of 2017 for the 19th German Bundestag. Data 2(4):34
Kwaik KA, Saad M, Chatzikyriakidis S, Dobnik S (2018). Shami: a corpus of levantine arabic dialects. In Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018).
Larabi Marie-Sainte S, Alalyani N, Alotaibi S, Ghouzali S, Abunadi I (2019) English natural language processing and machine learning-based systems. IEEE Access 7:7011–7020
Larkey LS, Ballesteros L, Connell ME (2002). Improving stemming for English information retrieval: light stemming and co-occurrence analysis. In: Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval (pp. 275–282).
LSTM-RNN 2017.pdf. (n.d.).
Luong MT, Le QV, Sutskever I, Vinyals O, Kaiser L (2015). Multi-task sequence to sequence learning. arXiv preprint arXiv:1511.06114.
Malmasi S, Zampieri M (2018) Challenges in discriminating profanity from hate speech. J Exp Theor Artif Intell 30(2):187–202
Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113
Mitchell TM (2006) The discipline of machine learning, vol 9. Carnegie Mellon University, School of Computer Science, Machine Learning Department, Pittsburgh
Mustafa M, Eldeen AS, Bani-Ahmad S, Elfaki AO (2017) A comparative survey on english stemming: approaches and challenges. Intell Inf Manag 09(02):39–67. https://doi.org/10.4236/iim.2017.92003
Nobata C, Tetreault J, Thomas A, Mehdad Y, Chang Y (2016). Abusive language detection in online user content. In: Proceedings of the 25th international conference on world wide web (pp. 145–153).
Node XL (2021). Your social network analysis tool for social media. Available: https://www.smrfoundation.org/nodexl/. Last accessed 2022.
Park JH, Shin J, Fung P (2018). Reducing gender bias in abusive language detection. arXiv preprint arXiv:1808.07231.
Peng H, Thomson S, Smith NA (2017). Deep multitask learning for semantic dependency parsing. arXiv preprint arXiv:1704.06855.
Qawasmeh E, Tawalbeh M, Abdullah M (2019). Automatic identification of fake news using deep learning. In: 2019 Sixth international conference on social networks analysis, Management and Security (SNAMS) (pp. 383-388). IEEE.https://doi.org/10.1109/SNAMS.2019.8931873
Ross B, Rist M, Carbonell G, Cabrera B, Kurowsky N, Wojatzki M (2017). Measuring the reliability of hate speech annotations: The case of the european refugee crisis. arXiv preprint arXiv:1701.08118.
Ruder S, Bingel J, Augenstein I, Søgaard A (2019). Latent multitask architecture learning. In: Proceedings of the AAAI Conference on Artificial Intelligence 33(01): 4822–4829.
Saberi B, Saad S (2017) Sentiment analysis or opinion mining: a review. Int J Adv Sci Eng Inform Technol 7:1660–1667
Saeed S, Suayyid SA, Al-Ghamdi MS, Al-Muhaisen H, Almuhaideb AM (2023) A systematic literature review on cyber threat intelligence for organizational cybersecurity resilience. Sensors 23(16):7273
Sallam RM, Mousa HM, Hussein M (2016) Improving Arabic text categorization using normalization and stemming techniques. Int J Comput Appl 135(2):38–43
Sanguinetti M, Poletto F, Bosco C, Patti V, Stranisci M (2018, May). An italian twitter corpus of hate speech against immigrants. In: Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018).
Sastrawan IK, Bayupati IPA, Arsa DMS (2022) Detection of fake news using deep learning CNN–RNN based methods. ICT Express 8(3):396–408
Saura JR, Ribeiro-Soriano D, Palacios-Marqués D (2022) Evaluating security and privacy issues of social networks based information systems in Industry 40. Enterprise Inform Syst 16(10–11):1694–1710
Schmidt A, Wiegand M (2017). A survey on hate speech detection using natural language processing. In: Proceedings of the fifth international workshop on natural language processing for social media (pp. 1-10).
Shaalan K, Siddiqui S, Alkhatib M, Monem AA (2018). Computational linguistics, speech and image processing for arabic language.
Smola A, Vishwanathan SVN (2008) Introduction to machine learning. Cambridge University, UK 32(34):2008
Soral W, Bilewicz M, Winiewski M (2018) Exposure to hate speech increases prejudice through desensitization. Aggressive Behav 44(2):136–146
Tarabasz A (2019) Corporate social responsibility in times of internet (in) security. In Responsible organizations in the global context, Palgrave Macmillan, Cham
Tokunaga RS (2010) Following you home from school: A critical review and synthesis of research on cyberbullying victimization. Comput Hum Behav 26(3):277–287
Vilariño D, Castillo E, Pinto D, Olmos I, León S (2012) Information retrieval and classification based approaches for the sexual predator identification. Faculty of Computer Science, Mexico
Waseem Z, Hovy D (2016). Hateful symbols or hateful people? predictive features for hate speech detection on twitter. In Proceedings of the NAACL student research workshop (pp. 88–93).
Wylde V, Rawindaran N, Lawrence J, Balasubramanian R, Prakash E, Jayal A, Platts J (2022) Cybersecurity, data privacy and blockchain: a review. SN Comput Sci 3(2):127