Built on

Khan, J.Y., Khondaker, M.T., Iqbal, A., Afroz, S.: A Benchmark Study on Machine Learning Methods for Fake News Detection. (2019). ArXiv, abs/1905.04749
Original
1905
Earlier work this paper cites.
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. (2020). ArXiv, abs/1909.11942
Original
1909
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Hochreiter, S., Schmidhuber, J.: Long Short-Term Memory. (1997). Neural Computation, 9, 1735-1780
1997
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Shahi, G.K., Nandini, D.: FakeCovid - A Multilingual Cross-domain Fact Check News Dataset for COVID-19. (2020). ArXiv, abs/2006.11343
Original
2006
Earlier work this paper cites.
Cui, L., Lee, D.: CoAID: COVID-19 Healthcare Misinformation Dataset. (2020). ArXiv, abs/2006.00885
Original
2006
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Memon, S.A., Carley, K.M.: Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset. (2020). ArXiv, abs/2008.00791
Original
2008
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Li, Y., Jiang, B., Shu, K., Liu, H.: MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation. (2020). ArXiv, abs/2011.04088
Original
2011
Earlier work this paper cites.
Patwa, P., Sharma, S., Pykl, S., Guptha, V., Kumari, G., Akhtar, M.S., Ekbal, A., Das, A., Chakraborty, T. (2020). Fighting an Infodemic: COVID-19 Fake News Dataset. ArXiv, abs/2011.03327
Original
2011
Earlier work this paper cites.
Chen, X., Sin, S.J.: ’Misinformation? What of it?’ Motivations and individual differences in misinformation sharing on social media. In: ASIST (2013)
2013
Earlier work this paper cites.
Pennington, J., Socher, R., Manning, C.D.: Glove: Global Vectors for Word Representation. (2014). In: EMNLP
2014
Earlier work this paper cites.

Similar

LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. (2015). Nature, 521(7553), pp.436-444
2015
Cited alongside, same era.
Sean, B., Doug, S., Yuxi, P: Talos Targets Disinformation with Fake News Challenge Victory. (2017). Available online: https://blog.talosintelligence.com/2017/06/talos-fake-news-challenge.html
2017
Cited alongside, same era.
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching Word Vectors with Subword Information. (2017). Transactions of the Association for Computational Linguistics, 5, 135-146
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is All you Need. (2017). NIPS
2017
Cited alongside, same era.
Thorne, J., Vlachos, A.: Automated Fact Checking: Task formulations, methods and future directions. In: COLING (2018)
2018

Then

Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: XLNet: Generalized Autoregressive Pretraining for Language Understanding. (2019). NeurIPS
2019
Later among the works it cites.
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q.V., Salakhutdinov, R.: Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context. (2019). ACL
2019
Later among the works it cites.
Datta, R., Yadav, K., Singh, A., Datta, K., Bansal, A.: The infodemics of COVID-19 amongst healthcare professionals in india. Med. J. Armed Forces India, vol. 76, no. 3, pp. 276–283, Jul. 2020
2020
Later among the works it cites.
Zhang, X., Ghorbani, A.: An overview of online fake news: Characterization, detection, and discussion. (2020). Inf. Process. Manag., 57, 102025
2020
Later among the works it cites.

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2021

Transformer based Automatic COVID-19 Fake News Detection System

Gundapu, Sunil, Mamidi, Radhika

Understand

Recent rapid technological advancements in online social networks such as Twitter have led to a great incline in spreading false information and fake news.

Misinformation is especially prevalent in the ongoing coronavirus disease (COVID-19) pandemic, leading to individuals accepting bogus and potentially deleterious claims and articles.
Quick detection of fake news can reduce the spread of panic and confusion among the public.
For our analysis in this paper, we report a methodology to analyze the reliability of information shared on social media pertaining to the COVID-19 pandemic.

Built on

Khan, J.Y., Khondaker, M.T., Iqbal, A., Afroz, S.: A Benchmark Study on Machine Learning Methods for Fake News Detection. (2019). ArXiv, abs/1905.04749
Original
1905
Earlier work this paper cites.
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. (2020). ArXiv, abs/1909.11942
Original
1909
Earlier work this paper cites.
Hochreiter, S., Schmidhuber, J.: Long Short-Term Memory. (1997). Neural Computation, 9, 1735-1780
1997
Earlier work this paper cites.
Shahi, G.K., Nandini, D.: FakeCovid - A Multilingual Cross-domain Fact Check News Dataset for COVID-19. (2020). ArXiv, abs/2006.11343
Original
2006
Earlier work this paper cites.
Cui, L., Lee, D.: CoAID: COVID-19 Healthcare Misinformation Dataset. (2020). ArXiv, abs/2006.00885
Original
2006
Earlier work this paper cites.
Memon, S.A., Carley, K.M.: Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset. (2020). ArXiv, abs/2008.00791
Original
2008
Earlier work this paper cites.
Li, Y., Jiang, B., Shu, K., Liu, H.: MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation. (2020). ArXiv, abs/2011.04088
Original
2011
Earlier work this paper cites.
Patwa, P., Sharma, S., Pykl, S., Guptha, V., Kumari, G., Akhtar, M.S., Ekbal, A., Das, A., Chakraborty, T. (2020). Fighting an Infodemic: COVID-19 Fake News Dataset. ArXiv, abs/2011.03327
Original
2011
Earlier work this paper cites.
Chen, X., Sin, S.J.: ’Misinformation? What of it?’ Motivations and individual differences in misinformation sharing on social media. In: ASIST (2013)
2013
Earlier work this paper cites.
Pennington, J., Socher, R., Manning, C.D.: Glove: Global Vectors for Word Representation. (2014). In: EMNLP
2014
Earlier work this paper cites.

Similar

LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. (2015). Nature, 521(7553), pp.436-444
2015
Cited alongside, same era.
Sean, B., Doug, S., Yuxi, P: Talos Targets Disinformation with Fake News Challenge Victory. (2017). Available online: https://blog.talosintelligence.com/2017/06/talos-fake-news-challenge.html
2017
Cited alongside, same era.
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching Word Vectors with Subword Information. (2017). Transactions of the Association for Computational Linguistics, 5, 135-146
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is All you Need. (2017). NIPS
2017
Cited alongside, same era.
Thorne, J., Vlachos, A.: Automated Fact Checking: Task formulations, methods and future directions. In: COLING (2018)
2018

Then

Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: XLNet: Generalized Autoregressive Pretraining for Language Understanding. (2019). NeurIPS
2019
Later among the works it cites.
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q.V., Salakhutdinov, R.: Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context. (2019). ACL
2019
Later among the works it cites.
Datta, R., Yadav, K., Singh, A., Datta, K., Bansal, A.: The infodemics of COVID-19 amongst healthcare professionals in india. Med. J. Armed Forces India, vol. 76, no. 3, pp. 276–283, Jul. 2020
2020
Later among the works it cites.
Zhang, X., Ghorbani, A.: An overview of online fake news: Characterization, detection, and discussion. (2020). Inf. Process. Manag., 57, 102025
2020
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

Thota, A., Tilak, P., Ahluwalia, S., Lohia, N.: Fake News Detection: A Deep Learning Approach. (2018). SMU Data Science Review: Vol. 1 : No. 3 , Article 10

2018

Cited alongside, same era.

Kaliyar, R., Singh, N.: Misinformation Detection on Online Social Media-A Survey. (2019). 1-6. 10.1109/ICCCNT45670.2019.8944587

2019

Cited alongside, same era.

Elhadad, M., Li, K.F., Gebali, F.: A Novel Approach for Selecting Hybrid Features from Online News Textual Metadata for Fake News Detection. In: Proc. 3PGCIC, Antwerp, Belgium, 2019, pp. 914–925

2019

Cited alongside, same era.

Jwa, H., Oh, D., Park, K., Kang, J., Lim, H.: exBAKE: Automatic Fake News Detection Model Based on Bidirectional Encoder Representations from Transformers (BERT). (2019). Applied Sciences, 9, 4062

2019

Cited alongside, same era.

Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. (2019). NAACL-HLT

2019

Cited alongside, same era.

Titcomb, J., Carson, J.: www.telegraph.co.uk. Fake news: What exactly is it – and how can you spot it?

Cited in the paper.

Zhou, X., Mulay, A., Ferrara, E., Zafarani, R.: ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research. (2020). In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

2020