{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:33:09Z","timestamp":1763202789791,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031440663"},{"type":"electronic","value":"9783031440670"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44067-0_17","type":"book-chapter","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T06:02:33Z","timestamp":1697781753000},"page":"321-337","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["What Will Make Misinformation Spread: An XAI Perspective"],"prefix":"10.1007","author":[{"given":"Hongbo","family":"Bo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiwen","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zinuo","family":"You","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryan","family":"McConville","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiru","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"issue":"2","key":"17_CR1","first-page":"155","volume":"11","author":"G Amati","year":"2016","unstructured":"Amati, G., Angelini, S., Capri, F., Gambosi, G., Rossi, G., Vocca, P.: Twitter temporal evolution analysis: comparing event and topic driven retweet graphs. IADIS Int. J. Comput. Sci. Inf. Syst. 11(2), 155\u2013162 (2016)","journal-title":"IADIS Int. J. Comput. Sci. Inf. Syst."},{"key":"17_CR2","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82\u2013115 (2020)","journal-title":"Inf. Fusion"},{"issue":"2","key":"17_CR3","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1509\/jmr.10.0353","volume":"49","author":"J Berger","year":"2012","unstructured":"Berger, J., Milkman, K.L.: What makes online content viral? J. Mark. Res. 49(2), 192\u2013205 (2012)","journal-title":"J. Mark. Res."},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Bian, T., et al.: Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 549\u2013556 (2020)","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Bo, H., McConville, R., Hong, J., Liu, W.: Social network influence ranking via embedding network interactions for user recommendation. In: Companion Proceedings of the Web Conference 2020, pp. 379\u2013384 (2020)","DOI":"10.1145\/3366424.3383299"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Bo, H., McConville, R., Hong, J., Liu, W.: Social influence prediction with train and test time augmentation for graph neural networks. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9533437"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Bo, H., McConville, R., Hong, J., Liu, W.: Ego-graph replay based continual learning for misinformation engagement prediction. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 01\u201308. IEEE (2022)","DOI":"10.1109\/IJCNN55064.2022.9892557"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Cao, Q., Shen, H., Gao, J., Wei, B., Cheng, X.: Popularity prediction on social platforms with coupled graph neural networks. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 70\u201378 (2020)","DOI":"10.1145\/3336191.3371834"},{"key":"17_CR9","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1007\/978-3-030-04503-6_4","volume-title":"Trends and Applications in Knowledge Discovery and Data Mining","author":"T Chen","year":"2018","unstructured":"Chen, T., Li, X., Yin, H., Zhang, J.: Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. In: Ganji, M., Rashidi, L., Fung, B.C.M., Wang, C. (eds.) PAKDD 2018. LNCS (LNAI), vol. 11154, pp. 40\u201352. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-04503-6_4"},{"issue":"3","key":"17_CR10","doi-asserted-by":"publisher","first-page":"2584","DOI":"10.1002\/int.22786","volume":"37","author":"X Chen","year":"2022","unstructured":"Chen, X., Zhang, F., Zhou, F., Bonsangue, M.: Multi-scale graph capsule with influence attention for information cascades prediction. Int. J. Intell. Syst. 37(3), 2584\u20132611 (2022)","journal-title":"Int. J. Intell. Syst."},{"key":"17_CR11","doi-asserted-by":"publisher","unstructured":"Chen, X., Zhou, F., Zhang, K., Trajcevski, G., Zhong, T., Zhang, F.: Information diffusion prediction via recurrent cascades convolution. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 770\u2013781 (2019). https:\/\/doi.org\/10.1109\/ICDE.2019.00074","DOI":"10.1109\/ICDE.2019.00074"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Do\u0161ilovi\u0107, F.K., Br\u010di\u0107, M., Hlupi\u0107, N.: Explainable artificial intelligence: a survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210\u20130215. IEEE (2018)","DOI":"10.23919\/MIPRO.2018.8400040"},{"key":"17_CR13","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"3","key":"17_CR14","first-page":"1","volume":"14","author":"WL Hamilton","year":"2020","unstructured":"Hamilton, W.L.: Graph representation learning. Synth. Lect. Artif. Intell. Mach. Learn. 14(3), 1\u2013159 (2020)","journal-title":"Synth. Lect. Artif. Intell. Mach. Learn."},{"issue":"8","key":"17_CR15","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"17_CR16","doi-asserted-by":"crossref","first-page":"6968","DOI":"10.1109\/TKDE.2022.3187455","volume":"35","author":"Q Huang","year":"2022","unstructured":"Huang, Q., Yamada, M., Tian, Y., Singh, D., Chang, Y.: Graphlime: local interpretable model explanations for graph neural networks. IEEE Trans. Knowl. Data Eng. 35, 6968\u20136972 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"17_CR17","doi-asserted-by":"publisher","first-page":"e3767","DOI":"10.1002\/ett.3767","volume":"31","author":"S Kumar","year":"2020","unstructured":"Kumar, S., Asthana, R., Upadhyay, S., Upreti, N., Akbar, M.: Fake news detection using deep learning models: a novel approach. Trans. Emerg. Telecommun. Technol. 31(2), e3767 (2020)","journal-title":"Trans. Emerg. Telecommun. Technol."},{"issue":"1","key":"17_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/0022243719881113","volume":"57","author":"Y Li","year":"2020","unstructured":"Li, Y., Xie, Y.: Is a picture worth a thousand words? an empirical study of image content and social media engagement. J. Mark. Res. 57(1), 1\u201319 (2020)","journal-title":"J. Mark. Res."},{"key":"17_CR19","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Ma, H., McAreavey, K., McConville, R., Liu, W.: Explainable AI for non-experts: energy tariff forecasting. In: 2022 27th International Conference on Automation and Computing (ICAC), pp. 1\u20136. IEEE (2022)","DOI":"10.1109\/ICAC55051.2022.9911105"},{"key":"17_CR21","unstructured":"Monti, F., Frasca, F., Eynard, D., Mannion, D., Bronstein, M.M.: Fake news detection on social media using geometric deep learning. arXiv preprint arXiv:1902.06673 (2019)"},{"issue":"1","key":"17_CR22","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/j.physa.2006.07.017","volume":"374","author":"M Nekovee","year":"2007","unstructured":"Nekovee, M., Moreno, Y., Bianconi, G., Marsili, M.: Theory of rumour spreading in complex social networks. Phys. A 374(1), 457\u2013470 (2007)","journal-title":"Phys. A"},{"key":"17_CR23","doi-asserted-by":"crossref","unstructured":"Nielsen, D.S., McConville, R.: Mumin: a large-scale multilingual multimodal fact-checked misinformation social network dataset. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3141\u20133153 (2022)","DOI":"10.1145\/3477495.3531744"},{"issue":"5","key":"17_CR24","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.tics.2021.02.007","volume":"25","author":"G Pennycook","year":"2021","unstructured":"Pennycook, G., Rand, D.G.: The psychology of fake news. Trends Cogn. Sci. 25(5), 388\u2013402 (2021)","journal-title":"Trends Cogn. Sci."},{"key":"17_CR25","unstructured":"Perotti, A., Bajardi, P., Bonchi, F., Panisson, A.: Graphshap: motif-based explanations for black-box graph classifiers. arXiv preprint arXiv:2202.08815 (2022)"},{"key":"17_CR26","doi-asserted-by":"crossref","unstructured":"Pope, P.E., Kolouri, S., Rostami, M., Martin, C.E., Hoffmann, H.: Explainability methods for graph convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10772\u201310781 (2019)","DOI":"10.1109\/CVPR.2019.01103"},{"key":"17_CR27","doi-asserted-by":"crossref","unstructured":"Qiu, J., Tang, J., Ma, H., Dong, Y., Wang, K., Tang, J.: Deepinf: social influence prediction with deep learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2110\u20132119 (2018)","DOI":"10.1145\/3219819.3220077"},{"key":"17_CR28","unstructured":"Schlichtkrull, M.S., De Cao, N., Titov, I.: Interpreting graph neural networks for NLP with differentiable edge masking. arXiv preprint arXiv:2010.00577 (2020)"},{"key":"17_CR29","doi-asserted-by":"crossref","unstructured":"Shi, Y., McAreavey, K., Liu, W.: Evaluating contrastive explanations for AI planning with non-experts: a smart home battery scenario. In: 2022 27th International Conference on Automation and Computing (ICAC), pp. 1\u20136. IEEE (2022)","DOI":"10.1109\/ICAC55051.2022.9911125"},{"key":"17_CR30","doi-asserted-by":"crossref","unstructured":"Shi, Z., Cartlidge, J.: State dependent parallel neural Hawkes process for limit order book event stream prediction and simulation. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1607\u20131615 (2022)","DOI":"10.1145\/3534678.3539462"},{"key":"17_CR31","doi-asserted-by":"crossref","unstructured":"Shu, K., Cui, L., Wang, S., Lee, D., Liu, H.: defend: explainable fake news detection. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 395\u2013405 (2019)","DOI":"10.1145\/3292500.3330935"},{"key":"17_CR32","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: International Conference on Machine Learning, pp. 3319\u20133328. PMLR (2017)"},{"issue":"6380","key":"17_CR33","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1126\/science.aap9559","volume":"359","author":"S Vosoughi","year":"2018","unstructured":"Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146\u20131151 (2018)","journal-title":"Science"},{"key":"17_CR34","first-page":"12225","volume":"33","author":"M Vu","year":"2020","unstructured":"Vu, M., Thai, M.T.: PGM-explainer: probabilistic graphical model explanations for graph neural networks. Adv. Neural. Inf. Process. Syst. 33, 12225\u201312235 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"17_CR35","unstructured":"Wang, M., et al.: Deep graph library: a graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315 (2019)"},{"key":"17_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-023-01748-3","volume":"131","author":"X Yang","year":"2023","unstructured":"Yang, X., Burghardt, T., Mirmehdi, M.: Dynamic curriculum learning for great ape detection in the wild. Int. J. Comput. Vis. 131, 1\u201319 (2023)","journal-title":"Int. J. Comput. Vis."},{"key":"17_CR37","unstructured":"Ying, Z., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: GNNExplainer: generating explanations for graph neural networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"issue":"5","key":"17_CR38","first-page":"5782","volume":"45","author":"H Yuan","year":"2022","unstructured":"Yuan, H., Yu, H., Gui, S., Ji, S.: Explainability in graph neural networks: a taxonomic survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(5), 5782\u20135799 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR39","doi-asserted-by":"crossref","unstructured":"Zuo, W., Raman, A., Mondrag\u00f3n, R.J., Tyson, G.: Set in stone: analysis of an immutable web3 social media platform. In: Proceedings of the ACM Web Conference 2023, pp. 1865\u20131874 (2023)","DOI":"10.1145\/3543507.3583510"}],"container-title":["Communications in Computer and Information Science","Explainable Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44067-0_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T06:02:08Z","timestamp":1730354528000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44067-0_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031440663","9783031440670"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44067-0_17","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"21 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"xAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"World Conference on Explainable Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 July 2023","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":"xai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/xaiworldconference.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"220","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"94","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}