Digital news environments are increasingly shaped by algorithmic amplification and fragmented audience engagement, enabling the unchecked spread of misinformation. Among the rhetorical strategies that obscure truth, irony and sarcasm pose unique challenges to automated detection systems due to their subtle contextual dependencies and linguistic ambiguity. To better understand and mitigate these forms of obfuscation, we curate a dataset of 1,200,000 English-language news headlines, combining verified satirical sources and crowd-sourced annotations to capture latent sarcastic and ironic cues. Among several transformer models evaluated, XLNet achieved the strongest performance and forms the basis of the primary reported results. We further apply a dual-layered attention mechanism to differentiate between ironic critique and factual distortion. To examine affective undertones, we incorporate the VADER sentiment lexicon to profile emotional valence and juxtapose it with misclassification likelihoods. Results reveal that sarcasm often overlaps with misinformation indicators, particularly when humor is weaponized to delegitimize opposing viewpoints or obscure factual content. We identify four recurring sarcasm archetypes in misinformation-laden headlines, with varying degrees of recognizability to models and readers alike. Contrary to expectations, emotional polarity alone proved insufficient for accurate sarcasm detection, suggesting a necessary interplay between pragmatics and machine inference. Our findings highlight the role of nuanced language in complicating efforts to regulate misinformation and offer empirical insight for developers of trustworthy news-ranking algorithms and digital literacy tools.
References
[1]
González-Ibáñez, R., Muresan, S. and Wacholder, N. (2011) Identifying Sarcasm in Twitter: A Closer Look. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, 19-24 June 2011, 581-586.
[2]
Wallace, B.C., Choe, D.K. and Charniak, E. (2015) Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, July 2015, 1035-1044. https://doi.org/10.3115/v1/p15-1100
[3]
Reyes, A., Rosso, P. and Veale, T. (2013) A Multidimensional Approach for Detecting Irony in Twitter. Language Resources and Evaluation, 47, 239-268. https://doi.org/10.1007/s10579-012-9196-x
[4]
Pennycook, G. and Rand, D.G. (2019) Fighting Misinformation on Social Media Using Crowdsourced Judgments of News Source Quality. Proceedings of the National Academy of Sciences, 116, 2521-2526. https://doi.org/10.1073/pnas.1806781116
[5]
Lazer, D.M.J., Baum, M.A., Benkler, Y., Berinsky, A.J., Greenhill, K.M., Menczer, F., et al. (2018) The Science of Fake News. Science, 359, 1094-1096. https://doi.org/10.1126/science.aao2998
[6]
Davidov, D., Tsur, O. and Rappoport, A. (2010) Semi-Supervised Recognition of Sarcastic Sentences in Twitter and Amazon. Proceedings of the 14th Conference on Computational Natural Language Learning, Uppsala, 15-16 July 2010, 107-116. https://aclanthology.org/W10-2914/
[7]
Giora, R. (2003) On Our Mind: Salience, Context, and Figurative Language. Oxford University Press.
[8]
Baly, R., Karadzhov, G., Alexandrov, D., Glass, J. and Nakov, P. (2018) Predicting Factuality of Reporting and Bias of News Media Sources. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, 31 October-4 November 2018, 3528-3529. https://doi.org/10.18653/v1/d18-1389
[9]
Kumar, R., Ojha, A.K., Malmasi, S. and Zampieri, M. (2020) Evaluating Aggression Identification in Social Media. Proceedings of the 2nd Workshop on Trolling, Aggression and Cyberbullying, Marseille, 16 May 2020, 1-5.
[10]
Tsur, O., Davidov, D. and Rappoport, A. (2010) ICWSM—A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews. ProceedingsoftheInternationalAAAIConferenceonWebandSocialMedia, 4, 162-169. https://doi.org/10.1609/icwsm.v4i1.14018
[11]
Devlin, J., Chang, M.W., Lee, K. and Toutanova, K. (2018) BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, 31 October-4 November 2018, 3528–3539. https://aclanthology.org/D18-1389/
[12]
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., et al. (2019) RoBERTa: A Robustly Optimized BERT Pretraining Approach. https://arxiv.org/abs/1907.11692
[13]
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. and Le, Q.V. (2019) XLNet: Generalized Autoregressive Pretraining for Language Understanding. Advances in Neural Information Processing Systems (NeurIPS 32), Vancouver, 8-14 December 2019, 1-18. https://arxiv.org/abs/1906.08237
[14]
Sobhani, P., Riloff, E. and Carter, S. (2021) Transformer Models for Sarcasm Detection: A Benchmark Study. 2021 Proceedings of the Workshop on Figurative Language Processing, Online, 10 June 2021, 42-53. https://aclanthology.org/2021.figlang-1.5/
[15]
Gorrell, G., Bakir, M.E., Roberts, I., Greenwood, M.A., et al. (2019) Partisanship, Propaganda and Post-Truth Politics: Quantifying Impact in Online Debate. http://eprints.whiterose.ac.uk/143174/
[16]
Horne, B.D., Nørregaard, J. and Adalı, S. (2019) Different Spirals of Sameness: A Study of Content Sharing in Mainstream and Alternative Media. ProceedingsoftheInternationalAAAIConferenceonWebandSocialMedia, 13, 257-266. https://doi.org/10.1609/icwsm.v13i01.3227
[17]
Van Hee, C., Lefever, E. and Hoste, V. (2018) SemEval-2018 Task 3: Irony Detection in English Tweets. Proceedings of the 12th International Workshop on Semantic Evaluation, New Orleans, 5-6 June 2018, 399-50. https://doi.org/10.18653/v1/s18-1005
[18]
Oprea, S. and Magdy, W. (2020) iSarcasm: A Dataset of Intended Sarcasm. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 5-10 July 2020, 1279-1289. https://doi.org/10.18653/v1/2020.acl-main.118
[19]
Riloff, E., Qadir, A., Surve, P., De Silva, L., Gilbert, N. and Huang, R. (2013) Sarcasm as Contrast between a Positive Sentiment and Negative Situation. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, 18-21 October 2013, 704-714. https://doi.org/10.18653/v1/d13-1066
[20]
Md Suhaimin, M.S., Ahmad Hijazi, M.H., Moung, E.G., Nohuddin, P.N.E., Chua, S. and Coenen, F. (2023) Social Media Sentiment Analysis and Opinion Mining in Public Security: Taxonomy, Trend Analysis, Issues and Future Directions. Journal of King Saud University—Computer and Information Sciences, 35, Article 101776. https://doi.org/10.1016/j.jksuci.2023.101776
[21]
Mohammad, S.M. and Turney, P.D. (2013) Crowdsourcing a Word–Emotion Association Lexicon. Computational Intelligence, 29, 436-465. https://doi.org/10.1111/j.1467-8640.2012.00460.x
[22]
Cambria, E., Li, Y., Xing, F.Z., Poria, S. and Kwok, K. (2020) SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, Ireland, 29-23 October 2020, 105-114. https://doi.org/10.1145/3340531.3412003
[23]
Ghosh, A. and Veale, D.T. (2016) Fracking Sarcasm Using Neural Network. Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, San Diego, 16 June 2016, 161-169. https://doi.org/10.18653/v1/w16-0425
[24]
Harris, S., Hadi, H.J., Ahmad, N. and Alshara, M.A. (2024) Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas. Technologies, 12, Article 222. https://doi.org/10.3390/technologies12110222
[25]
Bourgonje, P., Moreno Schneider, J. and Rehm, G. (2017) From Clickbait to Fake News Detection: An Approach Based on Detecting the Stance of Headlines to Articles. Proceedings of the 2017 EMNLP Workshop: Natural Language Processing Meets Journalism, Copenhagen, 7 September 2017, 84-89. https://doi.org/10.18653/v1/w17-4215
[26]
Zhang, J., Zhao, Y., Saleh, M. and Liu, P. (2020) PEGASUS: Pre-Training with Extracted Gap-Sentences for Abstractive Summarization. 2020 Proceedings of the 37th International Conference on Machine Learning, Vienna, 13-18 July 2020, 11328-11339.
[27]
Carrington, A.M., Manuel, D.G., Fieguth, P.W., Ramsay, T., Osmani, V., Wernly, B., et al. (2022) Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 329-341. https://doi.org/10.1109/tpami.2022.3145392
[28]
Wallace, B.C., Choe, D.K., Kertz, L. and Charniak, E. (2014) Humans Require Context to Infer Ironic Intent (so Computers Probably Do, Too). Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, 22-27 June 2014, 512-516. https://doi.org/10.3115/v1/p14-2084
[29]
Campbell, J.D. and Katz, A.N. (2012) Are There Necessary Conditions for Inducing a Sense of Sarcastic Irony? Discourse Processes, 49, 459-480. https://doi.org/10.1080/0163853x.2012.687863
[30]
Khodak, M., Saunshi, N. and Vodrahalli, K. (2018) A Large Self-Annotated Corpus for Sarcasm. 2018 Proceedings of the 11th International Conference on Language Resources and Evaluation, Miyazaki, 7-12 May 2018, 1-6. https://aclanthology.org/L18-1102/
[31]
Wilson, D. and Sperber, D. (2012) Meaning and Relevance. Cambridge University Press. https://doi.org/10.1017/cbo9781139028370
[32]
Attardo, S. (2000) Irony as Relevant Inappropriateness. Journal of Pragmatics, 32, 793-826. https://doi.org/10.1016/s0378-2166(99)00070-3
[33]
Colleoni, E., Rozza, A. and Arvidsson, A. (2014) Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data. Journal of Communication, 64, 317-332. https://doi.org/10.1111/jcom.12084
