{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:26:54Z","timestamp":1742912814851,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031263026"},{"type":"electronic","value":"9783031263033"}],"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\/978-3-031-26303-3_1","type":"book-chapter","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T16:08:38Z","timestamp":1676045318000},"page":"3-14","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Incorporating Neighborhood Information and\u00a0Sentence Embedding Similarity into\u00a0a\u00a0Repost Prediction Model in\u00a0Social Media Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6124-3047","authenticated-orcid":false,"given":"Zhecheng","family":"Qiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6403-6649","authenticated-orcid":false,"given":"Eduardo L.","family":"Pasiliao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2691-4575","authenticated-orcid":false,"given":"Alexander","family":"Semenov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4597-3426","authenticated-orcid":false,"given":"Qipeng P.","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,11]]},"reference":[{"key":"1_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1007\/978-3-642-10631-6_98","volume-title":"Algorithms and Computation","author":"E Anshelevich","year":"2009","unstructured":"Anshelevich, E., Chakrabarty, D., Hate, A., Swamy, C.: Approximation algorithms for the firefighter problem: cuts over time and submodularity. In: Dong, Y., Du, D.-Z., Ibarra, O. (eds.) ISAAC 2009. LNCS, vol. 5878, pp. 974\u2013983. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-10631-6_98"},{"key":"1_CR2","doi-asserted-by":"publisher","unstructured":"Bourigault, S., Lamprier, S., Gallinari, P.: Representation learning for information diffusion through social networks: an embedded cascade model. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, WSDM 2016, pp. 573\u2013582. ACM, New York (2016). https:\/\/doi.org\/10.1145\/2835776.2835817, http:\/\/doi.acm.org\/10.1145\/2835776.2835817","DOI":"10.1145\/2835776.2835817"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Budak, C., Agrawal, D., El Abbadi, A.: Limiting the spread of misinformation in social networks. In: Proceedings of the 20th International Conference on World Wide Web, pp. 665\u2013674. ACM (2011)","DOI":"10.1145\/1963405.1963499"},{"key":"1_CR4","unstructured":"Chen, G.H., Nikolov, S., Shah, D.: A latent source model for nonparametric time series classification. In: Advances in Neural Information Processing Systems, pp. 1088\u20131096 (2013)"},{"key":"1_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1007\/978-3-030-34980-6_17","volume-title":"Computational Data and Social Networks","author":"M Chen","year":"2019","unstructured":"Chen, M., Zheng, Q.P., Boginski, V., Pasiliao, E.L.: Reinforcement learning in information cascades based on dynamic user behavior. In: Tagarelli, A., Tong, H. (eds.) CSoNet 2019. LNCS, vol. 11917, pp. 148\u2013154. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-34980-6_17"},{"issue":"1","key":"1_CR6","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MIS.2005.16","volume":"20","author":"P Domingos","year":"2005","unstructured":"Domingos, P.: Mining social networks for viral marketing. IEEE Intell. Syst. 20(1), 80\u201382 (2005)","journal-title":"IEEE Intell. Syst."},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Fei, H., Jiang, R., Yang, Y., Luo, B., Huan, J.: Content based social behavior prediction: a multi-task learning approach. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 995\u20131000. ACM (2011)","DOI":"10.1145\/2063576.2063719"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the third ACM International Conference on Web Search and Data Mining, pp. 241\u2013250. ACM (2010)","DOI":"10.1145\/1718487.1718518"},{"issue":"6","key":"1_CR9","doi-asserted-by":"publisher","first-page":"1420","DOI":"10.1086\/226707","volume":"83","author":"M Granovetter","year":"1978","unstructured":"Granovetter, M.: Threshold models of collective behavior. Am. J. Sociol. 83(6), 1420\u20131443 (1978)","journal-title":"Am. J. Sociol."},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Guille, A., Hacid, H.: A predictive model for the temporal dynamics of information diffusion in online social networks. In: Proceedings of the 21st International Conference on World Wide Web, pp. 1145\u20131152. ACM (2012)","DOI":"10.1145\/2187980.2188254"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Jiang, B., et al.: Retweeting behavior prediction based on one-class collaborative filtering in social networks. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 977\u2013980. ACM (2016)","DOI":"10.1145\/2911451.2914713"},{"key":"1_CR12","doi-asserted-by":"publisher","unstructured":"Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 137\u2013146. ACM, New York (2003). https:\/\/doi.org\/10.1145\/956750.956769, http:\/\/doi.acm.org\/10.1145\/956750.956769","DOI":"10.1145\/956750.956769"},{"key":"1_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/978-3-642-36973-5_7","volume-title":"Advances in Information Retrieval","author":"C Lagnier","year":"2013","unstructured":"Lagnier, C., Denoyer, L., Gaussier, E., Gallinari, P.: Predicting information diffusion in social networks using content and user\u2019s profiles. In: Serdyukov, P., et al. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 74\u201385. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-36973-5_7"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Peng, H.K., Zhu, J., Piao, D., Yan, R., Zhang, Y.: Retweet modeling using conditional random fields. In: 2011 11th IEEE International Conference on Data Mining Workshops, pp. 336\u2013343. IEEE (2011)","DOI":"10.1109\/ICDMW.2011.146"},{"issue":"1","key":"1_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41109-019-0215-3","volume":"4","author":"Z Qiang","year":"2019","unstructured":"Qiang, Z., Pasiliao, E.L., Zheng, Q.P.: Model-based learning of information diffusion in social media networks. Appl. Netw. Sci. 4(1), 1\u201316 (2019). https:\/\/doi.org\/10.1007\/s41109-019-0215-3","journal-title":"Appl. Netw. Sci."},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Making monolingual sentence embeddings multilingual using knowledge distillation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2020). https:\/\/arxiv.org\/abs\/2004.09813","DOI":"10.18653\/v1\/2020.emnlp-main.365"},{"key":"1_CR17","unstructured":"Rodriguez, M.G., Balduzzi, D., Sch\u00f6lkopf, B.: Uncovering the temporal dynamics of diffusion networks. arXiv preprint arXiv:1105.0697 (2011)"},{"key":"1_CR18","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1007\/978-3-642-05224-8_25","volume-title":"Advances in Machine Learning","author":"K Saito","year":"2009","unstructured":"Saito, K., Kimura, M., Ohara, K., Motoda, H.: Learning continuous-time information diffusion model for social behavioral data analysis. In: Zhou, Z.-H., Washio, T. (eds.) ACML 2009. LNCS (LNAI), vol. 5828, pp. 322\u2013337. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-05224-8_25"},{"key":"1_CR19","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/978-3-540-85567-5_9","volume-title":"Knowledge-Based Intelligent Information and Engineering Systems","author":"K Saito","year":"2008","unstructured":"Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008. LNCS (LNAI), vol. 5179, pp. 67\u201375. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-85567-5_9"},{"key":"1_CR20","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/978-3-642-21916-0_18","volume-title":"Foundations of Intelligent Systems","author":"K Saito","year":"2011","unstructured":"Saito, K., Ohara, K., Yamagishi, Y., Kimura, M., Motoda, H.: Learning diffusion probability based on node attributes in social networks. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Ra\u015b, Z.W. (eds.) ISMIS 2011. LNCS (LNAI), vol. 6804, pp. 153\u2013162. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21916-0_18"},{"key":"1_CR21","doi-asserted-by":"publisher","unstructured":"Shah, D., Zaman, T.: Detecting sources of computer viruses in networks: theory and experiment. SIGMETRICS Perform. Eval. Rev. 38(1), 203\u2013214 (2010). https:\/\/doi.org\/10.1145\/1811099.1811063, http:\/\/doi.acm.org\/10.1145\/1811099.1811063","DOI":"10.1145\/1811099.1811063"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. In: 2010 IEEE Second International Conference on Social Computing, pp. 177\u2013184. IEEE (2010)","DOI":"10.1109\/SocialCom.2010.33"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Tsur, O., Rappoport, A.: What\u2019s in a hashtag?: content based prediction of the spread of ideas in microblogging communities. In: Proceedings of the fifth ACM International Conference on Web Search and Data Mining, pp. 643\u2013652. ACM (2012)","DOI":"10.1145\/2124295.2124320"},{"key":"1_CR24","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.knosys.2017.07.003","volume":"133","author":"D Varshney","year":"2017","unstructured":"Varshney, D., Kumar, S., Gupta, V.: Predicting information diffusion probabilities in social networks: a Bayesian networks based approach. Knowl.-Based Syst. 133, 66\u201376 (2017)","journal-title":"Knowl.-Based Syst."},{"key":"1_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/978-3-030-34980-6_37","volume-title":"Computational Data and Social Networks","author":"G Yun","year":"2019","unstructured":"Yun, G., Zheng, Q.P., Boginski, V., Pasiliao, E.L.: Information network cascading and network re-construction with bounded rational user behaviors. In: Tagarelli, A., Tong, H. (eds.) CSoNet 2019. LNCS, vol. 11917, pp. 351\u2013362. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-34980-6_37"},{"key":"1_CR26","doi-asserted-by":"publisher","unstructured":"Zhang, J., Tang, J., Li, J., Liu, Y., Xing, C.: Who influenced you? Predicting retweet via social influence locality. ACM Trans. Knowl. Discov. Data 9(3), 25:1\u201325:26 (2015). https:\/\/doi.org\/10.1145\/2700398, http:\/\/doi.acm.org\/10.1145\/2700398","DOI":"10.1145\/2700398"},{"key":"1_CR27","unstructured":"Zhang, M., Chen, Y.: Link prediction based on graph neural networks. arXiv preprint arXiv:1802.09691 (2018)"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Zhu, J., Xiong, F., Piao, D., Liu, Y., Zhang, Y.: Statistically modeling the effectiveness of disaster information in social media. In: 2011 IEEE Global Humanitarian Technology Conference (GHTC), pp. 431\u2013436. IEEE (2011)","DOI":"10.1109\/GHTC.2011.48"}],"container-title":["Lecture Notes in Computer Science","Computational Data and Social Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26303-3_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T13:02:43Z","timestamp":1690981363000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26303-3_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031263026","9783031263033"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26303-3_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"11 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSoNet","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Data and Social Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"csonet2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/csonet-conf.github.io\/csonet22\/index.html","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":"47","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":"17","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":"7","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":"36% - 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":"2","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":"5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}