{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T15:00:37Z","timestamp":1765292437891,"version":"3.46.0"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819698486"},{"type":"electronic","value":"9789819698493"}],"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\/978-981-96-9849-3_26","type":"book-chapter","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T08:59:54Z","timestamp":1752829194000},"page":"307-318","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GEETI: Graph Embedding-Enhanced Textual Inversion for Chinese Harmful Meme Detection and Identification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4604-9567","authenticated-orcid":false,"given":"Shichao","family":"Fu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8747-1790","authenticated-orcid":false,"given":"Tongguan","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6638-5009","authenticated-orcid":false,"given":"Ying","family":"Sha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,19]]},"reference":[{"key":"26_CR1","series-title":"LNCS","first-page":"527","volume-title":"NLPCC 2022","author":"Z Li","year":"2022","unstructured":"Li, Z., Lin, H., Yang, L., Xu, B., Zhang, S.: Memeplate: A Chinese multimodal dataset for humor understanding in meme templates. In: NLPCC 2022. LNCS, vol. 13551, pp. 527\u2013538. Springer, Cham (2022)"},{"key":"26_CR2","unstructured":"Kiela, D., et al.: The hateful memes challenge: Detecting hate speech in multimodal memes. In: Advances in Neural Information Processing Systems, vol. 33, pp. 2611\u20132624 (2020)"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Lu, J., et al.: Towards comprehensive detection of Chinese harmful memes. In: Advances in Neural Information Processing Systems 37, pp. 13302\u201313320 (2024)","DOI":"10.52202\/079017-0424"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Pramanick, S., Sharma, S., Dimitrov, D., Akhtar, M.S., Nakov, P., Chakraborty, T.: MOMENTA: a multimodal framework for detecting harmful memes and their targets. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 4439\u20134455. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.379"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Pramanick, S., et al.: Detecting harmful memes and their targets. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 2783\u20132796. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.findings-acl.246"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Zia, H.B., Castro, I., Tyson, G.: Racist or sexist meme? Classifying memes beyond hateful. In: Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021), pp. 215\u2013219. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.woah-1.23"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Wang, T. et al.: McHirc: a multimodal benchmark for Chinese idiom reading comprehension. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39 (24), pp. 25398\u201325406. AAAI Press (2025)","DOI":"10.1609\/aaai.v39i24.34728"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Su, G. et al.: Refine, align, and aggregate: multi-view linguistic features enhancement for aspect sentiment triplet extraction. In: Findings of the Association for Computational Linguistics: ACL 2024, pp. 3212\u20133228. Association for Computational Linguistics (2024)","DOI":"10.18653\/v1\/2024.findings-acl.191"},{"key":"26_CR9","unstructured":"Li, L.H., Yatskar, M., Yin, D., Hsieh, C.J., Chang, K.W.: VisualBERT: A simple and performant baseline for vision and language. arXiv preprint arXiv:1908.03557 (2019)"},{"key":"26_CR10","unstructured":"Lu, J., Batra, D., Parikh, D., Lee, S.: ViLBERT: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Kumar, G.K., Nandakumar, K.: Hate-CLIPper: multimodal hateful meme classification based on cross-modal interaction of CLIP features. In: Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI), pp. 171\u2013183. Association for Computational Linguistics (2022)","DOI":"10.18653\/v1\/2022.nlp4pi-1.20"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Burbi, G., Baldrati, A., Agnolucci, L., Bertini, M., Del Bimbo, A.: Mapping memes to words for multimodal hateful meme classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2832\u20132836 (2023)","DOI":"10.1109\/ICCVW60793.2023.00303"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Mei, J., Chen, J., Lin, W., Byrne, B., Tomalin, M.: Improving hateful meme detection through retrieval-guided contrastive learning. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), pp. 5333\u20135347. Association for Computational Linguistics (2024)","DOI":"10.18653\/v1\/2024.acl-long.291"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Cohen, N., Gal, R., Meirom, E.A., Chechik, G., Atzmon, Y.: \u201cThis is my unicorn, Fluffy\u201d: personalizing frozen vision-language representations. In: Proceedings of the European Conference on Computer Vision (ECCV) (2022), LNCS, vol. 13680, pp. 558\u2013577. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-20044-1_32"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Saito, K., Sohn, K., Zhang, X., Li, C.L., Lee, C.Y., Saenko, K., Pfister, T.: Pic2word: mapping pictures to words for zero-shot composed image retrieval. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19305\u201319314 (2023)","DOI":"10.1109\/CVPR52729.2023.01850"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Baldrati, A., Agnolucci, L., Bertini, M., Del Bimbo, A.: Zero-shot composed image retrieval with textual inversion. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15338\u201315347 (2023)","DOI":"10.1109\/ICCV51070.2023.01407"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Blaier, E., Malkiel, I., Wolf, L.: Caption enriched samples for improving hateful memes detection. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 9350\u20139358. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.738"},{"key":"26_CR18","unstructured":"Sandulescu, V.: Detecting hateful memes using a multimodal deep ensemble. arXiv preprint arXiv:2012.13235 (2020)"},{"key":"26_CR19","unstructured":"Yang, A., et al.: Chinese CLIP: Contrastive vision-language pretraining in Chinese. arXiv preprint arXiv:2211.01335 (2022)"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"26_CR21","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)"},{"key":"26_CR22","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"N Chawla","year":"2002","unstructured":"Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. (JAIR) 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res. (JAIR)"},{"key":"26_CR23","unstructured":"Liu, Y., et al.: RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"26_CR25","unstructured":"Dosovitskiy, A., et al.: An image is worth 16\u00d716 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2021)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9849-3_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T14:52:30Z","timestamp":1765291950000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9849-3_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698486","9789819698493"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9849-3_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"19 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}