Skip to main content
Register
Springer Professional
SEARCH
Menu 1 2 3 4
Top

Hint

Swipe to navigate through the chapters of this book

Published in:
Different Strategies of Fitting Logistic Regression for Positive and Unlabelled Data Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Sentiment Analysis for Fake News Detection by Means of Neural Networks

Authors : Sebastian Kula, Michał Choraś, Rafał Kozik, Paweł Ksieniewicz, Michał Woźniak

Published in: Computational Science – ICCS 2020

Publisher: Springer International Publishing

Login to get access
share
SHARE

Abstract

The problem of fake news has become one of the most challenging issues having an impact on societies. Nowadays, false information may spread quickly through social media. In that regard, fake news needs to be detected as fast as possible to avoid negative influence on people who may rely on such information while making important decisions (e.g., presidential elections). In this paper, we present an innovative solution for fake news detection that utilizes deep learning methods. Our experiments prove that the proposed approach allows us to achieve promising results.

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe



 


Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko





Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now
previous chapter Detecting Rumours in Disasters: An Imbalanced Learning Approach
Literature
1.
Goodman, M.: The Sun and the Moon: The Remarkable True Account of Hoaxers, Showmen, Dueling Journalists, and Lunar Man-Bats in Nineteenth-Century New York. Basic Books, New York (2008)
2.
Jain, A., Kasbe, A.: Fake News Detection. In: 2018 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, pp. 1–5 (2018)
3.
Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. CoRR, abs/1708.01967 (2017)
4.
5.
Zhang, J., Cui, L., Fu, Y., Gouza, F.B.: Fake news detection with deep diffusive network model. CoRR, abs/1805.08751 (2018)
6.
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:​1810.​04805 (2018)
7.
Akbik, A., Bergmann, T., Blythe, D., Rasul, K., Schweter, S., Vollgraf, R.: FLAIR: an easy-to-use framework for state-of-the-art NLP. In: Ammar, W., Louis, A., Mostafazadeh, N. (eds.) NAACL-HLT (Demonstrations), pp. 54–59. Association for Computational Linguistics (2019)
8.
Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
9.
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of Tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pp. 427–431 (2017)
10.
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017) CrossRef
11.
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A.J., Hovy, E.H.: 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 (2016)
12.
Chung, J., Gülcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning and Representation Learning, Montréal, Canada (2014)
13.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997) CrossRef
14.
Ahmed, H., Traore, I., Saad, S.: Detecting opinion spams and fake news using text classification. J. Secur. Priv. 1(1), e9 (2018) CrossRef
15.
16.
17.
18.
19.
20.
Choraś, M., Pawlicki, M., Kozik, R., Demestichas, K.P., Kosmides, P., Gupta, M.: SocialTruth project approach to online disinformation (fake news) detection and mitigation. In: Proceedings of ARES, Canterbury, UK, pp. 68:1–68:10 (2019)
Metadata
Title
Sentiment Analysis for Fake News Detection by Means of Neural Networks
Authors
Sebastian Kula
Michał Choraś
Rafał Kozik
Paweł Ksieniewicz
Michał Woźniak
Copyright Year
2020
Book
Computational Science – ICCS 2020

Print ISBN: 978-3-030-50422-9

Electronic ISBN: 978-3-030-50423-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-50423-6_49