Skip to main content
SpringerLink
Log in

“News Title Can Be Deceptive” Title Body Consistency Detection for News Articles Using Text Entailment

  • Conference paper
  • First Online:
Computational Linguistics and Intelligent Text Processing (CICLing 2019)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13452))

  • 369 Accesses

Abstract

News Title (NT) and News Body (NB) consistency detection is a demanding problem in Fake News Detection. In this paper, we formulate consistency detection between NT and NB from the perspective of Textual Entailment (TE), and propose various deep learning based methods for solving this problem. Inconsistency between NT and NB can affect the purpose of the news and alter the view of the reader towards the news contents. We develop various models based on Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a combination of CNN and LSTM. Evaluation of the proposed approaches on a recently released benchmark dataset demonstrate the effectiveness of our approaches.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We use these terms interchangeably throughout the paper.

  2. 2.

    http://www.fakenewschallenge.org/.

  3. 3.

    https://www.kaggle.com/mrisdal/fake-news/data.

  4. 4.

    Dense layer indicates feed-forward neural network, we use these terms interchangeably through out the paper.

  5. 5.

    For space constraint, we are not able to show all the seven layers in the diagram, the dotted lines indicate other layers.

  6. 6.

    https://en.wikipedia.org/wiki/Cosine_similarity.

  7. 7.

    Due to space limitations, all the CNNs applied are not shown, the dotted lines indicate the same.

  8. 8.

    The dotted lines indicate the other CNNs in the Figure, for space limitations we avoid this.

  9. 9.

    https://nlp.stanford.edu/projects/snli/.

  10. 10.

    http://nlp.stanford.edu/projects/glove/.

  11. 11.

    We find 2691 number of unique words whose WordEmbeddings are not present.

  12. 12.

    For space limitations, we are avoiding showing those examples.

References

  1. Hanselowski, A., Avinesh, P.V.S., Schiller, B., Caspelherr, F.: Description of the System Developed by Team Athene in the FNC-1 (2017))

    Google Scholar 

  2. Baird Sean, S.D., Yuxi, P.: Talos Targets Disinformation with Fake News Challenge Victory (2017)

    Google Scholar 

  3. Bajaj, S.: “The Pope Has a New Baby!” Fake News Detection using Deep Learning (2017)

    Google Scholar 

  4. Baly, R., Mohtarami, M., Glass, J., Màrquez, L., Moschitti, A., Nakov, P.: Integrating stance detection and fact checking in a unified corpus. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp. 21–27. Association for Computational Linguistics, New Orleans, Louisiana (2018)

    Google Scholar 

  5. Biyani, P., Tsioutsiouliklis, K., Blackmer, J.: “8 Amazing Secrets for Getting More Clicks”: detecting clickbaits in news streams using article informality. In: AAAI, pp. 94–100. Phoenix, Arizona, USA (2016)

    Google Scholar 

  6. Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A Large Annotated Corpus for Learning Natural Language Inference. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 632–642. Association for Computational Linguistics, Lisbon, Portugal (2015)

    Google Scholar 

  7. Ciampaglia, G.L., Shiralkar, P., Rocha, L.M., Bollen, J., Menczer, F., Flammini, A.: Computational fact checking from knowledge networks. PLoS ONE 10(6), e0128-193 (2015)

    Article  Google Scholar 

  8. Conroy, N.J., Rubin, V.L., Chen, Y.: Automatic deception detection: methods for finding fake news. Proc. Assocr Inf. Sci. Technol. 52(1), 1–4 (2015)

    Article  Google Scholar 

  9. Dagan, I., Glickman, O., Magnini, B.: The PASCAL recognising textual entailment challenge. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 177–190. Springer, Heidelberg (2006). https://doi.org/10.1007/11736790_9

    Chapter  Google Scholar 

  10. Doddington, G.: Automatic evaluation of machine translation quality using N-gram co-occurrence statistics. In: Proceedings of the Second International Conference on Human Language Technology Research, pp. 138–145. HLT 2002, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2002)

    Google Scholar 

  11. Ferreira, W., Vlachos, A.: Emergent: a novel data-set for stance classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1163–1168. Association for Computational Linguistics, San Diego, California (2016)

    Google Scholar 

  12. Green, Jr., B.F., Wolf, A.K., Chomsky, C., Laughery, K.: Baseball: an automatic question-answerer. In: Papers Presented at the 9–11 May 1961, Western Joint IRE-AIEE-ACM Computer Conference, pp. 219–224. IRE-AIEE-ACM 1961 (Western), ACM, New York, NY, USA (1961)

    Google Scholar 

  13. Grishman, R.: Information extraction: techniques and challenges. In: Pazienza, M.T. (ed.) SCIE 1997. LNCS, vol. 1299, pp. 10–27. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-63438-X_2

    Chapter  Google Scholar 

  14. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Nat. Lang. Inference 9(8), 1735–1780. Neural computation (1997)

    Google Scholar 

  15. Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for matching natural language sentences. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, pp. 2042–2050. Curran Associates Inc, Palais des Congrès de Montréal, Montréal, Canada (2014)

    Google Scholar 

  16. Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. Association for Computational Linguistics, Doha, Qatar (2014)

    Google Scholar 

  17. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  18. Knight, K., Marcu, D.: Summarization beyond sentence extraction: a probabilistic approach to sentence compression. Artif. Intell. 139(1), 91–107 (2002)

    Google Scholar 

  19. Kumar, S., West, R., Leskovec, J.: Disinformation on the web: impact, characteristics, and detection of wikipedia hoaxes. In: Proceedings of the 25th international conference on World Wide Web, pp. 591–602. International World Wide Web Conferences Steering Committee (2016)

    Google Scholar 

  20. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Xing, E.P., Jebara, T. (eds.) Proceedings of the 31st International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 32, pp. 1188–1196. PMLR, Bejing, China (22–24 June 2014)

    Google Scholar 

  21. Li, X., Croft, W.B.: Novelty detection based on sentence level patterns. In: Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management, Bremen, Germany, October 31–November 5, 2005, pp. 744–751 (2005)

    Google Scholar 

  22. Lukasik, M., Cohn, T., Bontcheva, K.: Classifying tweet level judgements of Rumours in social media. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. pp. 2590–2595. Association for Computational Linguistics, Lisbon, Portugal (2015)

    Google Scholar 

  23. MacCartney, B., Manning, C.D.: Natural logic for textual inference. In: Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, pp. 193–200. RTE 2007, Prague, Czech Republic (2007)

    Google Scholar 

  24. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)

    Book  Google Scholar 

  25. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed Representations of Words and Phrases and their Compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, pp. 3111–3119. Curran Associates Inc, Lake Tahoe, Nevada, USA (2013)

    Google Scholar 

  26. Mohtarami, M., Baly, R., Glass, J., Nakov, P., Màrquez, L., Moschitti, A.: Automatic stance detection using end-to-end memory networks. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 767–776. Association for Computational Linguistics, New Orleans, Louisiana (2018)

    Google Scholar 

  27. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. Association for Computational Linguistics, Doha, Qatar (2014)

    Google Scholar 

  28. Pfohl, S., Triebe, O., Legros, F.: Stance detection for the fake news challenge with attention and conditional encoding (2017)

    Google Scholar 

  29. Pomerleau, D., Rao., D.: The Fake News Challenge: Exploring how Artificial Intelligence Technologies could be Leveraged to Combat Fake News (2017)

    Google Scholar 

  30. Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.: Rumor has it: identifying misinformation in microblogs. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 1589–1599. Association for Computational Linguistics, Edinburgh, Scotland, UK. (2011)

    Google Scholar 

  31. Rakholia, N., Bhargava, S.: “Is it true?”-Deep Learning for Stance Detection in News (2017)

    Google Scholar 

  32. Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., Choi, Y.: Truth of varying shades: analyzing language in fake news and political fact-checking. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2931–2937. Association for Computational Linguistics, Copenhagen, Denmark (2017)

    Google Scholar 

  33. Riedel, B., Augenstein, A., Spithourakis, G.P., Riedel, S.: A simple but tough-to-beat baseline for the fake news challenge stance detection task. CoRR abs/1707.03264 (2017)

    Google Scholar 

  34. Rocktäschel, T., Grefenstette, E., Hermann, K.M., Kocisky, T., Blunsom, P.: Reasoning about Entailment with Neural Attention. In: International Conference on Learning Representations (ICLR) (2016)

    Google Scholar 

  35. Rubin, V.L., Chen, Y., Conroy, N.J.: Deception detection for news: three types of fakes. In: Information Science with Impact: Research in and for the Community - Proceedings of the 78th ASIS &T Annual Meeting, ASIST 2015, St. Louis, Missouri, Missouri, USA, 6–10 October 2015. pp. 1–4 (2015)

    Google Scholar 

  36. Ruder, S., Glover, J., Mehrabani, A., Ghaffari, P.: 360\(^\circ \) stance detection. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pp. 31–35. Association for Computational Linguistics, New Orleans, Louisiana (2018)

    Google Scholar 

  37. Silverman, C.: Lies, damn lies and viral content (2015)

    Google Scholar 

  38. Sukhbaatar, S., Szlam, A., Weston, J., Fergus, R.: End-to-end memory networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) 28th Proceedings conference on Advances in Neural Information Processing Systems, pp. 2440–2448. Montreal, Canada (2015)

    Google Scholar 

  39. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to Sequence Learning with Neural Networks. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, pp. 3104–3112. Montréal, QC, Canada (2014)

    Google Scholar 

  40. Tavernise, S.: As Fake news spreads lies, more readers shrug at the truth. New York Times 6, 110–132 (2016)

    Google Scholar 

  41. Vlachos, A., Riedel, S.: Fact checking: task definition and dataset construction. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 18–22 (2014)

    Google Scholar 

  42. Wang, S., Jiang, J.: Learning natural language inference with LSTM. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 1442–1451. Association for Computational Linguistics, San Diego, California (2016)

    Google Scholar 

  43. Wang, W.Y.: “liar, Liar Pants on Fire”: a new benchmark dataset for fake news detection. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 422–426. Association for Computational Linguistics, Vancouver, Canada (2017)

    Google Scholar 

  44. Yin, W., Schütze, H., Xiang, B., Zhou, B.: Abcnn: attention-based convolutional neural network for modeling sentence pairs. arXiv preprint arXiv:1512.05193 (2015)

  45. Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)

  46. Zhao, Z., Resnick, P., Mei, Q.: Enquiring minds: early detection of rumors in social media from enquiry posts. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1395–1405. International World Wide Web Conferences Steering Committee, Florence, Italy (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Technology Patna, Patna, India

    Tanik Saikh, Kingshuk Basak, Asif Ekbal & Pushpak Bhattacharyya

Authors
  1. Tanik Saikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kingshuk Basak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Asif Ekbal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pushpak Bhattacharyya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tanik Saikh .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Saikh, T., Basak, K., Ekbal, A., Bhattacharyya, P. (2023). “News Title Can Be Deceptive” Title Body Consistency Detection for News Articles Using Text Entailment. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13452. Springer, Cham. https://doi.org/10.1007/978-3-031-24340-0_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-24340-0_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24339-4

  • Online ISBN: 978-3-031-24340-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation