Abstract
Online social network platforms are utilized efficiently by massive users to read and disseminate the news in the form of text, image, audio and video. So, it is necessary to validate the genuineness of the news at an initial stage to avoid spreading fake news. Many existing works focused on textual content, they employed a pretrained word embedding and language models to capture the semantic and contextual information, respectively, for fake news identification. Though the existing text-based models achieve better predictions, still it has some limitations as follows: lacuna in extracting the efficient context-based features, pretrained on smaller corpus and static-masking utilization. To address this, we propose a Content-based Transfer Learning framework for Fake News Detection (CTrL-FND) which contains a word embedding block (WEB) and a classification block (CLB). In WEB, a transfer learning pretrained model, named RoBERTa, is employed for efficient context-based word representation since it is pretrained on larger corpus, eliminates the next sentence prediction loss and incorporates a dynamic masking pattern. The enriched contextual feature vector of WEB is passed as an input to the CLB block, which has a feed forward neural network to classify the news article into fake or legitimate. The proposed model has been evaluated using two standard datasets namely Politifact and Gossipcop, achieved an accuracy of 92.77% and 91.78%, respectively. Experimental results exhibit that the CTrL-FND model outperforms the other state-of-the-art (SoTA) techniques, especially achieved an average accuracy of 10.49% and 14.53% improvements compared to the SoTA methods on Politifact and Gossipcop, respectively.
Similar content being viewed by others
References
Ahmed H, Traore I, Saad S (2017) 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, pp 127–138. Springer
Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 election. J Econ Perspect 31(2):211–36
Bondielli A, Marcelloni F (2019) A survey on fake news and rumour detection techniques. Inf Sci 497:38–55
Castillo C, Mendoza M, Poblete B (2011) Information credibility on twitter. In: Proceedings of the 20th international conference on world wide web, pp 675–684
Chaturvedi B, Dubey S An efficient approach based on machine learning and performance improvement for fake news detection
Choudhary M, Chouhan SS, Pilli ES, Vipparthi SK (2021) Berconvonet: a deep learning framework for fake news classification. Appl Soft Comput 110:107614
Devlin J, Chang M.-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805
Faustini PHA, Covoes TF (2020) Fake news detection in multiple platforms and languages. Expert Syst Appl 158:113503
GLUE (2019) The general language understanding evaluation (glue) benchmark. https://gluebenchmark.com/leaderboard
Guo B, Ding Y, Yao L, Liang Y, Yu Z (2020) The future of false information detection on social media: new perspectives and trends. ACM Comput Surveys (CSUR) 53(4):1–36
Horne B, Adali S (2017) This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In: Proceedings of the International AAAI conference on web and social media, vol 11, pp 759–766
Institute AP (2014) Social and demographic differences in news habits and attitudes (2014)
Kaliyar RK, Goswami A, Narang P (2021) Fakebert: fake news detection in social media with a bert-based deep learning approach. Multimedia Tools Appl 80(8):11765–11788
Kaur S, Kumar P, Kumaraguru P (2020) Automating fake news detection system using multi-level voting model. Soft Comput 24(12):9049–9069
Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing (emnlp), association for computational linguistics, Doha, Qatar (2014) pp 1746–1751. https://doi.org/10.3115/v1/D14-1181. https://aclanthology.org/D14-1181
Kouzy R, Abi Jaoude J, Kraitem A, El Alam MB, Karam B, Adib E, Zarka J, Traboulsi C, Akl EW, Baddour K (2020) Coronavirus goes viral: quantifying the covid-19 misinformation epidemic on twitter. Cureus 12(3):e7255
Kumar S, Asthana R, Upadhyay S, Upreti N, Akbar M (2020) Fake news detection using deep learning models: a novel approach. Trans Emerg Telecommun Technol 31(2):3767
Kwon S, Cha M, Jung K, Chen W, Wang Y (2013) Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th international conference on data mining, pp 1103–1108. IEEE
Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692
Ma J, Gao W, Mitra P, Kwon S, Jansen B.J, Wong K-F, Cha M (2016) Detecting rumors from microblogs with recurrent neural networks
Ma J, Gao W, Wong K-F (2018) Rumor detection on twitter with tree-structured recursive neural networks. Association for Computational Linguistics
Meel P, Vishwakarma DK (2021) A temporal ensembling based semi-supervised convnet for the detection of fake news articles. Expert Syst Appl 177:115002
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26
Ozbay FA, Alatas B (2020) Fake news detection within online social media using supervised artificial intelligence algorithms. Phys A 540:123174
Palani B, Elango S, Viswanathan KV (2022) Cb-fake: a multimodal deep learning framework for automatic fake news detection using capsule neural network and bert. Multimedia Tools Appl 81(4):5587–5620
Pennebaker JW, Boyd RL, Jordan K, Blackburn K (2015) The development and psychometric properties of LIWC2015
Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543
Pérez-Rosas V, Kleinberg B, Lefevre A, Mihalcea R (2017) Automatic detection of fake news. arXiv preprint arXiv:1708.07104
Qian F, Gong C, Sharma K, Liu Y (2018) Neural user response generator: fake news detection with collective user intelligence. In: IJCAI, vol 18, pp 3834–3840
Rai N, Kumar D, Kaushik N, Raj C, Ali A (2022) Fake news classification using transformer based enhanced lstm and bert. Int J Cogn Comput Eng 3:98–105
Rapoza K (2017) Can ‘fake news’ impact the stock market? Forbes News
Reis JC, Correia A, Murai F, Veloso A, Benevenuto F (2019) Supervised learning for fake news detection. IEEE Intell Syst 34(2):76–81
Ruchansky N, Seo S, Liu Y (2017) 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
Sahoo SR, Gupta BB (2021) Multiple features based approach for automatic fake news detection on social networks using deep learning. Appl Soft Comput 100:106983
Samadi M, Mousavian M, Momtazi S (2021) Deep contextualized text representation and learning for fake news detection. Inf Process Manag 58(6):102723
Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: a data mining perspective. ACM SIGKDD Explorations Newsl 19(1):22–36
Shu K, Mahudeswaran D, Wang S, Lee D, Liu H (2018) Fakenewsnet: A data repository with news content, social context and spatialtemporal information for studying fake news on social media. arXiv preprint arXiv:1809.01286
Singhal S, Shah RR, Chakraborty T, Kumaraguru P, Satoh S (2019) Spotfake: a multi-modal framework for fake news detection. In: 2019 IEEE Fifth international conference on multimedia big data (BigMM), pp 39–47 . IEEE
Song C, Ning N, Zhang Y, Wu B (2021) A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks. Inf Process Manag 58(1):102437
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A.N, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30
Volkova S, Shaffer K, Jang JY, Hodas N (2017) Separating facts from fiction: linguistic models to classify suspicious and trusted news posts on twitter. In: Proceedings of the 55th annual meeting of the association for computational linguistics (volume 2: Short Papers), pp 647–653
Wu Z, Pi D, Chen J, Xie M, Cao J (2020) Rumor detection based on propagation graph neural network with attention mechanism. Expert Syst Appl 158:113595
Wu L, Liu H (2018) 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, pp 637–645
Xue J, Wang Y, Tian Y, Li Y, Shi L, Wei L (2021) Detecting fake news by exploring the consistency of multimodal data. Inf Process Manag 58(5):102610
Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the north american chapter of the association for computational linguistics: human language technologies, pp 1480–1489
Yu F, Liu Q, Wu S, Wang L, Tan T (2017) A convolutional approach for misinformation identification. In: IJCAI, pp 3901–3907
Zeng J, Zhang Y, Ma X (2021) Fake news detection for epidemic emergencies via deep correlations between text and images. Sustain Cities Soc 66:102652
Zhou X, Wu J, Zafarani R (2020) Safe: similarity-aware multi-modal fake news detection. In: Pacific-Asia Conference on knowledge discovery and data mining, pp 354–367. Springer
Funding
No funding was provided for the completion of this study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no competing interests to declare that are relevant to the content of this article.
Human participants and or animals
This research did not involve any human participants and/or animals for experimentation.
Informed consent
This research has reused the dataset from the prior publications. The datasets are available for public access.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Palani, B., Elango, S. CTrL-FND: content-based transfer learning approach for fake news detection on social media. Int J Syst Assur Eng Manag 14, 903–918 (2023). https://doi.org/10.1007/s13198-023-01891-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-023-01891-7