{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T09:05:43Z","timestamp":1779267943170,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,5,26]]},"DOI":"10.1145\/3795766.3799760","type":"proceedings-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T07:50:03Z","timestamp":1779263403000},"page":"97-107","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing the Reliability of a Large Multimodal Model for Scoring and Reasoning Commercial Videos"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3649-9785","authenticated-orcid":false,"given":"Xiao","family":"Luo","sequence":"first","affiliation":[{"name":"Southern Methodist University, Dallas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3769-9049","authenticated-orcid":false,"given":"Xiang","family":"Fang","sequence":"additional","affiliation":[{"name":"Oklahoma State University, Stillwater, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4068-864X","authenticated-orcid":false,"given":"Yuechen","family":"Wu","sequence":"additional","affiliation":[{"name":"Oklahoma State University, Stillwater, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6769-5655","authenticated-orcid":false,"given":"Preethika","family":"Chidara","sequence":"additional","affiliation":[{"name":"Oklahoma State University, Stillwater, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3144-7561","authenticated-orcid":false,"given":"Rob","family":"Elliott","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,25]]},"reference":[{"key":"e_1_3_3_2_2_2","unstructured":"[n. d.]. An Overview of Each Platform\u2019s AI Tools for Advertisers \u2014 socialmediatoday.com. https:\/\/www.socialmediatoday.com\/news\/overview-ai-ad-optimization-targeting-creation-tools-social-apps\/748800\/. [Accessed 19-11-2025]."},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3717867.3717881"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Toqa Alaa Ahmad Mongy Assem Bakr Mariam Diab and Walid Gomaa. 2024. Video Summarization Techniques: A Comprehensive Review. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2410.04449 (2024).","DOI":"10.5220\/0012936400003822"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"David Allan and Stephanie\u00a0A Tryce. 2016. Popular music in Super Bowl commercials 2005-2014. International Journal of Sports Marketing and Sponsorship 17 4 (2016) 333\u2013348.","DOI":"10.1108\/IJSMS-11-2016-019"},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3706599.3719991"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00676"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Stuart\u00a0J Barnes. 2024. Smooth talking and fast music: Understanding the importance of voice and music in travel and tourism ads via acoustic analytics. Journal of Travel Research 63 5 (2024) 1070\u20131085.","DOI":"10.1177\/00472875231185882"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"crossref","unstructured":"Deepa Chandrasekaran Raji Srinivasan and Debika Sihi. 2018. Effects of offline ad content on online brand search: Insights from super bowl advertising. Journal of the Academy of Marketing Science 46 3 (2018) 403\u2013430.","DOI":"10.1007\/s11747-017-0551-8"},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.findings-naacl.322"},{"key":"e_1_3_3_2_11_2","unstructured":"Ganqu Cui Lifan Yuan Ning Ding Guanming Yao Bingxiang He Wei Zhu Yuan Ni Guotong Xie Ruobing Xie Yankai Lin et\u00a0al. 2023. Ultrafeedback: Boosting language models with scaled ai feedback. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2310.01377 (2023)."},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Ishaya Gambo Faith-Jane Abegunde Omobola Gambo Roseline\u00a0Oluwaseun Ogundokun Akinbowale\u00a0Natheniel Babatunde and Cheng-Chi Lee. 2025. GRAD-AI: An automated grading tool for code assessment and feedback in programming course. Education and Information Technologies 30 7 (2025) 9859\u20139899.","DOI":"10.1007\/s10639-024-13218-5"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016391"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"crossref","unstructured":"Wagner Junior\u00a0Ladeira Joanna\u00a0Krywalski Santiago Fernando de Oliveira\u00a0Santini and Diego Costa\u00a0Pinto. 2022. Impact of brand familiarity on attitude formation: insights and generalizations from a meta-analysis. Journal of Product & Brand Management 31 8 (2022) 1168\u20131179.","DOI":"10.1108\/JPBM-10-2020-3166"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Rui Kang and Pei-Luen\u00a0Patrick Rau. 2024. IIVRS: an Intelligent Image and Video Rating System to Provide Scenario-Based Content for Different Users. Interacting with Computers 36 6 (2024) 406\u2013415.","DOI":"10.1093\/iwc\/iwae034"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.findings-acl.597"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Scott\u00a0W Kelley and Louis\u00a0W Turley. 2004. The effect of content on perceived affect of Super Bowl commercials. Journal of Sport Management 18 4 (2004) 398\u2013420.","DOI":"10.1123\/jsm.18.4.398"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Samsun Knight Yakov Bart and Minwen Yang. 2023. Generative ai and user-generated content: Evidence from online reviews. Northeastern U. D\u2019Amore-McKim School of Business Research Paper4621982 (2023).","DOI":"10.2139\/ssrn.4621982"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v18i1.31358"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/SNPD61259.2024.10673924"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Ke Lei and Yixuan Liu. 2025. When AI Becomes a Shopping Advisor: A Study on the Impact of Generative AI Review on Consumer Purchase Decision. SAGE Open 15 3 (2025) 21582440251357671.","DOI":"10.1177\/21582440251357671"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Dan Li and Nicholas\u00a0Masafumi Watanabe. 2022. Effects of super bowl advertising on online brand search: Ten years of insights from 2011 to 2020. International Journal of Sports Marketing and Sponsorship 23 4 (2022) 841\u2013854.","DOI":"10.1108\/IJSMS-07-2021-0151"},{"key":"e_1_3_3_2_23_2","unstructured":"Haitao Li Qian Dong Junjie Chen Huixue Su Yujia Zhou Qingyao Ai Ziyi Ye and Yiqun Liu. 2024. Llms-as-judges: a comprehensive survey on llm-based evaluation methods. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2412.05579 (2024)."},{"key":"e_1_3_3_2_24_2","unstructured":"Xiao Liu Xinhao Xiang Zizhong Li Yongheng Wang Zhuoheng Li Zhuosheng Liu Weidi Zhang Weiqi Ye and Jiawei Zhang. 2024. A survey of ai-generated video evaluation. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2410.19884 (2024)."},{"key":"e_1_3_3_2_25_2","unstructured":"Xiao Liu and Jiawei Zhang. 2025. AIGVE-MACS: Unified Multi-Aspect Commenting and Scoring Model for AI-Generated Video Evaluation. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2507.01255 (2025)."},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Yuan Liu Ibrahim\u00a0R Alzahrani Refed\u00a0Adnan Jaleel and Saleh Al\u00a0Sulaie. 2023. An efficient smart data mining framework based cloud internet of things for developing artificial intelligence of marketing information analysis. Information Processing & Management 60 1 (2023) 103121.","DOI":"10.1016\/j.ipm.2022.103121"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.00792"},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"crossref","unstructured":"Marcello\u00a0M Mariani Rodrigo Perez-Vega and Jochen Wirtz. 2022. AI in marketing consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing 39 4 (2022) 755\u2013776.","DOI":"10.1002\/mar.21619"},{"key":"e_1_3_3_2_29_2","doi-asserted-by":"crossref","unstructured":"Lyndon Nixon Konstantinos Apostolidis Evlampios Apostolidis Damianos Galanopoulos Vasileios Mezaris Basil Philipp and Rasa Bocyte. 2024. AI and data-driven media analysis of TV content for optimised digital content marketing. Multimedia Systems 30 1 (2024) 25.","DOI":"10.1007\/s00530-023-01195-7"},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3717867.3717924"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"crossref","unstructured":"Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1908.10084 (2019).","DOI":"10.18653\/v1\/D19-1410"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01743"},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"crossref","unstructured":"Larisa Sharakhina Irina Ilyina Dmitrii Kaplun Tatiana Teor and Valeria Kulibanova. 2024. AI technologies in the analysis of visual advertising messages: survey and application. Journal of Marketing Analytics 12 4 (2024) 1066\u20131089.","DOI":"10.1057\/s41270-023-00255-1"},{"key":"e_1_3_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.02436"},{"key":"e_1_3_3_2_35_2","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Two-stream convolutional networks for action recognition in videos. Advances in neural information processing systems 27 (2014)."},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"crossref","unstructured":"Fahim\u00a0K Sufi and Musleh AlSulami. 2022. AI-feature: A software for automatic extraction of features attributes from social medial texts using AI services. International Transaction Journal of Engineering Management & Applied Sciences & Technologies 13 4 (2022) 1\u20138.","DOI":"10.1016\/j.simpa.2022.100319"},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"crossref","unstructured":"Gerard\u00a0J Tellis Deborah\u00a0J MacInnis Seshadri Tirunillai and Yanwei Zhang. 2019. What drives virality (sharing) of online digital content? The critical role of information emotion and brand prominence. Journal of marketing 83 4 (2019) 1\u201320.","DOI":"10.1177\/0022242919841034"},{"key":"e_1_3_3_2_38_2","first-page":"97","volume-title":"International Conference on Pattern Recognition","author":"Thareja Rushil","year":"2024","unstructured":"Rushil Thareja, Deep Dwivedi, Ritik Garg, Shiva Baghel, Jainendra Shukla, and Mukesh Mohania. 2024. Video Analysis Engine for Predicting Effectiveness. In International Conference on Pattern Recognition. Springer, 97\u2013112."},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.510"},{"key":"e_1_3_3_2_40_2","unstructured":"Ruben Villegas Mohammad Babaeizadeh Pieter-Jan Kindermans Hernan Moraldo Han Zhang Mohammad\u00a0Taghi Saffar Santiago Castro Julius Kunze and Dumitru Erhan. 2022. Phenaki: Variable length video generation from open domain textual description. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2210.02399 (2022)."},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"crossref","unstructured":"Xiaoming Xi. 2010. Automated scoring and feedback systems: Where are we and where are we heading?291\u2013300\u00a0pages.","DOI":"10.1177\/0265532210364643"},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"crossref","unstructured":"Rama Yelkur Chuck Tomkovick Ashley Hofer and Daniel Rozumalski. 2013. Super Bowl ad likeability: Enduring and emerging predictors. Journal of Marketing Communications 19 1 (2013) 58\u201380.","DOI":"10.1080\/13527266.2011.581302"},{"key":"e_1_3_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51701.2025.02025"},{"key":"e_1_3_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657929"},{"key":"e_1_3_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.432"},{"key":"e_1_3_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.01760"}],"event":{"name":"WebSci '26: 18th ACM Web Science Conference 2026","location":"Braunschweig Germany","acronym":"WebSci '26","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the 18th ACM Web Science Conference 2026"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3795766.3799760","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T08:07:46Z","timestamp":1779264466000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3795766.3799760"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,25]]},"references-count":45,"alternative-id":["10.1145\/3795766.3799760","10.1145\/3795766"],"URL":"https:\/\/doi.org\/10.1145\/3795766.3799760","relation":{},"subject":[],"published":{"date-parts":[[2026,5,25]]},"assertion":[{"value":"2026-05-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}