1. Barbado R, Araque O, Iglesias CA (2019) A framework for fake review detection in online consumer electronics retailers. Inform Process Manag 56(4):1234–1244

   Article  Google Scholar 

2. Barrón-Cedeno A, Jaradat I, Da San Martino G, Nakov P (2019) Proppy: organizing the news based on their propagandistic content. Inform Process Manag 56(5):1849–1864

   Article  Google Scholar 

3. Clark K, Luong MT, Le QV, Manning CD (2020) Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprintarXiv:2003.10555

4. Devlin J, Chang MW, Lee K, Toutanova K (2019) Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp 4171–4186

5. Faustini PHA, Covoes TF (2020) Fake news detection in multiple platforms and languages. Expert Syst Appl 158:113503

   Article  Google Scholar 

6. Guo H, Cao J, Zhang Y, Guo J, Li J (2018) Rumor detection with hierarchical social attention network. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 943–951

7. Huang L, Ma D, Li S, Zhang X, Houfeng W (2019) Text level graph neural network for text classification. In: Proceedings of the 2019 conference on Empirical Methods in Natural Language Processing and the 9th international joint conference on Natural Language Processing (EMNLP-IJCNLP), pp 3435–3441

8. Ji Y, Eisenstein J (2014) Representation learning for text-level discourse parsing. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 13–24

9. Jiao X, Yin Y, Shang L, Jiang X, Chen X, Li L, Wang F, Liu Q (2019) Tinybert: Distilling bert for natural language understanding. arXiv preprintarXiv:1909.10351

10. Joshi M, Chen D, Liu Y, Weld DS, Zettlemoyer L, Levy O (2019) Spanbert: improving pre-training by representing and predicting spans. arXiv preprintarXiv:1907.10529

11. Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in Natural Language Processing (EMNLP), pp 1746–1751

12. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net.https://openreview.net/forum?id=SJU4ayYgl

13. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

14. Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R (2019) Albert: A lite bert for self-supervised learning of language representations. arXiv preprintarXiv:1909.11942

15. Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, pp 1188–1196

16. Li X, Saude J (2020) Explain graph neural networks to understand weighted graph features in node classification. arXiv preprintarXiv:2002.00514

17. 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 preprintarXiv:1907.11692

18. Liu Y, Wu YFB (2020) Fned: a deep network for fake news early detection on social media. ACM Trans Inform Syst (TOIS) 38(3):1–33

    Article MathSciNet  Google Scholar 

19. Mikolov T, Grave É, Bojanowski P, Puhrsch C, Joulin A (2018) Advances in pre-training distributed word representations. In: Proceedings of the eleventh international conference on Language Resources and Evaluation (LREC 2018)

20. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems 26: 27th annual conference on neural information processing systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States, pp 3111–3119

21. Monti F, Frasca F, Eynard D, Mannion D, Bronstein MM (2019) Fake news detection on social media using geometric deep learning. arXiv preprintarXiv:1902.06673

22. Pennebaker JW, Boyd RL, Jordan K, Blackburn K (2015) The development and psychometric properties of liwc2015. Tech. rep

23. Pennington J, Socher R, Manning C (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

24. Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. In: Proceedings of NAACL

25. Qian F, Gong C, Sharma K, Liu Y (2018) Neural user response generator: Fake news detection with collective user intelligence. In: IJCAI, pp 3834–3840

26. Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I (2019) Language models are unsupervised multitask learners. OpenAI Blog 1(8):9

    Google Scholar 

27. Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2019) Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv e-prints

28. Reis JC, Correia A, Murai F, Veloso A, Benevenuto F (2019) Supervised learning for fake news detection. IEEE Intell Syst 34(2):76–81

    Article  Google Scholar 

29. Rubin VL, 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

30. 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

31. Shu K, Cui L, Wang S, Lee D, Liu H (2019) defend: Explainable fake news detection. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 395–405

32. Shu K, Mahudeswaran D, Liu H (2019) Fakenewstracker: a tool for fake news collection, detection, and visualization. Comput Math Organ Theory 25(1):60–71

    Article  Google Scholar 

33. Shu K, Mahudeswaran D, Wang S, Liu H (2020) Hierarchical propagation networks for fake news detection: Investigation and exploitation. In: Proceedings of the international AAAI conference on Web and Social Media, vol 14, pp 626–637

34. 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

    Article  Google Scholar 

35. Shu K, Wang S, Liu H (2019) Beyond news contents: The role of social context for fake news detection. In: Proceedings of the twelfth ACM international conference on web search and data mining, pp 312–320

36. Silva A, Han Y, Luo L, Karunasekera S, Leckie C (2020) Embedding partial propagation network for fake news early detection. In:  Conrad S, Tiddi I (eds) Proceedings of the CIKM 2020 workshops co-located with 29th ACM international conference on information and knowledge management (CIKM 2020), Galway, Ireland, October 19–23, 2020, CEUR Workshop Proceedings, vol 2699. CEUR-WS.org.http://ceur-ws.org/Vol-2699/paper35.pdf

37. Singh DV, Dasgupta R, Ghosh I (2017) Automated fake news detection using linguistic analysis and machine learning. In: International conference on social computing, behavioral-cultural modeling, & prediction and behavior representation in modeling and simulation (SBP-BRiMS), pp 1–3

38. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008

39. Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph attention networks. In: International conference on learning representations.https://openreview.net/forum?id=rJXMpikCZ

40. Vicario MD, Quattrociocchi W, Scala A, Zollo F (2019) Polarization and fake news: early warning of potential misinformation targets. ACM Trans Web (TWEB) 13(2):1–22

    Article  Google Scholar 

41. Vosoughi S, Roy D, Arak S (2018) The spread of true and false news online. sciencemag

42. Wu F, Souza A, Zhang T, Fifty C, Yu T, Weinberger K (2019) Simplifying graph convolutional networks. In: International conference on machine learning, pp 6861–6871

43. Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov RR, Le QV (2019) Xlnet: Generalized autoregressive pretraining for language understanding. In: Advances in neural information processing systems, pp. 5754–5764

44. 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

45. Yao L, Mao C, Luo Y (2019) Graph convolutional networks for text classification. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 7370–7377

46. Zhang J, Dong B, Philip SY (2020) Fakedetector: effective fake news detection with deep diffusive neural network. In: 2020 IEEE 36th international conference on data engineering (ICDE), pp 1826–1829. IEEE

47. Zhang X, Ghorbani AA (2020) An overview of online fake news: characterization, detection, and discussion. Inform Process Manag 57(2):102025

    Article  Google Scholar 

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