{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T20:19:15Z","timestamp":1776111555906,"version":"3.50.1"},"publisher-location":"Cham","reference-count":54,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031891021","type":"print"},{"value":"9783031891038","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-89103-8_5","type":"book-chapter","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T07:29:23Z","timestamp":1745479763000},"page":"65-87","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Augmenting Dark Patterns Text Data by\u00a0Leveraging Large Language Models: A Multi-agent Framework and\u00a0Parameter-Efficient Fine-Tuning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2534-5366","authenticated-orcid":false,"given":"Emre","family":"Kocyigit","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1124-0299","authenticated-orcid":false,"given":"Davide","family":"Liga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8229-3270","authenticated-orcid":false,"given":"Gabriele","family":"Lenzini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"5_CR1","unstructured":"Deceptive Patterns (aka Dark Patterns) - spreading awareness since 2010 \u2014 deceptive.design. https:\/\/www.deceptive.design. Accessed 14 June 2024"},{"key":"5_CR2","unstructured":"GitHub - eugeneyan\/open-llms: A list of open LLMs available for commercial use. \u2014 github.com. https:\/\/github.com\/eugeneyan\/open-llms. Accessed 10 June 2024"},{"key":"5_CR3","unstructured":"Hugging Face - The AI community building the future. \u2014 huggingface.co. https:\/\/huggingface.co Accessed 24 June 2024"},{"key":"5_CR4","unstructured":"Introducing Meta Llama 3: The most capable openly available LLM to date \u2014 ai.meta.com. https:\/\/ai.meta.com\/blog\/meta-llama-3\/. Accessed 10 June 2024"},{"key":"5_CR5","unstructured":"Meta Llama 3 \u2014 llama.meta.com. https:\/\/llama.meta.com\/llama3\/ Accessed 24 June 2024"},{"key":"5_CR6","unstructured":"Dark commercial patterns. https:\/\/www.oecd-ilibrary.org\/docserver\/44f5e846-en.pdf?expires=1707456299&id=id &accname=guest &checksum=063A07EB53611E9EB1941F5DECEF38C3 (2022)"},{"key":"5_CR7","unstructured":"Dark pattern visual detection dataset. https:\/\/universe.roboflow.com\/syd-help\/dark-pattern-_visual-detection (2023). 07 June 2024"},{"key":"5_CR8","unstructured":"Guidelines 03\/2022 on deceptive design patterns in social media platform interfaces: how to recognise and avoid them - version 2.0. https:\/\/edpb.europa.eu\/system\/files\/2023-02\/edpb_03-2022_guidelines_on_deceptive_design_patterns_in_social_media_platform_interfaces_v2_en_0.pdf (2023). Accessed 10 Nov 2023"},{"issue":"1","key":"5_CR9","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s13042-022-01553-3","volume":"14","author":"M Bayer","year":"2023","unstructured":"Bayer, M., Kaufhold, M.A., Buchhold, B., Keller, M., Dallmeyer, J., Reuter, C.: Data augmentation in natural language processing: a novel text generation approach for long and short text classifiers. Int. J. Mach. Learn. Cybern. 14(1), 135\u2013150 (2023)","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"7","key":"5_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3544558","volume":"55","author":"M Bayer","year":"2022","unstructured":"Bayer, M., Kaufhold, M.A., Reuter, C.: A survey on data augmentation for text classification. ACM Comput. Surv. 55(7), 1\u201339 (2022)","journal-title":"ACM Comput. Surv."},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"B\u00f6sch, C., Erb, B., Kargl, F., Kopp, H., Pfattheicher, S.: Tales from the dark side: Privacy dark strategies and privacy dark patterns. Proce. Priv. Enhancing Technol. (2016)","DOI":"10.1515\/popets-2016-0038"},{"issue":"14","key":"5_CR12","first-page":"105","volume":"7","author":"C Cara","year":"2019","unstructured":"Cara, C., et al.: Dark patterns in the media: a systematic review. Netw. Intell. Stud. 7(14), 105\u2013113 (2019)","journal-title":"Netw. Intell. Stud."},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Aletras, N., Androutsopoulos, I.: Legal-bert: The muppets straight out of law school. arXiv preprint arXiv:2010.02559 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.261"},{"key":"5_CR14","unstructured":"Chang, Y., et\u00a0al.: A survey on evaluation of large language models. ACM Trans. Intell. Syst. Technol. (2023)"},{"issue":"3","key":"5_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3641289","volume":"15","author":"Y Chang","year":"2024","unstructured":"Chang, Y., et al.: A survey on evaluation of large language models. ACM Trans. Intell. Syst. Technol. 15(3), 1\u201345 (2024)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Chen, J., et al.: Unveiling the tricks: automated detection of dark patterns in mobile applications. In: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, pp. 1\u201320 (2023)","DOI":"10.1145\/3586183.3606783"},{"issue":"5","key":"5_CR17","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1111\/1754-9485.13261","volume":"65","author":"P Chlap","year":"2021","unstructured":"Chlap, P., Min, H., Vandenberg, N., Dowling, J., Holloway, L., Haworth, A.: A review of medical image data augmentation techniques for deep learning applications. J. Med. Imaging Radiat. Oncol. 65(5), 545\u2013563 (2021)","journal-title":"J. Med. Imaging Radiat. Oncol."},{"key":"5_CR18","unstructured":"Commission, F.T.: Bringing dark patterns to light (2022)"},{"key":"5_CR19","unstructured":"Dai, H., et al.: Auggpt: Leveraging chatgpt for text data augmentation. arXiv preprint arXiv:2302.13007 (2023)"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Di\u00a0Geronimo, L., Braz, L., Fregnan, E., Palomba, F., Bacchelli, A.: Ui dark patterns and where to find them: a study on mobile applications and user perception. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1\u201314 (2020)","DOI":"10.1145\/3313831.3376600"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Dubiel, M., Sergeeva, A., Leiva, L.A.: Impact of voice fidelity on decision making: A potential dark pattern? arXiv preprint arXiv:2402.07010 (2024)","DOI":"10.1145\/3640543.3645202"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Ekin, S.: Prompt engineering for chatgpt: a quick guide to techniques, tips, and best practices. Authorea Preprints (2023)","DOI":"10.36227\/techrxiv.22683919"},{"issue":"11","key":"5_CR23","doi-asserted-by":"publisher","first-page":"12561","DOI":"10.1007\/s10462-023-10453-z","volume":"56","author":"E Goceri","year":"2023","unstructured":"Goceri, E.: Medical image data augmentation: techniques, comparisons and interpretations. Artif. Intell. Rev. 56(11), 12561\u201312605 (2023)","journal-title":"Artif. Intell. Rev."},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Gray, C.M., Kou, Y., Battles, B., Hoggatt, J., Toombs, A.L.: The dark (patterns) side of UX design. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1\u201314 (2018)","DOI":"10.1145\/3173574.3174108"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Gray, C.M., Santos, C., Bielova, N., Mildner, T.: An ontology of dark patterns knowledge: Foundations, definitions, and a pathway for shared knowledge-building. In: 2024 CHI Conference on Human Factors in Computing Systems (in press)","DOI":"10.1145\/3613904.3642436"},{"key":"5_CR26","unstructured":"Hu, E.J., et al.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)"},{"key":"5_CR27","unstructured":"for Justice, D.G., Commission), C.E., et al.: Behavioural study on unfair commercial practices in the digital environment: dark patterns and manipulative personalisation: final report. Publications Office of the European Union, LU (2022). https:\/\/data.europa.eu\/doi\/10.2838\/859030"},{"issue":"3","key":"5_CR28","doi-asserted-by":"publisher","first-page":"3713","DOI":"10.1007\/s11042-022-13428-4","volume":"82","author":"D Khurana","year":"2023","unstructured":"Khurana, D., Koli, A., Khatter, K., Singh, S.: Natural language processing: state of the art, current trends and challenges. Multimedia Tools Appl. 82(3), 3713\u20133744 (2023)","journal-title":"Multimedia Tools Appl."},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Kocyigit, E., Rossi, A., Lenzini, G.: Towards assessing features of dark patterns in cookie consent processes. In: IFIP International Summer School on Privacy and Identity Management, pp. 165\u2013183. Springer (2022)","DOI":"10.1007\/978-3-031-31971-6_13"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Kocyigit, E., Rossi, A., Lenzini, G.: A systematic approach for a reliable detection of deceptive design patterns through measurable HCI features. In: Proceedings of the 2024 European Symposium on Usable Security, pp. 290\u2013308, New York, NY, USA (2024). Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3688459.3688475","DOI":"10.1145\/3688459.3688475"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Krauss, V., et al.: What makes XR dark? examining emerging dark patterns in augmented and virtual reality through expert co-design. ACM Trans. Comput. Human Interact. (2024)","DOI":"10.1145\/3660340"},{"key":"5_CR32","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.aiopen.2022.03.001","volume":"3","author":"B Li","year":"2022","unstructured":"Li, B., Hou, Y., Che, W.: Data augmentation approaches in natural language processing: a survey. AI Open 3, 71\u201390 (2022)","journal-title":"AI Open"},{"key":"5_CR33","unstructured":"Liang, X., et al.: Task oriented in-domain data augmentation. arXiv preprint arXiv:2406.16694 (2024)"},{"key":"5_CR34","doi-asserted-by":"publisher","first-page":"105864","DOI":"10.1016\/j.clsr.2023.105864","volume":"51","author":"D Liga","year":"2023","unstructured":"Liga, D., Robaldo, L.: Fine-tuning GPT-3 for legal rule classification. Comput. Law Secur. Rev. 51, 105864 (2023)","journal-title":"Comput. Law Secur. Rev."},{"key":"5_CR35","doi-asserted-by":"crossref","unstructured":"Lin, X., et al.: Data-efficient fine-tuning for llm-based recommendation. arXiv preprint arXiv:2401.17197 (2024)","DOI":"10.1145\/3626772.3657807"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Mansur, S.H., Salma, S., Awofisayo, D., Moran, K.: Aidui: toward automated recognition of dark patterns in user interfaces. In: 2023 IEEE\/ACM 45th International Conference on Software Engineering (ICSE), pp. 1958\u20131970. IEEE (2023)","DOI":"10.1109\/ICSE48619.2023.00166"},{"key":"5_CR37","doi-asserted-by":"crossref","unstructured":"Mathur, A., et al.: Dark patterns at scale: Findings from a crawl of 11k shopping websites. Proce. ACM Human-comput. Interact. 3(CSCW), 1\u201332 (2019)","DOI":"10.1145\/3359183"},{"key":"5_CR38","doi-asserted-by":"crossref","unstructured":"Mathur, A., Kshirsagar, M., Mayer, J.: What makes a dark pattern... dark? Design attributes, normative considerations, and measurement methods. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1\u201318 (2021)","DOI":"10.1145\/3411764.3445610"},{"key":"5_CR39","doi-asserted-by":"publisher","first-page":"100258","DOI":"10.1016\/j.array.2022.100258","volume":"16","author":"A Mumuni","year":"2022","unstructured":"Mumuni, A., Mumuni, F.: Data augmentation: a comprehensive survey of modern approaches. Array 16, 100258 (2022)","journal-title":"Array"},{"key":"5_CR40","doi-asserted-by":"crossref","unstructured":"Naheyan, T., Oyibo, K.: The effect of dark patterns and user knowledge on user experience and decision-making. In: International Conference on Persuasive Technology, pp. 190\u2013206. Springer (2024)","DOI":"10.1007\/978-3-031-58226-4_15"},{"issue":"2","key":"5_CR41","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1145\/3400899.3400901","volume":"18","author":"A Narayanan","year":"2020","unstructured":"Narayanan, A., Mathur, A., Chetty, M., Kshirsagar, M.: Dark patterns: past, present, and future: the evolution of tricky user interfaces. Queue 18(2), 67\u201392 (2020)","journal-title":"Queue"},{"key":"5_CR42","doi-asserted-by":"publisher","first-page":"107791","DOI":"10.1016\/j.engappai.2023.107791","volume":"131","author":"H Naveed","year":"2024","unstructured":"Naveed, H., Anwar, S., Hayat, M., Javed, K., Mian, A.: Survey: image mixing and deleting for data augmentation. Eng. Appl. Artif. Intell. 131, 107791 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"5_CR43","doi-asserted-by":"crossref","unstructured":"Potel-Saville, M., Da\u00a0Rocha, M.: From dark patterns to fair patterns? Usable taxonomy to contribute solving the issue with countermeasures. In: Annual Privacy Forum, pp. 145\u2013165. Springer (2023)","DOI":"10.1007\/978-3-031-61089-9_7"},{"issue":"8","key":"5_CR44","doi-asserted-by":"publisher","first-page":"4054","DOI":"10.3390\/app12084054","volume":"12","author":"AM Saghiri","year":"2022","unstructured":"Saghiri, A.M., Vahidipour, S.M., Jabbarpour, M.R., Sookhak, M., Forestiero, A.: A survey of artificial intelligence challenges: analyzing the definitions, relationships, and evolutions. Appl. Sci. 12(8), 4054 (2022)","journal-title":"Appl. Sci."},{"key":"5_CR45","unstructured":"Santos, C., Rossi, A.: The emergence of dark patterns as a legal concept in case law. Internet Policy Rev. (2023). https:\/\/policyreview.info\/articles\/news\/emergence-of-dark-patterns-as-a-legal-concept"},{"issue":"1","key":"5_CR46","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1186\/s40537-021-00492-0","volume":"8","author":"C Shorten","year":"2021","unstructured":"Shorten, C., Khoshgoftaar, T.M., Furht, B.: Text data augmentation for deep learning. J. Big Data 8(1), 101 (2021)","journal-title":"J. Big Data"},{"key":"5_CR47","doi-asserted-by":"crossref","unstructured":"Soe, T.H., Nordberg, O.E., Guribye, F., Slavkovik, M.: Circumvention by design-dark patterns in cookie consent for online news outlets. In: Proceedings of the 11th Nordic Conference On Human-computer Interaction: Shaping Experiences, shaping society, pp. 1\u201312 (2020)","DOI":"10.1145\/3419249.3420132"},{"key":"5_CR48","unstructured":"Touvron, H., et\u00a0al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)"},{"key":"5_CR49","unstructured":"Touvron, H., et\u00a0al.: Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)"},{"issue":"2017","key":"5_CR50","first-page":"1","volume":"11","author":"J Wang","year":"2017","unstructured":"Wang, J., Perez, L., et al.: The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Netw. Vis. Recognit. 11(2017), 1\u20138 (2017)","journal-title":"Convolutional Neural Netw. Vis. Recognit."},{"key":"5_CR51","unstructured":"Wei, J., et\u00a0al.: Emergent abilities of large language models. arXiv preprint arXiv:2206.07682 (2022)"},{"key":"5_CR52","unstructured":"Wolf, T., et\u00a0al.: Huggingface\u2019s transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019)"},{"key":"5_CR53","doi-asserted-by":"crossref","unstructured":"Yada, Y., Feng, J., Matsumoto, T., Fukushima, N., Kido, F., Yamana, H.: Dark patterns in e-commerce: a dataset and its baseline evaluations. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3015\u20133022. IEEE (2022)","DOI":"10.1109\/BigData55660.2022.10020800"},{"key":"5_CR54","unstructured":"Zhao, W.X., et\u00a0al.: A survey of large language models. arXiv preprint arXiv:2303.18223 (2023)"}],"container-title":["Communications in Computer and Information Science","Advances in Explainability, Agents, and Large Language Models"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-89103-8_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T07:29:39Z","timestamp":1745479779000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-89103-8_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031891021","9783031891038"],"references-count":54,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-89103-8_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CALM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Causality, Agents and Large Models","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"calm2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ciad-lab.fr\/prima-causal-ai-workshop\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}