{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T09:03:14Z","timestamp":1769158994270,"version":"3.49.0"},"reference-count":63,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Fake news generation and propagation occurs in large volumes, at high speed, in diverse formats, while also being short-lived to evade detection and counteraction. Despite its role as an enabler, Artificial Intelligence (AI) has been effective at fake news detection and prediction through diverse techniques of both supervised and unsupervised machine learning. In this article, we propose a novel Artificial Intelligence (AI) approach that addresses the underexplored attribution of information asymmetry in fake news detection. This approach demonstrates how fine-tuned language models and emotion embeddings can be used to detect information asymmetry in intent, emotional framing, and linguistic complexity between content creators and content consumers. The intensity and temperature of emotion, selection of words, and the structure and relationship between words contribute to detecting this asymmetry. An empirical evaluation conducted on five benchmark datasets demonstrates the generalizability and real-time detection capabilities of the proposed AI approach.<\/jats:p>","DOI":"10.3390\/sym17081290","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T09:59:13Z","timestamp":1754906353000},"page":"1290","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Addressing the Information Asymmetry of Fake News Detection Using Large Language Models and Emotion Embeddings"],"prefix":"10.3390","volume":"17","author":[{"given":"Kirishnni","family":"Prabagar","sequence":"first","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Melbourne 3086, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7009-5084","authenticated-orcid":false,"given":"Kogul","family":"Srikandabala","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Melbourne 3086, Australia"}]},{"given":"Nilaan","family":"Loganathan","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Melbourne 3086, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6354-8653","authenticated-orcid":false,"given":"Shalinka","family":"Jayatilleke","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Melbourne 3086, Australia"}]},{"given":"Gihan","family":"Gamage","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Melbourne 3086, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3878-5969","authenticated-orcid":false,"given":"Daswin","family":"De Silva","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Melbourne 3086, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"key":"ref_1","first-page":"60","article-title":"Fake news: A classification proposal and a future research agenda","volume":"27","author":"Rahmanian","year":"2022","journal-title":"Span. J. Mark.-ESIC"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1037\/xge0000465","article-title":"Prior exposure increases perceived accuracy of fake news","volume":"147","author":"Pennycook","year":"2018","journal-title":"J. Exp. Psychol. Gen."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Majerczak, P., and Strzelecki, A. (2022). Trust, Media Credibility, Social Ties, and the Intention to Share towards Information Verification in an Age of Fake News. Behav. Sci., 12.","DOI":"10.3390\/bs12020051"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s41235-021-00306-0","article-title":"Infodemic: The effect of death-related thoughts on news-sharing","volume":"6","author":"Lim","year":"2021","journal-title":"Cogn. Res. Princ. Implic."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3381750","article-title":"An emotional analysis of false information in social media and news articles","volume":"20","author":"Ghanem","year":"2020","journal-title":"ACM Trans. Internet Technol. (TOIT)"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102025","DOI":"10.1016\/j.ipm.2019.03.004","article-title":"An overview of online fake news: Characterization, detection, and discussion","volume":"57","author":"Zhang","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_7","unstructured":"Verma, P. (The Washington Post, 2023). The rise of AI fake news is creating a \u2018misinformation superspreader\u2019, The Washington Post."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1007\/s13278-023-01028-5","article-title":"Fake news, disinformation and misinformation in social media: A review","volume":"13","author":"Amri","year":"2023","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lee, D.C., Jhang, J., and Baek, T.H. (2025). AI-Generated News Content: The Impact of AI Writer Identity and Perceived AI Human-Likeness. Int. J. Hum.\u2013Comput. Interact., 1\u201313.","DOI":"10.1080\/10447318.2025.2477739"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"156151","DOI":"10.1109\/ACCESS.2021.3129329","article-title":"A comprehensive review on fake news detection with deep learning","volume":"9","author":"Mridha","year":"2021","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"54757","DOI":"10.1109\/ACCESS.2025.3553909","article-title":"Fake News Detection Landscape: Datasets, Data Modalities, AI Approaches, their Challenges, and Future Perspectives","volume":"13","author":"Hussain","year":"2025","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1007\/s42001-024-00248-9","article-title":"A survey of explainable AI techniques for detection of fake news and hate speech on social media platforms","volume":"7","author":"Gongane","year":"2024","journal-title":"J. Comput. Soc. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4828","DOI":"10.1109\/TCSS.2022.3220420","article-title":"Fake news in virtual community, virtual society, and metaverse: A survey","volume":"11","author":"Wang","year":"2023","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_14","unstructured":"Organisation for Economic Co-operation and Development (2023). The State of Implementation of the OECD AI Principles Four Years on, OECD."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1245\/s10434-018-6372-2","article-title":"The Patient-Reported Information Multidimensional Exploration (PRIME) framework for investigating emotions and other factors of prostate cancer patients with low intermediate risk based on online cancer support group discussions","volume":"25","author":"Bandaragoda","year":"2018","journal-title":"Ann. Surg. Oncol."},{"key":"ref_16","first-page":"S99","article-title":"Addressing the complexities of big data analytics in healthcare: The diabetes screening case","volume":"19","author":"Burstein","year":"2015","journal-title":"Australas. J. Inf. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sumanasena, V., Gunasekara, L., Kahawala, S., Mills, N., De Silva, D., Jalili, M., Sierla, S., and Jennings, A. (2023). Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation. Energies, 16.","DOI":"10.3390\/en16052245"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Madhavi, I., Chamishka, S., Nawaratne, R., Nanayakkara, V., Alahakoon, D., and De Silva, D. (2020, January 8\u201311). A deep learning approach for work related stress detection from audio streams in cyber physical environments. Proceedings of the 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria.","DOI":"10.1109\/ETFA46521.2020.9212098"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.3390\/smartcities7030057","article-title":"Artificial Intelligence in Smart Cities\u2014Applications, Barriers, and Future Directions: A Review","volume":"7","author":"Wolniak","year":"2024","journal-title":"Smart Cities"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1080\/17579961.2021.1898300","article-title":"From a \u2018race to AI\u2019 to a \u2018race to AI regulation\u2019: Regulatory competition for artificial intelligence","volume":"13","author":"Smuha","year":"2021","journal-title":"Law Innov. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2239","DOI":"10.1007\/s10796-021-10154-4","article-title":"Accelerating AI adoption with responsible AI signals and employee engagement mechanisms in health care","volume":"25","author":"Wang","year":"2023","journal-title":"Inf. Syst. Front."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1007\/s00146-023-01650-z","article-title":"The regulation of artificial intelligence","volume":"39","author":"Finocchiaro","year":"2024","journal-title":"AI Soc."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kleyko, D., Osipov, E., De Silva, D., Wiklund, U., and Alahakoon, D. (2019, January 14\u201319). Integer self-organizing maps for digital hardware. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852471"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6583","DOI":"10.1109\/TNNLS.2022.3211274","article-title":"Hyperseed: Unsupervised learning with vector symbolic architectures","volume":"35","author":"Osipov","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_26","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"De Silva, D., Mills, N., El-Ayoubi, M., Manic, M., and Alahakoon, D. (2023, January 4\u20136). ChatGPT and Generative AI Guidelines for Addressing Academic Integrity and Augmenting Pre-Existing Chatbots. Proceedings of the 2023 IEEE International Conference on Industrial Technology (ICIT), Orlando, FL, USA.","DOI":"10.1109\/ICIT58465.2023.10143123"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Singhal, S., Shah, R.R., Chakraborty, T., Kumaraguru, P., and Satoh, S. (2019, January 11\u201313). Spotfake: A multi-modal framework for fake news detection. Proceedings of the 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), Singapore.","DOI":"10.1109\/BigMM.2019.00-44"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jwa, H., Oh, D., Park, K., Kang, J.M., and Lim, H. (2019). exbake: Automatic fake news detection model based on bidirectional encoder representations from transformers (bert). Appl. Sci., 9.","DOI":"10.3390\/app9194062"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"11765","DOI":"10.1007\/s11042-020-10183-2","article-title":"FakeBERT: Fake news detection in social media with a BERT-based deep learning approach","volume":"80","author":"Kaliyar","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_31","unstructured":"Sch\u00fctz, M. (2021). Detection and Identification of Fake News: Binary Content Classification with Pre-Trained Language Models, Austrian Institute of Technology."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s41060-021-00302-z","article-title":"Fake news detection based on news content and social contexts: A transformer-based approach","volume":"13","author":"Raza","year":"2022","journal-title":"Int. J. Data Sci. Anal."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Caramancion, K.M. (2023). News verifiers showdown: A comparative performance evaluation of ChatGPT 3.5, ChatGPT 4.0, Bing AI, and bard in news fact-checking. arXiv.","DOI":"10.1109\/FNWF58287.2023.10520446"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wu, G., Wu, W., Liu, X., Xu, K., Wan, T., and Wang, W. (2023). Cheap-fake Detection with LLM using Prompt Engineering. arXiv.","DOI":"10.1109\/ICMEW59549.2023.00025"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103029","DOI":"10.1016\/j.ipm.2022.103029","article-title":"Fake news detection via knowledgeable prompt learning","volume":"59","author":"Jiang","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ajao, O., Bhowmik, D., and Zargari, S. (2019, January 12\u201317). Sentiment aware fake news detection on online social networks. Proceedings of the ICASSP 2019\u20142019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683170"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Giachanou, A., Rosso, P., and Crestani, F. (2019, January 21\u201325). Leveraging emotional signals for credibility detection. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France.","DOI":"10.1145\/3331184.3331285"},{"key":"ref_38","unstructured":"Guo, C., Cao, J., Zhang, X., Shu, K., and Yu, M. (2019). Exploiting emotions for fake news detection on social media. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, X., Cao, J., Li, X., Sheng, Q., Zhong, L., and Shu, K. (2021, January 19\u201323). Mining dual emotion for fake news detection. Proceedings of the Web Conference 2021, Ljubljana, Slovenia.","DOI":"10.1145\/3442381.3450004"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Buntain, C., and Golbeck, J. (2017, January 3\u20135). Automatically identifying fake news in popular twitter threads. Proceedings of the 2017 IEEE International Conference on Smart Cloud (SmartCloud), New York, NY, USA.","DOI":"10.1109\/SmartCloud.2017.40"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yang, F., Pentyala, S.K., Mohseni, S., Du, M., Yuan, H., Linder, R., Ragan, E.D., Ji, S., and Hu, X. (2019, January 13\u201317). Xfake: Explainable fake news detector with visualizations. Proceedings of the World Wide Web Conference, San Francisco, CA, USA.","DOI":"10.1145\/3308558.3314119"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TCSS.2021.3068519","article-title":"WELFake: Word embedding over linguistic features for fake news detection","volume":"8","author":"Verma","year":"2021","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_43","unstructured":"Merto\u011flu, U., and Gen\u00e7, B. (2020). Lexicon generation for detecting fake news. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yanagi, Y., Orihara, R., Sei, Y., Tahara, Y., and Ohsuga, A. (2020, January 8\u201310). Fake news detection with generated comments for news articles. Proceedings of the 2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES), Reykjav\u00edk, Iceland.","DOI":"10.1109\/INES49302.2020.9147195"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1177\/0146167203029005010","article-title":"Lying words: Predicting deception from linguistic styles","volume":"29","author":"Newman","year":"2003","journal-title":"Personal. Soc. Psychol. Bull."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Patwa, P., Sharma, S., Pykl, S., Guptha, V., Kumari, G., Akhtar, M.S., Ekbal, A., Das, A., and Chakraborty, T. (2020). Fighting an Infodemic: COVID-19 Fake News Dataset. arXiv.","DOI":"10.1007\/978-3-030-73696-5_3"},{"key":"ref_47","unstructured":"(2024, October 04). Real or Fake News Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/rchitic17\/real-or-fake."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1089\/big.2020.0062","article-title":"Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media","volume":"8","author":"Shu","year":"2020","journal-title":"Big Data"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, W.Y. (2017). \u201cliar, liar pants on fire\u201d: A new benchmark dataset for fake news detection. arXiv.","DOI":"10.18653\/v1\/P17-2067"},{"key":"ref_50","unstructured":"(2024, October 21). ISOT Fake News Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/csmalarkodi\/isot-fake-news-dataset."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1609\/icwsm.v13i01.3254","article-title":"FA-KES: A Fake News Dataset around the Syrian War","volume":"13","author":"Elbassuoni","year":"2019","journal-title":"Proc. Int. AAAI Conf. Web Soc. Media"},{"key":"ref_52","unstructured":"Koirala, A. (2020). COVID-19 fake news classification with deep learning. Preprint."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., and Choi, Y. (2017, January 9\u201311). Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.","DOI":"10.18653\/v1\/D17-1317"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lai, J., Yang, X., Luo, W., Zhou, L., Li, L., Wang, Y., and Shi, X. (2024). RumorLLM: A Rumor Large Language Model-Based Fake-News-Detection Data-Augmentation Approach. Appl. Sci., 14.","DOI":"10.3390\/app14083532"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3267","DOI":"10.1007\/s10115-024-02321-1","article-title":"Fake news detection: Comparative evaluation of BERT-like models and large language models with generative AI-annotated data","volume":"67","author":"Raza","year":"2025","journal-title":"Knowl. Inf. Syst."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"102300","DOI":"10.1016\/j.inffus.2024.102300","article-title":"Emotion Detection for Misinformation: A Review","volume":"107","author":"Liu","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1057\/s41599-024-03083-5","article-title":"Emotions unveiled: Detecting COVID-19 fake news on social media","volume":"11","author":"Farhoudinia","year":"2024","journal-title":"Humanit. Soc. Sci. Commun."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"35781","DOI":"10.1007\/s11042-023-14883-3","article-title":"Fake news detection on social media based on sentiment analysis using classifier techniques","volume":"82","author":"Balshetwar","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Alsmadi, I., Alazzam, I., Al-Ramahi, M., and Zarour, M. (2024). Stance Detection in the Context of Fake News\u2014A New Approach. Future Internet, 16.","DOI":"10.3390\/fi16100364"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s13278-023-01185-7","article-title":"BRaG: A hybrid multi-feature framework for fake news detection on social media","volume":"14","author":"Chelehchaleh","year":"2024","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"111715","DOI":"10.1016\/j.knosys.2024.111715","article-title":"DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection","volume":"294","author":"Truicua","year":"2023","journal-title":"Knowl. Based Syst."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Wang, J., Zheng, J., Yao, S., Wang, R., and Du, H. (2023). TLFND: A Multimodal Fusion Model Based on Three-Level Feature Matching Distance for Fake News Detection. Entropy, 25.","DOI":"10.3390\/e25111533"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"119446","DOI":"10.1016\/j.ins.2023.119446","article-title":"Similarity-Aware Multimodal Prompt Learning for Fake News Detection","volume":"647","author":"Jiang","year":"2023","journal-title":"Inf. Sci."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/8\/1290\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:24:31Z","timestamp":1760034271000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/8\/1290"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,11]]},"references-count":63,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["sym17081290"],"URL":"https:\/\/doi.org\/10.3390\/sym17081290","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,11]]}}}