Skip to main content
SpringerLink
Log in

Multi-layer perceptron based fake news classification using knowledge base triples

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Recent attempts to detect fake news have relied on the implementation of machine or deep learning models that have been trained on text. These models, on the other hand, are insufficient for classifying knowledge base facts or triples as fake or true. However, it is critical to assess the credibility of facts before they are included to the knowledge base. Hence, this paper suggests using a Multi-layer Perceptron to categorize a given triple as fake or true. Furthermore, extant works embed the features using either frequency or prediction based word embedding models, and thus both document and word level features are not captured. To address this issue, a data modeling approach is proposed that vectorizes the triples using two cutting-edge word embedding models, Wrod2Vec and GloVe, as well as TF-IDF and Counter Vectorizer. Empirical results show that the Multi-layer Perceptron with GloVe and count vectorizer outperforms the baseline model in terms of accuracy. Moreover, named entity tags associated with the entities, such as PERSON, add an extra feature for training the models. As a result, an algorithm that jointly extracts the triples along with named entity tags is also proposed. Experiments demonstrated that models trained on triples with named entity tags produce high accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Agarwal A, Mittal M, Pathak A, Goyal LM (2020) Fake News Detection Using a Blend of Neural Networks: An Application of Deep Learning. SN Computer Science 1(3):1–9

    Article  Google Scholar 

  2. Thota A., Tilak P., Ahluwalia S., Lohia N. (2018) Fake news detection: A deep learning approach SMU Data Science Review 1(3)

  3. Popov B., Kiryakov A., Ognyanoff D., Manov D., Kirilov A., Goranov M. (2003) Towards semantic web information extraction

  4. Mesquita F., Schmidek J., Barbosa D. (2013) Effectiveness and efficiency of open relation extraction. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp 447–457

  5. Reddy H., Raj N., Gala M., Basava A. (2020) Text-mining-based Fake News Detection Using Ensemble Methods. International Journal of Automation and Computing 17(2):210–221

    Article  Google Scholar 

  6. Ahmad I., Yousaf M., Yousaf S., Ahmad M. O. (2020) Fake News Detection Using Machine Learning Ensemble Methods, Complexity 2020, pp 1–11

  7. Albahr A., Albahar M. (2020) An Empirical Comparison of Fake News Detection using different Machine Learning Algorithms. (IJACSA) International Journal of Advanced Computer Science and Applications 11(9):146–152

    Google Scholar 

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

  9. Zhang J., Dong B., Philip S. Y. (2020) Fake detector: Effective fake news detection with deep diffusive neural network, 2020 IEEE 36th International Conference on Data Engineering, pp 1826–1829

  10. Shu K., Cui L., Wang S., Lee D. (2019) H Liu.: defend: Explainable fake news detection, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 395–405

  11. Shu K., Sliva A., Wang S., Tang J., Liu H. (2017) Fake News Detection on Social Media:A data mining perspective. ACM SIGKDD Explorations Newsletter 19(1):22–36

    Article  Google Scholar 

  12. CHURCH K.W. (2017) Word2Vec. Natural Language Engineering 23(1):155–162

    Article  Google Scholar 

  13. Khan J.Y., Khondaker M.T.I., Afroz S., Uddin G., Iqbal A. (2021) A benchmark study of machine learning models for online fake news detection. Machine Learning with Applications 4:100032

    Article  Google Scholar 

  14. Rospocher M., Erp M.V., Vossen P., Fokkens A., Aldabe I., Rigau G., Soroa A., Ploeger T., Bogaard T. (2016) Building event-centric knowledge graphs from news. Journal of Web Semantics 37:132–151

    Article  Google Scholar 

  15. Arulanandam R., Savarimuthu B. T. R., Purvis M. A. (2014) Extracting crime information from online newspaper articles. Proceedings of the second australasian web conference 155:31–38

    Google Scholar 

  16. Stern R., Sagot B. (2012) Population of a knowledge base for news metadata from unstructured text and web data. AKBC-WEKEX 2012 - The Knowledge Extraction Workshop at NAACL-HLT, 2012

  17. Wu R., Yao Y., Han X., Xie R., Liu Z., Lin F., Lin L., Sun M. (2019) Open relation extraction: Relational knowledge transfer from supervised data to unsupervised data, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp 219–228

  18. Auer S., Bizer C., Kobilarov G., Lehmann J., Cyganiak R., Ives Z. (2007) Dbpedia: A nucleus for a web of open data, The semantic web, Springer, pp 722–735

  19. Bird S. (2006) NLTK: The natural language toolkit, Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions, pp 69–72

  20. Ghosh S., Shah C. (2018) Towards automatic fake news classification. In: Proceedings of the Association for Information Science and Technology 55(1), pp 805–807

  21. Gupta S., Meel P. (2021) Fake News Detection Using Passive-Aggressive Classifier, Inventive Communication and Computational Technologies, Springer, pp 155–164

  22. Jia S., Xiang Y., Chen X., Wang K. (2019) Triple trustworthiness measurement for knowledge graph. In: The World Wide Web Conference, pp 2865–2871

  23. Manzoor S., Ishfaq J. (2019) Singla Fake news detection using machine learning approaches: A systematic review, 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp 230–234

  24. Dasgupta T., Naskar A., Saha R., Dey L. (2017) Crime profiler: Crime information extraction and visualization from news media, Proceedings of the International Conference on Web Intelligence, pp 541–549

  25. Priyanga V. T., Sanjanasri J. P., Menon V. K., Gopalakrishnan E. A., Soman K. P. (2021) Exploring fake news identification using word and sentence embeddings, Journal of Intelligent & Fuzzy Systems, Pre-press, pp 1–8

  26. Zhou X., Zafarani R., Shu K., Liu H. (2019) Fake news: Fundamental theories, detection strategies and challenges, Proceedings of the twelfth ACM international conference on web search and data mining, pp 836–837

  27. Wu Z., Liang C., Giles C. L. (2015) Storybase: Towards building a knowledge base for news events, Proceedings of ACL-IJCNLP 2015 System Demonstrations, pp 133–138

  28. Raj C., Meel P. (2021) Convnet frameworks for multi-modal fake news detection Applied Intelligence. https://doi.org/10.1007/s10489-021-02345-y

  29. Li H., Wu X., Li Z., et al. (2013) A relation extraction method of Chinese named entities based on location and semantic features, vol 38. https://doi.org/10.1007/s10489-012-0353-0

  30. Dimililer N., Varoğ E., Altinçay H. (2009) Classifier subset selection for biomedical named entity recognition. Appl Intell 31:267. https://doi.org/10.1007/s10489-008-0124-0

    Article  Google Scholar 

  31. Akkasi A., Varoğ E., Dimililer N. (2018) Balanced undersampling: a novel sentence-based undersampling method to improve recognition of named entities in chemical and biomedical text. Applied Intelligence 48:1965–1978. https://doi.org/10.1007/s10489-017-0920-5

    Article  Google Scholar 

  32. Pérez Jorge, Arenas Marcelo, Gutierrez Claudio (2009) Semantics and complexity of SPARQL. ACM Transactions on Database Systems (TODS) 34(3):1–45

    Article  Google Scholar 

  33. Srinivasa K., Santhi Thilagam P. (2019) Crime base: Towards building a knowledge base for crime entities and their relationships from online news papers, Information Processing & Management, 56, 6, pp 102059

  34. Khan J. Y., Khondaker M. T. I., Afroz S., Uddin G., Iqbal A. (2021) A benchmark study of machine learning models for online fake news detection. Machine Learning with Applications 4:100032

    Article  Google Scholar 

  35. Kaliyar Rohit Kumar, Goswami Anurag, Narang Pratik (2021) FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. Multimed Tools Appl 80(8):11765–11788

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Karnataka, Surathkal, 575025, India

    Srinivasa K & P Santhi Thilagam

Authors
  1. Srinivasa K
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P Santhi Thilagam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Srinivasa K.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

K, S., Thilagam, P.S. Multi-layer perceptron based fake news classification using knowledge base triples. Appl Intell 53, 6276–6287 (2023). https://doi.org/10.1007/s10489-022-03627-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03627-9

Keywords

Search

Navigation