{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T14:22:43Z","timestamp":1780755763978,"version":"3.54.1"},"reference-count":21,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2025,3,1]]},"DOI":"10.1587\/transfun.2024smp0004","type":"journal-article","created":{"date-parts":[[2024,8,21]],"date-time":"2024-08-21T22:30:27Z","timestamp":1724279427000},"page":"295-303","source":"Crossref","is-referenced-by-count":2,"title":["Embedding Learning with Relational Heterogeneous Information in Social Network Posts to Detect Malicious Behavior"],"prefix":"10.1587","volume":"E108.A","author":[{"given":"Ryo","family":"YOSHIDA","sequence":"first","affiliation":[{"name":"Graduate School of Science and Engineering, Kansai University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soh","family":"YOSHIDA","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Engineering, Kansai University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mitsuji","family":"MUNEYASU","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Engineering, Kansai University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] P. Fortuna and S. Nunes, \u201cA survey on automatic detection of hate speech in text,\u201d ACM Computing Surveys, vol.51, no.4, pp.1-30, 2018. 10.1145\/3232676","DOI":"10.1145\/3232676"},{"key":"2","unstructured":"[2] J. Devlin, M.W. Chang, K. Lee, and K. Toutanova, \u201cBERT: Pre-training of deep bidirectional transformers for language understanding,\u201d Proc. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.4171-4186, 2019."},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] J. Wu, C. Zhang, Z. Liu, E. Zhang, S. Wilson, and C. Zhang, \u201cGraphBERT: Bridging graph and text for malicious behavior detection on social media,\u201d Proc. IEEE International Conference on Data Mining, pp.548-557, 2022. 10.1109\/icdm54844.2022.00065","DOI":"10.1109\/ICDM54844.2022.00065"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] Z. Hu, Y. Dong, K. Wang, and Y. Sun, \u201cHeterogeneous graph transformer,\u201d Proc. International World Wide Web Conference, pp.2704-2710, 2020. 10.1145\/3366423.3380027","DOI":"10.1145\/3366423.3380027"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] S. MacAvaney, H.R. Yao, E. Yang, K. Russell, N. Goharian, and O. Frieder, \u201cHate speech detection: Challenges and solutions,\u201d PLOS ONE, vol.14, no.8, pp.1-16, 2019. 10.1371\/journal.pone.0221152","DOI":"10.1371\/journal.pone.0221152"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] R. Gomez, J. Gibert, L. G\u00f3mez, and D. Karatzas, \u201cExploring hate speech detection in multimodal publications,\u201d Proc. IEEE Winter Conference on Applications of Computer Vision, pp.1459-1467, 2019. 10.1109\/wacv45572.2020.9093414","DOI":"10.1109\/WACV45572.2020.9093414"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] M. Mozafari, R. Farahbakhsh, and N. Crespi, \u201cA bert-based transfer learning approach for hate speech detection in online social media,\u201d Proc. International Conference on Complex Networks and Their Applications, pp.928-940, 2020. 10.1007\/978-3-030-36687-2_77","DOI":"10.1007\/978-3-030-36687-2_77"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] M. Mozafari, R. Farahbakhsh, and N. Crespi, \u201cHate speech detection and racial bias mitigation in social media based on BERT model,\u201d PLOS ONE, vol.15, no.8, pp.1-26, 2020. 10.1371\/journal.pone.0237861","DOI":"10.1371\/journal.pone.0237861"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] G. Rajput, N.S. Punn, S.K. Sonbhadra, and S. Agarwal, \u201cHate speech detection using static BERT embeddings,\u201d Proc. International Conference on Big Data Analytics, pp.67-77, 2021. 10.1007\/978-3-030-93620-4_6","DOI":"10.1007\/978-3-030-93620-4_6"},{"key":"10","unstructured":"[10] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, \u0141. Kaiser, and I. Polosukhin, \u201cAttention is all you need,\u201d Advances in Neural Information Processing Systems, pp.6000-6010, 2017."},{"key":"11","unstructured":"[11] T.N. Kipf and M. Welling, \u201cSemi-supervised classification with graph convolutional networks,\u201d International Conference on Learning Representations, 2017."},{"key":"12","unstructured":"[12] W. Hamilton, Z. Ying, and J. Leskovec, \u201cInductive representation learning on large graphs,\u201d Advances in Neural Information Processing Systems, 2017."},{"key":"13","unstructured":"[13] P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. Li\u00f3, and Y. Bengio, \u201cGraph attention networks,\u201d International Conference on Learning Representations, 2017."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] X. Wang, H. Ji, C. Shi, B. Wang, Y. Ye, P. Cui, and P.S. Yu, \u201cHeterogeneous graph attention network,\u201d Proc. International World Wide Web Conference, pp.2022-2032, 2019. 10.1145\/3308558.3313562","DOI":"10.1145\/3308558.3313562"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] O. de Gibert, N. Perez, A. Garc\u0131\u0301a-Pablos, and M. Cuadros, \u201cHate speech dataset from a white supremacy forum,\u201d Proc. Workshop on Abusive Language Online, pp.11-20, 2018. 10.18653\/v1\/w18-5102","DOI":"10.18653\/v1\/W18-5102"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] A. Lees, V.Q. Tran, Y. Tay, J. Sorensen, J. Gupta, D. Metzler, and L. Vasserman, \u201cA new generation of perspective api: Efficient multilingual character-level transformers,\u201d Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, p.3197-3207, 2022. 10.1145\/3534678.3539147","DOI":"10.1145\/3534678.3539147"},{"key":"17","unstructured":"[17] D. Arpit, S.K. Jastrzebski, N. Ballas, D. Krueger, E. Bengio, M.S. Kanwal, T. Maharaj, A. Fischer, A.C. Courville, Y. Bengio, and S. Lacoste-Julien, \u201cA closer look at memorization in deep networks,\u201d Int. Conf. Mach. Learn., pp.233-242, 2017."},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] H. Song, M. Kim, D. Park, Y. Shin, and J.G. Lee, \u201cLearning from noisy labels with deep neural networks: A survey,\u201d IEEE Trans. Neural Netw. Learn. Syst., vol.34, no.11, pp.8135-8153, 2023. 10.1109\/tnnls.2022.3152527","DOI":"10.1109\/TNNLS.2022.3152527"},{"key":"19","unstructured":"[19] D.M. Blei, A.Y. Ng, and M.I. Jordan, \u201cLatent dirichlet allocation,\u201d Journal of Machine Learning Research, vol.3, pp.993-1022, 2003."},{"key":"20","unstructured":"[20] Tohoku NLP Group, \u201cBERT Japanese pretrained model,\u201d https:\/\/github.com\/cl-tohoku\/bert-japanese"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] T.Y. Lin, P. Goyal, R. Girshick, K. He, and P. Doll\u00e1r, \u201cFocal loss for dense object detection,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.42, no.2, pp.318-327, 2020. 10.1109\/tpami.2018.2858826","DOI":"10.1109\/TPAMI.2018.2858826"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E108.A\/3\/E108.A_2024SMP0004\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T03:30:40Z","timestamp":1740799840000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E108.A\/3\/E108.A_2024SMP0004\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,1]]},"references-count":21,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2024smp0004","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"value":"0916-8508","type":"print"},{"value":"1745-1337","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,1]]},"article-number":"2024SMP0004"}}