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Detecting fake information with knowledge-enhanced AutoPrompt

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Abstract

The rapid growth of fake news on the Internet poses a challenge to accessing authentic information. Fake news detection plays a crucial role in filtering out false information and improving information accuracy. However, practical implementation faces challenges like high annotation costs, limited samples, poor training results, and weak model generalization. To tackle these issues, we propose knowledge-enhanced AutoPrompt (KEAP) for fake news detection. This method leverages prompt templates generated by the T5 model to transform the fake news detection task into a prompt learning-based task. We also incorporate external entity knowledge to enhance detection capabilities. Carefully designed prompts activate the model’s latent knowledge, improving performance in low-resource scenarios and model generalization. Experiments on GossipCop and PolitiFact datasets demonstrate the superiority of prompt learning over existing methods without extra text during testing. KEAP achieves an average F1 score improvement of 2.79 \(\%\) compared to state-of-the-art methods in few-shot settings.

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Availability of data and materials

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Shu K, Mahudeswaran D, Wang S, Lee D, Liu H (2020) Fakenewsnet: a data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big data 8(3):171–188

    Article  Google Scholar 

  2. Han X, Zhang Z, Ding N, Gu Y, Liu X, Huo Y, Qiu J, Zhang L, Han W, Huang M, et al (2021) Pre-trained models: past, present and future. AI Open

  3. Liu X, Zheng Y, Du Z, Ding M, Qian Y, Yang Z, Tang J (2021) Gpt understands, too. arXiv preprint arXiv:2103.10385

  4. Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21(1):5485–5551

    MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  6. Kaliyar RK, Goswami A, Narang P (2021) Echofaked: improving fake news detection in social media with an efficient deep neural network. Neural Comput Appl 33(14):8597–8613

    Article  Google Scholar 

  7. Inan E (2022) Zoka: a fake news detection method using edge-weighted graph attention network with transfer models. Neural Comput Appl, 1–9

  8. Meel P, Vishwakarma DK (2020) Fake news, rumor, information pollution in social media and web: a contemporary survey of state-of-the-arts, challenges and opportunities. Expert Syst Appl 153:112986

    Article  Google Scholar 

  9. Zhou X, Zafarani R (2020) A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput Surv (CSUR) 53(5):1–40

    Article  Google Scholar 

  10. Przybyla P (2020) Capturing the style of fake news. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 490–497

  11. Zhang X, Cao J, Li X, Sheng Q, Zhong L, Shu K (2021) Mining dual emotion for fake news detection. In: Proceedings of the web conference 2021, pp 3465–3476

  12. Ajao O, Bhowmik D, Zargari S (2019) Sentiment aware fake news detection on online social networks. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2507–2511. IEEE

  13. Ozbay FA, Alatas B (2021) Adaptive salp swarm optimization algorithms with inertia weights for novel fake news detection model in online social media. Multimedia Tools Appl 80(26–27):34333–34357

    Article  Google Scholar 

  14. Ozbay FA, Alatas B (2019) A novel approach for detection of fake news on social media using metaheuristic optimization algorithms. Elektronika ir Elektrotechnika 25(4):62–67

    Article  Google Scholar 

  15. Bahad P, Saxena P, Kamal R (2019) Fake news detection using bi-directional lstm-recurrent neural network. Procedia Comput Sci 165:74–82

    Article  Google Scholar 

  16. Sheng Q, Zhang X, Cao J, Zhong L (2021) Integrating pattern-and fact-based fake news detection via model preference learning. In: Proceedings of the 30th ACM international conference on information & knowledge management, pp 1640–1650

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

  18. Bengio Y, Ducharme R, Vincent P (2000) A neural probabilistic language model. Adv Neural Inf Process Syst 13

  19. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781

  20. Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

  21. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30

  22. Dai Z, Yang Z, Yang Y, Carbonell J, Le QV, Salakhutdinov R (2019) Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860

  23. 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 preprint arXiv:1907.11692

  24. Potamias RA, Siolas G, Stafylopatis A-G (2020) A transformer-based approach to irony and sarcasm detection. Neural Comput Appl 32(23):17309–17320

    Article  Google Scholar 

  25. Qiao B, Zou Z, Huang Y, Fang K, Zhu X, Chen Y (2022) A joint model for entity and relation extraction based on bert. Neural Comput Appl 34(5):3471–3481

    Article  Google Scholar 

  26. Liu J, Shen D, Zhang Y, Dolan B, Carin L, Chen W (2021) What makes good in-context examples for gpt-\(3\)? arXiv preprint arXiv:2101.06804

  27. Zhao Z, Wallace E, Feng S, Klein D, Singh S (2021) Calibrate before use: improving few-shot performance of language models. In: International conference on machine learning, pp 12697–12706. PMLR

  28. Gao T, Fisch A, Chen D (2020) Making pre-trained language models better few-shot learners. arXiv preprint arXiv:2012.15723

  29. Jiang Z, Anastasopoulos A, Araki J, Ding H, Neubig G (2020) X-factr: Multilingual factual knowledge retrieval from pretrained language models. arXiv preprint arXiv:2010.06189

  30. Kassner N, Dufter P, Schütze H (2021) Multilingual lama: Investigating knowledge in multilingual pretrained language models. arXiv preprint arXiv:2102.00894

  31. Shin T, Razeghi Y, Logan IV RL, Wallace E, Singh S (2020) Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980

  32. Li XL, Liang P (2021) Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190

  33. Shu K, Mosallanezhad A, Liu H (2022) In: Khosravy M, Echizen I, Babaguchi N (eds) Cross-domain fake news detection on social media: a context-aware adversarial approach, pp 215–232. Springer, Singapore. https://doi.org/10.1007/978-981-19-1524-6_9

  34. Hewitt J, Manning CD (2019) A structural probe for finding syntax in word representations. 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 4129–4138

  35. Petroni F, Rocktäschel T, Lewis P, Bakhtin A, Wu Y, Miller AH, Riedel S (2019) Language models as knowledge bases? arXiv preprint arXiv:1909.01066

  36. Jiang Z, Xu FF, Araki J, Neubig G (2020) How can we know what language models know? Trans Assoc Comput Linguist 8:423–438

    Article  Google Scholar 

  37. Ferragina P, Scaiella U (2010) Tagme: On-the-fly annotation of short text fragments (by wikipedia entities). In: Proceedings of the 19th ACM international conference on information and knowledge management. CIKM ’10, pp 1625–1628. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/1871437.1871689

  38. Vrandečić D, Krötzsch M (2014) Wikidata: a free collaborative knowledgebase. Commun ACM 57(10):78–85. https://doi.org/10.1145/2629489

    Article  Google Scholar 

  39. 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 

  40. Piccinno F, Ferragina P (2014) From tagme to wat: a new entity annotator. In: Proceedings of the first international workshop on entity recognition & disambiguation, pp 55–62

  41. Castillo C, Mendoza M, Poblete B (2011) Information credibility on twitter. In: Proceedings of the 20th international conference on world wide web, pp 675–684

  42. Kwon S, Cha M, Jung K, Chen W, Wang Y (2013) Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th international conference on data mining, pp 1103–1108 . IEEE

  43. Yang F, Liu Y, Yu X, Yang M (2012) Automatic detection of rumor on sina weibo. In: Proceedings of the ACM SIGKDD workshop on mining data semantics, pp 1–7

  44. Zhang Y, Wallace B (2015) A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820

  45. Wang H, Zhang F, Xie X, Guo M (2018) Dkn: Deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 world wide web conference, pp 1835–1844

  46. Dun Y, Tu K, Chen C, Hou C, Yuan X (2021) Kan: Knowledge-aware attention network for fake news detection. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 81–89

  47. Schick T, Schütze H (2020) Exploiting cloze questions for few shot text classification and natural language inference. arXiv preprint arXiv:2001.07676

  48. Sarzynska-Wawer J, Wawer A, Pawlak A, Szymanowska J, Stefaniak I, Jarkiewicz M, Okruszek L (2021) Detecting formal thought disorder by deep contextualized word representations. Psychiatry Res 304:114135

    Article  Google Scholar 

  49. Kaliyar RK, Goswami A, Narang P (2021) Fakebert: Fake news detection in social media with a bert-based deep learning approach. Multimedia Tools Appl 80(8):11765–11788

    Article  Google Scholar 

Download references

Funding

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 620 72449,61802197) and is also funded in part by the Science and Technology Development Fund, Macau SAR (Grant no. 0018/2019/AKP and SKL-IOTSC(UM)-2021–2023), in part by the Guangdong Science and Technology Department (Grant no. 2018B030324002), in part by the Zhuhai Science and Technology Innovation Bureau Zhuhai-Hong Kong-Macau Special Cooperation Project (Grant no. ZH22017002200001PWC)

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China

    Xun Che & Qianmu Li

  2. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Gang Yang & Yadang Chen

Authors
  1. Xun Che
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  2. Gang Yang
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  3. Yadang Chen
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  4. Qianmu Li
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Contributions

Xun Che was involved in the methodology, conceptualization, validation, writing—original draft, writing—review and editing. Gang Yang contributed to the methodology, investigation, data curation, and software. Yadang Chen assisted in writing—review and editing and supervision. Qianmu Li contributed to writing—review and editing and supervision

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Correspondence to Yadang Chen.

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Che, X., Yang, G., Chen, Y. et al. Detecting fake information with knowledge-enhanced AutoPrompt. Neural Comput & Applic 36, 7725–7742 (2024). https://doi.org/10.1007/s00521-024-09491-7

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