Dutta, A. K. et al. Optimal weighted extreme learning machine for cybersecurity fake news classification. Comput. Syst. Sci. Eng., 44 (3) (2023).
Nelson, J. L. & Lei, R. F. The effect of digital platforms on news audience behavior. Digit. Journal. 6 (5), 619–633 (2018).
Google Scholar
Nazari, Z., Oruji, M. & Jamali, H. R. News consumption and behavior of young adults and the issue of fake news. J. Inform. Sci. Theory Pract. 10 (2), 1–16 (2022).
Google Scholar
Ekbal, A. & Kumari, R. Dive into misinformation detection.
Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. Preprint at arXiv:1711.05101, (2017).
Levak, T. Disinformation in the new media system–Characteristics, forms, reasons for its dissemination and potential means of tackling the issue. Medijska istraživanja: znanstveno-stručni časopis za novinarstvo i medije, 26 (2), 29–58, (2020).
Google Scholar
Rubin, V. L. & Conroy, N. Discerning truth from deception: Human judgments and automation efforts. First Monday, 17 (5) (2012).
Fridkin, K., Kenney, P. J. & Wintersieck, A. Liar, liar, pants on fire: How fact-checking influences citizens’ reactions to negative advertising. Political Communication. 32 (1), 127–151 (2015).
Google Scholar
Flintham, M. et al. Falling for fake news: Investigating the consumption of news via social media, in Proceedings of the CHI conference on human factors in computing systems, 2018, 1–10. (2018).
Ashforth, B. E. & Mael, F. Social identity theory and the organization. Acad. Manag. Rev. 14 (1), 20–39 (1989).
Google Scholar
Yeneakal, T. Y. AI and Extremism in Social Networks (The University of Bergen, 2019).
Akhter, M. P. et al. Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media. PeerJ Comput. Sci. 7, e425 (2021).
Google Scholar
Zhou, X. & Zafarani, R. A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Comput. Surv. (CSUR). 53 (5), 1–40 (2020).
Google Scholar
Tabassum, N. et al. Punny_punctuators@ dravidianlangtech-eacl2024: Transformer-based approach for detection and classification of fake news in malayalam social media text, in Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, 180–186. (2024).
Shahzad, R. K. & Lavesson, N. Comparative analysis of voting schemes for ensemble-based malware detection. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 4 (1), 98–117 (2013).
Google Scholar
Mahlous, A. R. The impact of fake news on social media users during the COVID-19 pandemic, health, political and religious conflicts: A deep look. Int. J. Psychol. Relig. 5, 481–492 (2024).
Amjad, M. et al. Bend the truth: Benchmark dataset for fake news detection in Urdu Language and its evaluation. J. Intell. Fuzzy Syst. 39 (2), 2457–2469 (2020).
Google Scholar
Kausar, N. et al. Multiclass skin cancer classification using ensemble of fine-tuned deep learning models. Appl. Sci. 11 (22), 10593 (2021).
Google Scholar
Humayoun, M. The 2021 Urdu fake news detection task using supervised machine learning and feature combinations. Preprint at preprint arXiv:2204.03064 (2022).
Balaji, N. N. A. & Bharathi, B. SSNCSE_NLP@ fake news detection in the Urdu language (UrduFake) 2020. Health (Irvine Calif). 100, 100 (2020).
Google Scholar
Ameer, I., Capetillo, C. P., Gómez-Adorno, H. & Sidorov, G. Automatic fake news detection in urdu language using transformers, in FIRE (Working Notes), 1127–1134. (2021).
Kalraa, S., Vermaa, P., Sharma, Y. & Chauhan, G. S. Ensembling of various transformer based models for the fake news detection task in the Urdu language, in FIRE (Working Notes), 1175–1181. (2021).
Harris, S., Liu, J., Hadi, H. J. & Cao, Y. Ax-to-Grind Urdu: Benchmark dataset for Urdu fake news detection. Preprint at arXiv:2403.14037, (2024).
Patil, D. R. Fake news detection using majority voting technique. Preprint at arXiv:2203.09936, (2022).
Vaswani, A. et al. Attention is all you need. Adv. Neural. Inf. Process. Syst., 30, (2017).
Pan, S. J. & Yang, Q. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22 (10), 1345–1359 (2009).
Google Scholar
Devlin, J., Chang, M. W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. Preprint at arXiv:1810.04805, (2018).
Praseed, A., Rodrigues, J. & Thilagam, P. S. Hindi fake news detection using transformer ensembles. Eng. Appl. Artif. Intell. 119, 105731 (2023).
Google Scholar
Clark, K., Luong, M. T., Le, Q. V. & Manning, C. D. Electra: Pre-training text encoders as discriminators rather than generators. Preprint at arXiv:10555, 2020. (2003).
Cortiz, D. Exploring transformers models for emotion recognition: A comparision of BERT, DistilBERT, RoBERTa, XLNET and ELECTRA, in Proceedings of the 3rd International Conference on Control, Robotics and Intelligent System, 2022, 230–234. (2022).
Szczepański, M., Pawlicki, M., Kozik, R. & Choraś, M. New explainability method for BERT-based model in fake news detection. Sci. Rep. 11 (1), 23705 (2021).
Google Scholar
Liu, Y. et al. Roberta: A robustly optimized bert pretraining approach. Preprint at arXiv:1907.11692, (2019).
Sajjad, H., Dalvi, F., Durrani, N. & Nakov, P. On the effect of dropping layers of pre-trained transformer models. Comput. Speech Lang. 77, 101429 (2023).
Google Scholar
Badam, J., Bonagiri, A., Raju, K. & Chakraborty, D. Aletheia: A fake news detection system for Hindi, in Proceedings of the 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD), 255–259. (2022).
Kareem, I. & Awan, S. M. Pakistani media fake news classification using machine learning classifiers, in international conference on innovative computing (ICIC), 2019: IEEE, 1–6. (2019).
Sharma, R. & Arya, A. Lfwe: linguistic feature based word embedding for Hindi fake news detection. ACM Trans. Asian Low-Resource Lang. Inform. Process. 22 (6), 1–24 (2023).
Google Scholar
Truică, C. O., Apostol, E. S., Nicolescu, R. C. & Karras, P. MCWDST: A minimum-cost weighted directed spanning tree algorithm for real-time fake news mitigation in social media. IEEE Access. 11, 125861–125873 (2023).
Google Scholar
Reshi, J. A. & Ali, R. Defending against misinformation: Evaluating transformer architectures for quick misinformation detection on social media. Procedia Comput. Sci. 235, 2909–2919 (2024).
Google Scholar
Al-Alshaqi, M., Rawat, D. B. & Liu, C. Ensemble techniques for robust fake news detection: Integrating transformers, natural language processing, and machine learning. Sensors, 24 (18) 6062 (2024).
Google Scholar
Zhang, Y., Ma, X., Wu, J., Yang, J. & Fan, H. Heterogeneous subgraph transformer for fake news detection, in Proceedings of the ACM on Web Conference 2024, 1272–1282. (2024).
Mallik, A. & Kumar, S. Word2Vec and LSTM based deep learning technique for context-free fake news detection. Multimed. Tools Appl. 83 (1), 919–940 (2024).
Google Scholar
Coban, Ö., Truică, C. O. & Apostol, E. S. CONTAIN: A community-based algorithm for network immunization. Preprint at arXiv:2303.01934, (2023).
Williamson, L. & Wooden, S. L. Facets of english spelling, (1980).
Riaz, K. Baseline for Urdu IR evaluation, in Proceedings of the 2nd ACM workshop on Improving non english web searching, 97–100. (2008).
Bhawal, S. & Roy, P. K. Fake news detection in Urdu language using BERT, in FIRE (Working Notes), 1182–1189. (2021).
Xue, J. et al. Detecting fake news by exploring the consistency of multimodal data. Inf. Process. Manag. 58 (5), 102610 (2021).
Google Scholar
Martín, A. G. et al. Suspicious news detection through semantic and sentiment measures. Eng. Appl. Artif. Intell. 101, 104230 (2021).
Google Scholar
Kishwar, A. & Zafar, A. Predicting fake news using glove and bert embeddings, in 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), 2021: IEEE, 1–6. (2021).
Amjad, M. et al. Overview of the shared task on fake news detection in Urdu at Fire 2021. Preprint at arXiv:2207.05133, (2022).
Amjad, M., Sidorov, G., Zhila, A., Gelbukh, A. & Rosso, P. UrduFake@ FIRE2020: shared track on fake news identification in Urdu, in Proceedings of the 12th Annual Meeting of the Forum for Information Retrieval Evaluation, 37–40. (2020).
Lina, N., Fua, S. & Jianga, S. Fake news detection in the urdu language using CharCNN-RoBERTa. Health 100 100 (2020).
Khiljia, A. F. U. R., Laskara, S. R., Pakraya, P. & Bandyopadhyaya, S. Urdu fake news detection using generalized autoregressors, (2020).
Kumar, A., Saumya, S., Singh, J. P. & AI-NLP@ UrduFake, N. I. T. P. -FIRE2020: Multi-layer dense neural network for fake news detection in Urdu news articles, in FIRE (Working Notes), 458–463. (2020).
Balouchzahi, F., Shashirekha, H. L. & Sidorov, G. MUCIC at CheckThat! 2021: FaDo-Fake news detection and domain identification using transformers ensembling, in CLEF (Working Notes), 455–464. (2021).
Balouchzahi, F. & Shashirekha, H. Learning models for Urdu fake news detection, in FIRE (Working Notes), 474–479. (2020).
Glazkova, A. & Glazkov, M. Detecting generated scientific papers using an ensemble of transformer models. Preprint at arXiv:2209.08283, (2022).
Balouchzahi, F., Shashirekha, H. L. & Sidorov, G. Ensembled feature selection for Urdu fake news detection, in FIRE (Working Notes), 1117–1126. (2021).
Saha, S. & Ekbal, A. Combining multiple classifiers using vote based classifier ensemble technique for named entity recognition. Data Knowl. Eng. 85, 15–39 (2013).
Google Scholar
Acheampong, F. A., Nunoo-Mensah, H. & Chen, W. Transformer models for text-based emotion detection: A review of BERT-based approaches. Artif. Intell. Rev. 54 (8), 5789–5829 (2021).
Google Scholar
Brown, G. & Kuncheva, L. I. good and bad diversity in majority vote ensembles, in Multiple Classifier Systems: 9th International Workshop, MCS 2010, Cairo, Egypt, April 7–9, 2010. Proceedings 9 124–133. (Springer, 2010).
Kioutsioukis, I. & Galmarini, S. De praeceptis ferendis: Good practice in multi-model ensembles. Atmos. Chem. Phys. 14 (21), 11791–11815 (2014).
Google Scholar
Sharif, O. & Hoque, M. M. Tackling cyber-aggression: Identification and fine-grained categorization of aggressive texts on social media using weighted ensemble of transformers. Neurocomputing 490, 462–481 (2022).
Google Scholar
Meetei, L. S., Singh, T. D., Borgohain, S. K. & Bandyopadhyay, S. Low resource Language specific pre-processing and features for sentiment analysis task. Lang. Resour. Evaluation. 55 (4), 947–969 (2021).
Google Scholar
Chicco, D. & Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom. 21, 1–13 (2020).
Google Scholar
Boughorbel, S., Jarray, F. & El-Anbari, M. Optimal classifier for imbalanced data using Matthews correlation coefficient metric. PloS One. 12 (6), e0177678 (2017).
Google Scholar
More, S. S. & Gaikwad, P. P. Trust-based voting method for efficient malware detection. Procedia Comput. Sci. 79, 657–667 (2016).
Google Scholar
Reddy, S. M., Suman, C., Saha, S. & Bhattacharyya, P. A GRU-based fake news prediction system: Working notes for Urdu Fake-FIRE 2020, in FIRE (Working Notes), 464–468. (2020).