Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation
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Recent ubiquity and disruptive impacts of large language models (LLMs) have raised concerns about their potential to be misused (i.e., generating large-scale harmful and misleading content).To combat this emerging risk of LLMs, we propose a novel "Fighting Fire with Fire" (F3) strategy that harnesses modern LLMs' generative and emergent reasoning capabilities to counter human-written and LLM-generated disinformation.First, we leverage GPT-3.5-turbo to synthesize authentic and deceptive LLMgenerated content through paraphrase-based and perturbation-based prefix-style prompts, respectively.Second, we apply zero-shot incontext semantic reasoning techniques with cloze-style prompts to discern genuine from deceptive posts & news articles.In our extensive experiments, we observe GPT-3.5-turbo'szero-shot superiority for both in-distribution and out-of-distribution datasets, where GPT-3.5-turboconsistently achieved accuracy at 68-72%, unlike the decline observed in previous customized and fine-tuned disinformation detectors.Our codebase and dataset are available at https://github.com/mickeymst/F3.
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Methods for detecting hallucinations and evaluating their mitigation are themselves not foolproof.
“methods for detecting hallucinations and evaluating their mitigation are themselves not foolproof.”
From Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models (2024) · cites this paper as Lucas et al. 2023
Appendix B.2 identifies limitations of statistical hallucination-detection methods, and Section 5.3.1 says overlapping metric scores make real and fake news difficult to distinguish. These passages support the point that detection is not foolproof, but the paper does not address whether methods for evaluating hallucination mitigation are reliable. (LLM, conf=0.82)
Experimental — accuracy not yet measuredEvaluation datasets and metrics may not always accurately reflect real-world performance reliability.
“Evaluation datasets and metrics may not always accurately reflect real-world performance reliability (Ji, Lee, et al. 2023; Y. Zhang et al. 2023; Lucas et al. 2023).”
From Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models (2024) · cites this paper as Lucas et al. 2023
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · see citing works
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831 · see citing works
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · see citing works
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · see citing works
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · see citing works
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263 · see citing works
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096 · see citing works
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · arXiv · doi:10.48550/arxiv.2104.08691 · Cited by 3,092
BERTScore: Evaluating Text Generation with BERT
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09675 · Cited by 2,865 · see citing works
Llama 2: Open Foundation and Fine-Tuned Chat Models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.09288 · Cited by 2,604 · see citing works
Self-citationPrefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L., & Liang, P. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.353 · Cited by 2,473 · see citing works
Longformer: The Long-Document Transformer
Beltagy, I., Peters, M. E., & Cohan, A. (2020) · arXiv · doi:10.48550/arxiv.2004.05150 · Cited by 2,281 · see citing works
A Survey of Large Language Models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Dong, Z., Hou, Y., Zhang, B., Min, Y., Zhang, J., Liu, P., et al. (2026) · Frontiers of Computer Science · doi:10.1007/s11704-026-60308-3 · Cited by 2,093 · see citing works
A Survey of Large Language Models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.18223 · Cited by 2,090
Large language models are zero-shot reasoners
Kojima, T., Gu, S. (., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1613 · Cited by 2,013 · see citing works
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
Qi, P., Zhang, Y., Zhang, Y., Bolton, J., & Manning, C. D. (2020) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2020.acl-demos.14 · Cited by 1,505 · see citing works
Making Pre-trained Language Models Better Few-shot Learners
Gao, T., Fisch, A., & Chen, D. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.295 · Cited by 1,380 · see citing works
Shu, K., Mahudeswaran, D., Wang, S., Lee, D., & Liu, H. (2018) · Big Data · doi:10.1089/big.2020.0062 · Cited by 1,362 · see citing works
Self-citationExploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference
Schick, T., & Schütze, H. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.20 · Cited by 1,305 · see citing works
The Curious Case of Neural Text Degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09751 · Cited by 1,304 · see citing works
Emergent Abilities of Large Language Models
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.07682 · Cited by 1,264 · see citing works
DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION
He, P., Liu, X., Gao, J., & Chen, W. (2021) · International Conference on Learning Representations · Cited by 1,116 · see citing works
Detecting rumors from microblogs with recurrent neural networks
Ma, J., Gao, W. G., Mitra, P., Kwon, S., Jansen, B. J. J., Wong, K., & Cha, M. (2016) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 1,102 · see citing works
Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
Reynolds, L., & McDonell, K. (2021) · Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411763.3451760 · Cited by 952 · see citing works
Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.10403 · Cited by 834 · see citing works
dEFEND: Explainable Fake News Detection
Shu, K., Cui, L.-M., Wang, S., Lee, D., & Liu, H. (2019) · Knowledge Discovery and Data Mining · doi:10.1145/3292500.3330935 · Cited by 767 · see citing works
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Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533088 · Cited by 764 · see citing works
ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health
De Angelis, L., Baglivo, F., Arzilli, G., Privitera, G. P., Ferragina, P., Tozzi, A. E., & Rizzo, C. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1166120 · Cited by 708 · see citing works
Code Llama: Open Foundation Models for Code
Rozière, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Sauvestre, R., Remez, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2308.12950 · Cited by 531 · see citing works
Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., & Yang, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.85 · Cited by 519 · see citing works
A Survey on Natural Language Processing for Fake News DetectionPreprint
Oshikawa, R., Qian, J., & Wang, W. Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00770 · Cited by 390 · see citing works
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.04023 · Cited by 337 · see citing works
Zhou, J., Zhang, Y., Luo, Q., Parker, A. G., & De Choudhury, M. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581318 · Cited by 288 · see citing works
Nguyen, V.-H., Sugiyama, K., Nakov, P., & Kan, M. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412046 · Cited by 286 · see citing works
Automatic Chain of Thought Prompting in Large Language Models
Zhang, Z., Zhang, A., Li, M., & Smola, A. (2022) · arXiv · doi:10.48550/arxiv.2210.03493 · Cited by 274 · see citing works
CoAID: COVID-19 Healthcare Misinformation DatasetPreprint
Cui, L., & Lee, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.00885 · Cited by 252 · see citing works
Self-citationWARP: Word-level Adversarial ReProgramming
Hambardzumyan, K., Khachatrian, H., & May, J. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.381 · Cited by 243 · see citing works
AI model GPT-3 (dis)informs us better than humans
Spitale, G., Biller-Andorno, N., & Germani, F. (2023) · Science Advances · doi:10.1126/sciadv.adh1850 · Cited by 241 · see citing works
GSum: A General Framework for Guided Neural Abstractive Summarization
Dou, Z.-Y., Liu, P., Hayashi, H., Jiang, Z., & Neubig, G. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.384 · Cited by 210 · see citing works
DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation
Cui, L., Seo, H., Tabar, M., Ma, F., Wang, S., & Lee, D. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403092 · Cited by 177 · see citing works
Self-citationPenedo, G., Malartic, Q., Hesslow, D., Cojocaru, R., Cappelli, A., Alobeidli, H., Pannier, B., Almazrouei, E., & Launay, J. (2023) · arXiv · doi:10.48550/arxiv.2306.01116 · Cited by 164 · see citing works
Can AI-Generated Text be Reliably Detected?
Sadasivan, V. S., Kumar, A., Balasubramanian, S., Wang, W., & Feizi, S. (2023) · arXiv · doi:10.48550/arxiv.2303.11156 · Cited by 161 · see citing works
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
Krishna, K., Song, Y., Karpinska, M., Wieting, J., & Iyyer, M. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-1195 · Cited by 150 · see citing works
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
Krishna, K., Song, Y., Karpinska, M., Wieting, J., & Iyyer, M. (2023) · arXiv · doi:10.48550/arxiv.2303.13408 · Cited by 150 · see citing works
Fake Detect: A Deep Learning Ensemble Model for Fake News Detection
Aslam, N., Khan, I. U., Alotaibi, F. S., Aldaej, L. A., & Aldubaikil, A. K. (2021) · Complexity · doi:10.1155/2021/5557784 · Cited by 149 · see citing works
Memory-Guided Multi-View Multi-Domain Fake News Detection
Zhu, Y., Sheng, Q., Cao, J., Nan, Q., Shu, K., Wu, M., Wang, J., & Zhuang, F. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3185151 · Cited by 145 · see citing works
Motivations, Methods and Metrics of Misinformation Detection: An NLP Perspective
Su, Q., Wan, M., Liu, X., & Huang, C.-R. (2020) · Natural Language Processing Research · doi:10.2991/nlpr.d.200522.001 · Cited by 105 · see citing works
Evaluating the logical reasoning ability of chatgpt and GPT-4
Liu, H., Ning, R., Teng, Z., Liu, J., Zhou, Q., & Zhang, Y. (2023) · arXiv · doi:10.48550/arxiv.2304.03439 · Cited by 100 · see citing works
Paraphrasing with Large Language Models
Witteveen, S., & Andrews, M. (2019) · Workshop on Neural Generation and Translation (WNGT) · doi:10.18653/v1/d19-5623 · Cited by 92 · see citing works
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
Uchendu, A., Ma, Z., Le, T., Zhang, R., & Lee, D. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.172 · Cited by 82 · see citing works
Self-citationAlignScore: Evaluating factual consistency with a unified alignment function
Zha, Y., Yang, Y., Li, R., & Hu, Z. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.634 · Cited by 81 · see citing works
Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique
Ali, A., Ghaleb, F. A. A., Al‐rimy, B. A. S., Alsolami, F. J., & Khan, A. I. (2022) · Sensors · doi:10.3390/s22186970 · Cited by 60 · see citing works
On the Possibilities of AI-Generated Text Detection
Chakraborty, S., Bedi, A. S., Zhu, S., An, B., Manocha, D., & Huang, F. (2023) · arXiv · doi:10.48550/arxiv.2304.04736 · Cited by 59 · see citing works
Distributional Measures of Semantic Distance: A SurveyPreprint
Mohammad, S. M., & Hirst, G. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1203.1858 · Cited by 58 · see citing works
Dataset of Fake News Detection and Fact Verification: A Survey
Murayama, T. (2021) · arXiv · doi:10.48550/arxiv.2111.03299 · Cited by 40 · see citing works
Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners
Tang, X., Zheng, Z., Li, J., Meng, F., Zhu, S.-C., Liang, Y., & Zhang, M. (2023) · arXiv · doi:10.48550/arxiv.2305.14825 · Cited by 37 · see citing works
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
Uchendu, A., Lee, J., Shen, H., Le, T., Huang, T.-H., & Lee, D. (2023) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v11i1.27557 · Cited by 35 · see citing works
Self-citationBLiMP: the benchmark of linguistic minimal pairs for English
Warstadt, A., Parrish, A., Liu, H., Mohananey, A., Peng, W., Wang, S.-F., & Bowman, S. R. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00321 · Cited by 28 · see citing works
Taking MT Evaluation Metrics to Extremes: Beyond Correlation with Human Judgments
Fomicheva, M., & Specia, L. (2019) · Computational Linguistics · doi:10.1162/coli_a_00356 · Cited by 24 · see citing works
DEMETR: Diagnosing Evaluation Metrics for Translation
Karpinska, M., Raj, N., Thai, K., Song, Y., Gupta, A., & Iyyer, M. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.649 · Cited by 22 · see citing works
Sun, Y., He, J., Lei, S., Cui, L., & Lu, C. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.08871 · Cited by 13 · see citing works
Fake News Detection using a Decentralized Deep Learning Model and Federated Learning
Jayakody, N., Mohammad, A., & Halgamuge, M. N. (2022) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon49645.2022.9968358 · Cited by 12 · see citing works
The effects of different paraphrasing styles on the quality of reports from young child witnesses
Evans, A. D., & Roberts, K. P. (2009) · Psychology Crime and Law · doi:10.1080/10683160802385398 · Cited by 10 · see citing works
Hybrid Deep Learning Model for Fake News Detection in Social Networks (Student Abstract)
Upadhayay, B., & Behzadan, V. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21670 · Cited by 9 · see citing works
PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer
Chen, L., Chen, J., Huang, H., & Cheng, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.833 · Cited by 4 · see citing works
Controllable Paraphrasing and Translation with a Syntactic Exemplar.Preprint
Chen, M., Wiseman, S., & Gimpel, K. (2020) · arXiv (Cornell University) · Cited by 3 · see citing works
Exemplar-Controllable Paraphrasing and Translation using BitextPreprint
Chen, M., Wiseman, S., & Gimpel, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.05856 · Cited by 2 · see citing works
Paraphrasing, textual entailment, and semantic similarity above word levelPreprint
Kovatchev, V. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2208.05387 · Cited by 1 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
Research works printed but not linked: 5
- Angela D Evans and Kim P Roberts. 2009b. Can paraphrasing increase the amount and accuracy of reports from child eyewitnesses? "Psy- chology, Crime, & Law, 6, 531-546. DOI: 10.1080/10683160802385398". · DOI
- Martin Müller, Marcel Salathé, and Per E Kummervold. 2023. Covid-twitter-bert: A natural language pro- cessing model to analyse covid-19 content on twitter. Frontiers, 6:1023281. · DOI
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.
- Faisal Rahutomo, Teruaki Kitasuka, and Masayoshi Ar- itsugi. 2012. Semantic cosine similarity. In The 7th international student conference on advanced science and technology ICAST, volume 4, page 1.
- Xiaojuan Tang, Zilong Zheng, Jiaqi Li, Fanxu Meng, Song-Chun Zhu, Yitao Liang, and Muhan Zhang. 2023b. Large language models are in-context seman- tic reasoners rather than symbolic reasoners. arXiv preprint arXiv:2305.14825. · arXiv
Cited by
Sorted by citations
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Dahl, M., Magesh, V., Suzgun, M., & Ho, D. E. (2024) · Journal of Legal Analysis · doi:10.1093/jla/laae003 · Cited by 288 · see citing works
Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks
Wu, J., Guo, J., & Hooi, B. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671977 · Cited by 115 · see citing works
Navigating LLM ethics: advancements, challenges, and future directions
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Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models
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Detecting AI-Generated Text: Factors Influencing Detectability with Current Methods
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Open Science at the generative AI turn: An exploratory analysis of challenges and opportunities
Hosseini, M., Horbach, S. P. J. M., Holmes, K., & Ross-Hellauer, T. (2025) · Quantitative Science Studies · doi:10.1162/qss_a_00337 · Cited by 40 · see citing works
Silver Lining in the Fake News Cloud: Can Large Language Models Help Detect Misinformation?
Kumar, R., Goddu, B., Saha, S., & Jatowt, A. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3440248 · Cited by 32 · see citing works
Enhancing text-centric fake news detection via external knowledge distillation from LLMs
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Beyond Text: Multimodal Credibility Assessment Approaches for Online User-Generated Content
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Proma, A. M., Pate, N., Druckman, J. N., Ghoshal, G., & Hoque, M. E. (2025) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3699682.3728349 · Cited by 3 · see citing works
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Lu, M., He, Z., Guo, Y., Liu, S., Huang, J., Zhao, Y., Tian, Z., Zhao, X., Shao, C., Deng, L., et al. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3650830 · Cited by 3 · see citing works
Li, F., Yuanjie, L. I. U., Yang, Y., & Yu, G. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2618553 · Cited by 3 · see citing works
Behnamian, S., Shahbazi, Z., Shahbazi, Z., & Jafari, S. (2026) · Information · doi:10.3390/info17050481 · Cited by 3 · see citing works
Detecting Artificial Intelligence-Generated Textual and Image Misinformation Using Machine Learning
Bao, A. (2024) · International Symposium on Computer Engineering and Intelligent Communications (ISCEIC) · doi:10.1109/isceic63613.2024.10810219 · Cited by 3 · see citing works
GuARD: Effective Anomaly Detection through a Text-Rich and Graph-Informed Language Model
Pang, Y., Chen, B., Zhang, F., Rao, Y., Kharlamov, E., & Tang, J. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736993 · Cited by 2 · see citing works
Robust Fake News Detection using Large Language Models under Adversarial Sentiment Attacks
Tahmasebi, S., Müller-Budack, E., & Ewerth, R. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792606 · Cited by 2 · see citing works
Kumar, S., & Singh, S. R. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3623667 · Cited by 1 · see citing works
MESE: Mining Emotional and Semantic Evolution From User Comments for Fake News Detection
Xu, R., Shang, J., Guan, M., Wang, J., Cui, H., & Min, G. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3690971 · Cited by 1 · see citing works
Transformer-Based Misinformation Detection in Headache Medicine: A BERT Model Approach
Dave, J. M., Patel, P., & Zhang, P. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11252870
ReFEND: Leveraging Social Sentiment Resonances for Fake News Detection
Guan, M., Shang, J., Xu, R., Hao, F., Li, R., & Min, G. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3676162
Enhancing Fake News Detection Resilience Against Style Attacks: The Content-Style Invariant Detector
Hong, V. N., Bao, P. N., Anh, H. T. M., Loc, V. P., Dung, H. T. C., Nguyen, M. Y., Hao, L. V. N., & Tran, T. K. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-21625-0_17
Liar, Liar, LLM on Fire: Investigating Deception in Turkish Text Generation
Bölücü, N., Yücel, Z., & Çetintaş, D. (2026) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3811025
Knowing What They Don’t Know: LLM Uncertainty and the Limits of Automated Misinformation Detection
Mortaga, M., Sieger, M., Diedrichs, H., Kojan, L., & Valdez, A. S. C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29842-3_25
Sun, L., Rao, Y., Xia, H., Jiang, H., & Zhang, C. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770854.3780276
BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages
Lucas, J. S., White, M. A., Uchendu, A., Al-Lawati, A., Yamashita, M., Macko, D., Srba, I., Móro, R., & Lee, D. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3817546
Self-citationEvoFEND: Dual Memory-Driven Self-Evolving Fake News Detection
Hu, B., Sheng, Q., Mi, H., Wu, J., Wang, Z., Yu, Y., Wang, D., Hu, X., & Cao, J. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3817673
Tao, T., & Li, Y. (2026) · Aslib Journal of Information Management · doi:10.1108/ajim-11-2025-0940
Muraku, B., & Meçe, E. K. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-032-29430-2_16
- Authors
- Lucas, J., Uchendu, A., Yamashita, M., Lee, J., Rohatgi, S., & Lee, D.
- Affiliations
- Pennsylvania State University (US)
- Venue
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Published
- 2023
- Pages
- 14279-14305
- OpenAlex
- W4389523840
- Cited by
- 37
- References
- 65
- Citations by year
- 2026: 16 · 2025: 11 · 2024: 10
- FWCI
- 34.36
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1820609, 1934782, 2114824, 2131144)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- disinformation detection, large language models, GPT-3.5-turbo
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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